From b2878ed5d6d129f2ab571bac71ad3a1c301a3a9d Mon Sep 17 00:00:00 2001 From: Guillermo Marcus Date: Wed, 7 Dec 2022 13:48:03 +0100 Subject: [PATCH] Release v0.12.0 Signed-off-by: The Sionna Team --- .gitlab-ci.yml | 110 -- README.md | 6 +- doc/source/api/channel.wireless.rst | 6 +- doc/source/api/fec.conv.rst | 22 +- doc/source/api/fec.ldpc.rst | 15 +- doc/source/api/fec.linear.rst | 80 + doc/source/api/fec.rst | 1 + doc/source/api/fec.turbo.rst | 5 +- doc/source/api/fec.utils.rst | 18 +- doc/source/api/mapping.rst | 20 +- doc/source/api/mimo.rst | 61 +- doc/source/api/ofdm.rst | 96 +- .../figures/drop_uts_in_sector_topology.png | Bin 72163 -> 84933 bytes .../drop_uts_in_sector_topology_inter.png | Bin 71134 -> 80892 bytes doc/source/index.rst | 4 +- doc/source/installation.rst | 6 +- doc/source/made_with_sionna.rst | 24 + doc/source/tutorials.rst | 1 + ...G_Channel_Coding_Polar_vs_LDPC_Codes.ipynb | 396 +++- examples/OFDM_MIMO_Detection.ipynb | 1557 +++++++++++++++ requirements.txt | 4 +- setup.cfg | 4 +- sionna/__init__.py | 2 +- sionna/channel/tr38901/models/TDL-A.json | 1 + sionna/channel/tr38901/models/TDL-A30.json | 31 + sionna/channel/tr38901/models/TDL-B.json | 1 + sionna/channel/tr38901/models/TDL-B100.json | 31 + sionna/channel/tr38901/models/TDL-C.json | 1 + sionna/channel/tr38901/models/TDL-C300.json | 31 + sionna/channel/tr38901/models/TDL-D.json | 1 + sionna/channel/tr38901/models/TDL-E.json | 1 + sionna/channel/tr38901/tdl.py | 171 +- sionna/channel/utils.py | 6 +- sionna/fec/__init__.py | 5 + sionna/fec/conv/decoding.py | 523 +++-- sionna/fec/conv/encoding.py | 110 +- sionna/fec/conv/utils.py | 30 +- sionna/fec/crc.py | 16 + sionna/fec/ldpc/decoding.py | 18 +- sionna/fec/ldpc/encoding.py | 150 +- sionna/fec/linear/__init__.py | 10 + sionna/fec/linear/decoding.py | 473 +++++ sionna/fec/linear/encoding.py | 272 +++ sionna/fec/polar/__init__.py | 3 - sionna/fec/polar/decoding.py | 4 +- sionna/fec/polar/encoding.py | 20 + sionna/fec/turbo/decoding.py | 82 +- sionna/fec/turbo/encoding.py | 104 +- sionna/fec/turbo/utils.py | 18 +- sionna/fec/utils.py | 178 +- sionna/mapping.py | 521 +++-- sionna/mimo/__init__.py | 4 +- sionna/mimo/detection.py | 1507 ++++++++++++-- sionna/mimo/equalization.py | 4 +- sionna/mimo/stream_management.py | 2 +- sionna/mimo/utils.py | 242 ++- sionna/ofdm/__init__.py | 6 +- sionna/ofdm/channel_estimation.py | 1757 ++++++++++++++++- sionna/ofdm/detection.py | 1213 ++++++++++-- sionna/ofdm/equalization.py | 312 ++- sionna/ofdm/pilot_pattern.py | 34 +- sionna/ofdm/resource_grid.py | 20 +- sionna/utils/misc.py | 29 +- sionna/utils/plotting.py | 31 +- test/codes/turbo/ref_k112_u.npy | Bin 0 -> 9088 bytes test/codes/turbo/ref_k112_uhat.npy | Bin 0 -> 9088 bytes test/codes/turbo/ref_k112_x.npy | Bin 0 -> 3608 bytes test/codes/turbo/ref_k112_y.npy | Bin 0 -> 27968 bytes test/codes/turbo/ref_k168_u.npy | Bin 0 -> 13568 bytes test/codes/turbo/ref_k168_uhat.npy | Bin 0 -> 13568 bytes test/codes/turbo/ref_k168_x.npy | Bin 0 -> 5288 bytes test/codes/turbo/ref_k168_y.npy | Bin 0 -> 41408 bytes test/codes/turbo/ref_k40_u.npy | Bin 0 -> 3328 bytes test/codes/turbo/ref_k40_uhat.npy | Bin 0 -> 3328 bytes test/codes/turbo/ref_k40_x.npy | Bin 0 -> 1448 bytes test/codes/turbo/ref_k40_y.npy | Bin 0 -> 10688 bytes test/codes/turbo/ref_k432_u.npy | Bin 0 -> 34688 bytes test/codes/turbo/ref_k432_uhat.npy | Bin 0 -> 34688 bytes test/codes/turbo/ref_k432_x.npy | Bin 0 -> 13208 bytes test/codes/turbo/ref_k432_y.npy | Bin 0 -> 104768 bytes test/integration/test_ofdm_mimo_detectors.py | 145 ++ .../test_ofdm_mimo_estimation_detection.py | 217 ++ test/unit/channel/channel_test_utils.py | 74 +- test/unit/channel/test_3gpp_channel_tdl.py | 341 +++- test/unit/fec/Validate_OSD.ipynb | 808 ++++++++ test/unit/fec/test_conv_decoding.py | 517 +++-- test/unit/fec/test_conv_encoding.py | 147 +- test/unit/fec/test_crc.py | 4 + test/unit/fec/test_fec_utils.py | 188 +- test/unit/fec/test_ldpc_encoding.py | 96 +- test/unit/fec/test_linear_decoding.py | 275 +++ test/unit/fec/test_linear_encoding.py | 297 +++ test/unit/fec/test_turbo_decoding.py | 141 +- test/unit/fec/test_turbo_encoding.py | 13 +- test/unit/mapping/test_mapping.py | 36 +- test/unit/mimo/test_ep_det.py | 207 ++ test/unit/mimo/test_kbest_det.py | 423 ++++ test/unit/mimo/test_mmse_pic_det.py | 385 ++++ .../unit/ofdm/test_ofdm_channel_estimation.py | 852 +++++++- 99 files changed, 13375 insertions(+), 2202 deletions(-) create mode 100644 doc/source/api/fec.linear.rst create mode 100644 examples/OFDM_MIMO_Detection.ipynb create mode 100644 sionna/channel/tr38901/models/TDL-A30.json create mode 100644 sionna/channel/tr38901/models/TDL-B100.json create mode 100644 sionna/channel/tr38901/models/TDL-C300.json create mode 100644 sionna/fec/linear/__init__.py create mode 100644 sionna/fec/linear/decoding.py create mode 100644 sionna/fec/linear/encoding.py create mode 100644 test/codes/turbo/ref_k112_u.npy create mode 100644 test/codes/turbo/ref_k112_uhat.npy create mode 100644 test/codes/turbo/ref_k112_x.npy create mode 100644 test/codes/turbo/ref_k112_y.npy create mode 100644 test/codes/turbo/ref_k168_u.npy create mode 100644 test/codes/turbo/ref_k168_uhat.npy create mode 100644 test/codes/turbo/ref_k168_x.npy create mode 100644 test/codes/turbo/ref_k168_y.npy create mode 100644 test/codes/turbo/ref_k40_u.npy create mode 100644 test/codes/turbo/ref_k40_uhat.npy create mode 100644 test/codes/turbo/ref_k40_x.npy create mode 100644 test/codes/turbo/ref_k40_y.npy create mode 100644 test/codes/turbo/ref_k432_u.npy create mode 100644 test/codes/turbo/ref_k432_uhat.npy create mode 100644 test/codes/turbo/ref_k432_x.npy create mode 100644 test/codes/turbo/ref_k432_y.npy create mode 100644 test/integration/test_ofdm_mimo_detectors.py create mode 100644 test/integration/test_ofdm_mimo_estimation_detection.py create mode 100755 test/unit/fec/Validate_OSD.ipynb create mode 100644 test/unit/fec/test_linear_decoding.py create mode 100644 test/unit/fec/test_linear_encoding.py create mode 100644 test/unit/mimo/test_ep_det.py create mode 100644 test/unit/mimo/test_kbest_det.py create mode 100644 test/unit/mimo/test_mmse_pic_det.py diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml index 3c55a6cb..e69de29b 100644 --- a/.gitlab-ci.yml +++ b/.gitlab-ci.yml @@ -1,110 +0,0 @@ -## -## Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. -## -## NVIDIA CORPORATION and its licensors retain all intellectual property -## and proprietary rights in and to this software, related documentation -## and any modifications thereto. Any use, reproduction, disclosure or -## distribution of this software and related documentation without an express -## license agreement from NVIDIA CORPORATION is strictly prohibited. -## -stages: - - build - - test -documentation: - image: gitlab-master.nvidia.com:5005/nvresearch-gcml/sionna/python-doc:latest - stage: build - before_script: - - echo 'Cleanup environment...' - - git branch -D $CI_DOCUMENTATION_BRANCH || IGNORE_FAILURE=true - - git remote remove origin-rw || IGNORE_FAILURE=true - - git config --local --replace-all user.name "${CI_GIT_USER_NAME}" || IGNORE_FAILURE=true - - git config --local --replace-all user.email "${CI_GIT_USER_EMAIL}" || IGNORE_FAILURE=true - script: - - echo 'Building documentation...' - - make doc - - echo 'Fetch current state of documentation branch...' - - REPO_URL=`echo $CI_REPOSITORY_URL | cut -d'@' -f 2` - - git remote add origin-rw https://$CI_GIT_RW_NAME:$CI_GIT_RW_TOKEN@$REPO_URL - - git remote -v - - git fetch origin-rw $CI_DOCUMENTATION_BRANCH - - git checkout -b $CI_DOCUMENTATION_BRANCH --track origin-rw/${CI_DOCUMENTATION_BRANCH} - - echo 'Replace website folders with updated version...' - - rm -rf docs - - mv doc/build/html docs - - echo 'Commit changes to git' - - git add docs - - git status - - | - if git diff --cached --quiet - then - echo 'No changes detected.' - else - git commit -m "update Documentation from commit ${CI_COMMIT_SHORT_SHA}" - git log -n 1 - git push origin-rw $CI_DOCUMENTATION_BRANCH - fi - - echo 'Done.' - tags: - artifacts: - name: "$CI_PROJECT_NAME-docs-$CI_COMMIT_SHORT_SHA" - paths: - - docs - only: - - main -all-tests: - image: gitlab-master.nvidia.com:5005/nvresearch-gcml/sionna/test-sionna-tensorflow:2.8.0-gpu-jupyter - stage: test - script: - - nvidia-smi - - cd test - - pytest --junitxml=report.xml - tags: - - test - artifacts: - when: always - reports: - junit: test/report.xml - only: - - main -all-tests-tf-2.8.2: - image: gitlab-master.nvidia.com:5005/nvresearch-gcml/sionna/test-sionna-tensorflow:2.8.2-gpu-jupyter - stage: test - script: - - nvidia-smi - - cd test - - pytest --junitxml=report-tf-2.8.2.xml - tags: - - test - artifacts: - when: always - reports: - junit: test/report-tf-2.8.2.xml - when: manual -all-tests-tf-2.9.1: - image: gitlab-master.nvidia.com:5005/nvresearch-gcml/sionna/test-sionna-tensorflow:2.9.1-gpu-jupyter - stage: test - script: - - nvidia-smi - - cd test - - pytest --junitxml=report-tf-2.9.1.xml - tags: - - test - artifacts: - when: always - reports: - junit: test/report-tf-2.9.1.xml - when: manual -all-tests-tf-2.10.0: - image: gitlab-master.nvidia.com:5005/nvresearch-gcml/sionna/test-sionna-tensorflow:2.10.0-gpu-jupyter - stage: test - script: - - nvidia-smi - - cd test - - pytest --junitxml=report-tf-2.10.0.xml - tags: - - test - artifacts: - when: always - reports: - junit: test/report-tf-2.10.0.xml - when: manual diff --git a/README.md b/README.md index 5a1604ab..0f5770ea 100644 --- a/README.md +++ b/README.md @@ -14,7 +14,7 @@ In order to run the tutorial notebooks on your machine, you also need [Jupyter]( You can alternatively test them on [Google Colab](https://colab.research.google.com/). Although not necessary, we recommend running Sionna in a [Docker container](https://www.docker.com). -Sionna requires [TensorFlow 2.6-2.10](https://www.tensorflow.org/install) and Python 3.6-3.9. We recommend Ubuntu 20.04. +Sionna requires [TensorFlow 2.7-2.10](https://www.tensorflow.org/install) and Python 3.6-3.9. We recommend Ubuntu 20.04. TensorFlow 2.6 still works but is not recommended because of known, unpatched CVEs. We refer to the [TensorFlow GPU support tutorial](https://www.tensorflow.org/install/gpu) for GPU support and the required driver setup. @@ -35,7 +35,7 @@ On macOS, you need to install [tensorflow-macos](https://github.com/apple/tensor ``` >>> import sionna >>> print(sionna.__version__) - 0.11.0 + 0.12.0 ``` 3.) Once Sionna is installed, you can run the [Sionna "Hello, World!" example](https://nvlabs.github.io/sionna/examples/Hello_World.html), have a look at the [quick start guide](https://nvlabs.github.io/sionna/quickstart.html), or at the [tutorials](https://nvlabs.github.io/sionna/tutorials.html). @@ -94,7 +94,7 @@ We recommend to do this within a [virtual environment](https://docs.python.org/3 ``` >>> import sionna >>> print(sionna.__version__) - 0.11.0 + 0.12.0 ``` ## License and Citation diff --git a/doc/source/api/channel.wireless.rst b/doc/source/api/channel.wireless.rst index 0c08913e..9981bcb2 100644 --- a/doc/source/api/channel.wireless.rst +++ b/doc/source/api/channel.wireless.rst @@ -580,7 +580,11 @@ one_ring_corr_mat References: .. [TR38901] 3GPP TR 38.901, - “Study on channel model for frequencies from 0.5 to 100 GHz“, Release 16.1 + "Study on channel model for frequencies from 0.5 to 100 GHz", Release 16.1 + + .. [TS38141-1] 3GPP TS 38.141-1 + "Base Station (BS) conformance testing Part 1: Conducted conformance testing", + Release 17 .. [Tse] D. Tse and P. Viswanath, “Fundamentals of wireless communication“, Cambridge university press, 2005. diff --git a/doc/source/api/fec.conv.rst b/doc/source/api/fec.conv.rst index 6e42af70..9616239f 100644 --- a/doc/source/api/fec.conv.rst +++ b/doc/source/api/fec.conv.rst @@ -3,7 +3,7 @@ Convolutional Codes This module supports encoding of convolutional codes and provides layers for Viterbi [Viterbi]_ and BCJR [BCJR]_ decoding. -While the :class:`~sionna.fec.conv.decoding.ViterbiDecoder` decoding algorithm produces maximum likelihood *sequence* estimations, the :class:`~sionna.fec.conv.decoding.BCJRDecoder` produces the a posterior probability (APP) bit-estimates. +While the :class:`~sionna.fec.conv.decoding.ViterbiDecoder` decoding algorithm produces maximum likelihood *sequence* estimates, the :class:`~sionna.fec.conv.decoding.BCJRDecoder` produces the maximum a posterior (MAP) bit-estimates. The following code snippet shows how to set up a rate-1/2, constraint-length-3 :class:`~sionna.fec.conv.encoding.ConvEncoder` in two alternate ways and a corresponding :class:`~sionna.fec.conv.decoding.ViterbiDecoder` or :class:`~sionna.fec.conv.decoding.BCJRDecoder`. You can find further examples in the `Channel Coding Tutorial Notebook <../examples/5G_Channel_Coding_Polar_vs_LDPC_Codes.html>`_. @@ -16,11 +16,16 @@ Setting-up: # or encoder = ConvEncoder(gen_poly=['101', '111']) # or polynomial can be used as input directly - # Viterbi decoding + # --- Viterbi decoding --- decoder = ViterbiDecoder(gen_poly=encoder.gen_poly) # polynomial used in encoder + # or just reference to the encoder + decoder = ViterbiDecoder(encoder=encoder) # the code parameters are infered from the encoder - # or BCJR decoding - decoder = BCJRDecoder(gen_poly=encoder.gen_poly) # polynomial used in encoder + # --- or BCJR decoding --- + decoder = BCJRDecoder(gen_poly=encoder.gen_poly, algorithm="map") # polynomial used in encoder + + # or just reference to the encoder + decoder = BCJRDecoder(encoder=encoder, algorithm="map") # the code parameters are infered from the encoder Running the encoder / decoder: @@ -75,12 +80,13 @@ polynomial_selector .. autofunction:: sionna.fec.conv.utils.polynomial_selector + References: - .. [Viterbi] A. Viterbi "Error bounds for convolutional codes and an - asymptotically optimum decoding algorithm", IEEE Trans Inf Theory, 1967. + .. [Viterbi] A. Viterbi, "Error bounds for convolutional codes and an + asymptotically optimum decoding algorithm", IEEE Trans. Inf. Theory, 1967. - .. [BCJR] L. Bahl, J. Cocke, F. Jelinek, und J. Raviv "Optimal Decoding - of Linear Codes for Minimizing Symbol Error Rate", IEEE Trans Inf + .. [BCJR] L. Bahl, J. Cocke, F. Jelinek, und J. Raviv, "Optimal Decoding + of Linear Codes for Minimizing Symbol Error Rate", IEEE Trans. Inf. Theory, March 1974. .. [Moon] Todd. K. Moon, "Error Correction Coding: Mathematical diff --git a/doc/source/api/fec.ldpc.rst b/doc/source/api/fec.ldpc.rst index daa4e375..1fe55224 100644 --- a/doc/source/api/fec.ldpc.rst +++ b/doc/source/api/fec.ldpc.rst @@ -32,8 +32,8 @@ Now, the encoder and decoder can be used by: # u_hat contains the estimated information bits and has shape [...,k]. u_hat = decoder(llr) -Encoder -******* +LDPC Encoder +************ LDPC5GEncoder ------------- @@ -42,15 +42,8 @@ LDPC5GEncoder :members: :exclude-members: call, build -AllZeroEncoder --------------- - -.. autoclass:: sionna.fec.ldpc.encoding.AllZeroEncoder - :members: - :exclude-members: call, build - -Decoder -******* +LDPC Decoder +************ LDPCBPDecoder ------------- diff --git a/doc/source/api/fec.linear.rst b/doc/source/api/fec.linear.rst new file mode 100644 index 00000000..83068bb9 --- /dev/null +++ b/doc/source/api/fec.linear.rst @@ -0,0 +1,80 @@ +Linear Codes +############ + +This package provides generic support for binary linear block codes. + +For encoding, a universal :class:`~sionna.fec.linear.LinearEncoder` is available and can be initialized with either a generator or parity-check matrix. The matrix must be binary and of full rank. + +For decoding, :class:`~sionna.fec.linear.OSDecoder` implements the +ordered-statistics decoding (OSD) algorithm [Fossorier]_ which provides close to +maximum-likelihood (ML) estimates for a sufficiently large order of the decoder. +Please note that OSD is highly complex and not feasible for all code lengths. + +*Remark:* As this package provides support for generic encoding and decoding +(including Polar and LDPC codes), it cannot rely on code specific +optimizations. To benefit from an optimized decoder and keep the complexity as low as possible, please use the code specific enc-/decoders whenever available. + +The encoder and decoder can be set up as follows: + +.. code-block:: Python + + pcm, k, n, coderate = load_parity_check_examples(pcm_id=1) # load example code + + # or directly import an external parity-check matrix in alist format + al = load_alist(path=filename) + pcm, k, n, coderate = alist2mat(al) + + # encoder can be directly initialized with the parity-check matrix + encoder = LinearEncoder(enc_mat=pcm, is_pcm=True) + + # decoder can be initialized with generator or parity-check matrix + decoder = OSDecoder(pcm, t=4, is_pcm=True) # t is the OSD order + + # or instantiated from a specific encoder + decoder = OSDecoder(encoder=encoder, t=4) # t is the OSD order + +We can now run the encoder and decoder: + +.. code-block:: Python + + # u contains the information bits to be encoded and has shape [...,k]. + # c contains codeword bits and has shape [...,n] + c = encoder(u) + + # after transmission LLRs must be calculated with a demapper + # let's assume the resulting llr_ch has shape [...,n] + c_hat = decoder(llr_ch) + + +Encoder +******* + +LinearEncoder +------------- +.. autoclass:: sionna.fec.linear.LinearEncoder + :members: + :exclude-members: call, build + +AllZeroEncoder +-------------- +.. autoclass:: sionna.fec.linear.AllZeroEncoder + :members: + :exclude-members: call, build + +Decoder +******* + +OSDecoder +--------- +.. autoclass:: sionna.fec.linear.OSDecoder + :members: + :exclude-members: call, build + +References: + .. [Fossorier] M. Fossorier, S. Lin, "Soft-Decision Decoding of Linear + Block Codes Based on Ordered Statistics", IEEE Trans. Inf. + Theory, vol. 41, no.5, 1995. + + .. [Stimming_LLR_OSD] A.Balatsoukas-Stimming, M. Parizi, A. Burg, + "LLR-Based Successive Cancellation List Decoding + of Polar Codes." IEEE Trans Signal Processing, 2015. diff --git a/doc/source/api/fec.rst b/doc/source/api/fec.rst index 1921aeab..247a375d 100644 --- a/doc/source/api/fec.rst +++ b/doc/source/api/fec.rst @@ -41,6 +41,7 @@ All this--and much more--can be explored within the Sionna FEC module. .. toctree:: :maxdepth: 3 + fec.linear fec.ldpc fec.polar fec.conv diff --git a/doc/source/api/fec.turbo.rst b/doc/source/api/fec.turbo.rst index 9c15b905..a0906cf4 100644 --- a/doc/source/api/fec.turbo.rst +++ b/doc/source/api/fec.turbo.rst @@ -9,7 +9,7 @@ decoders are composed of the :class:`~sionna.fec.conv.encoding.ConvEncoder` and Please note that various notations are used in literature to represent the generator polynomials for the underlying convolutional codes. For simplicity, :class:`~sionna.fec.turbo.encoding.TurboEncoder` only accepts the binary -format, i.e., `10011` for the generator polynomial which corresponds to the +format, i.e., `10011`, for the generator polynomial which corresponds to the polynomial :math:`1 + D^3 + D^4`. The following code snippet shows how to set-up a rate-1/3, constraint-length-4 :class:`~sionna.fec.turbo.encoding.TurboEncoder` and the corresponding :class:`~sionna.fec.turbo.decoding.TurboDecoder`. @@ -27,11 +27,12 @@ Setting-up: rate=1/3, # Rate of the desired Turbo code terminate=False) # Do not terminate the constituent convolutional encoders + # the decoder can be initialized with a reference to the encoder decoder = TurboDecoder(encoder, num_iter=6, # Number of iterations between component BCJR decoders + algorithm="map", # can be also "maxlog" hard_out=True) # hard_decide output - Running the encoder / decoder: .. code-block:: Python diff --git a/doc/source/api/fec.utils.rst b/doc/source/api/fec.utils.rst index 1da66cae..e9a4fc64 100644 --- a/doc/source/api/fec.utils.rst +++ b/doc/source/api/fec.utils.rst @@ -4,19 +4,15 @@ Utility Functions This module provides utility functions for the FEC package. It also provides serval functions to simplify EXIT analysis of iterative receivers. (Binary) Linear Codes -************************** +*********************** Several functions are provided to convert parity-check matrices into generator matrices and vice versa. Please note that currently only binary codes are supported. -Further, a universal linear encoder is available and can be initialized either with a generator or with a parity-check matrix, respectively. .. code-block:: Python # load example parity-check matrix pcm, k, n, coderate = load_parity_check_examples(pcm_id=3) - # the encoder can be directly initialized with a parity-check matrix - encoder = LinearEncoder(pcm, is_pcm=True) - Note that many research projects provide their parity-check matrices in the `alist` format [MacKay]_ (e.g., see [UniKL]_). The follwing code snippet provides an example of how to import an external LDPC parity-check matrix from an `alist` file and how to set-up an encoder/decoder. .. code-block:: Python @@ -25,7 +21,7 @@ Note that many research projects provide their parity-check matrices in the `al al = load_alist(path=filename) pcm, k, n, coderate = alist2mat(al) - # the encoder can be directly initialized with a parity-check matrix + # the linear encoder can be directly initialized with a parity-check matrix encoder = LinearEncoder(pcm, is_pcm=True) # initalize BP decoder for the given parity-check matrix @@ -48,12 +44,6 @@ Note that many research projects provide their parity-check matrices in the `al llr = demapper([y, no]) c_hat = decoder(llr) -LinearEncoder -------------- -.. autoclass:: sionna.fec.utils.LinearEncoder - :members: - :exclude-members: call, build - load_parity_check_examples -------------------------- .. autofunction:: sionna.fec.utils.load_parity_check_examples @@ -183,6 +173,10 @@ int2bin_tf ---------- .. autofunction:: sionna.fec.utils.int2bin_tf +int_mod_2 +--------- +.. autofunction:: sionna.fec.utils.int_mod_2 + llr2mi ------ .. autofunction:: sionna.fec.utils.llr2mi diff --git a/doc/source/api/mapping.rst b/doc/source/api/mapping.rst index 924a476d..db506029 100755 --- a/doc/source/api/mapping.rst +++ b/doc/source/api/mapping.rst @@ -89,6 +89,24 @@ SymbolLogits2Moments :exclude-members: call, build :members: +SymbolInds2Bits +--------------- +.. autoclass:: sionna.mapping.SymbolInds2Bits + :exclude-members: call, build + :members: + +PAM2QAM +------- +.. autoclass:: sionna.mapping.PAM2QAM + :exclude-members: call, build + :members: + +QAM2PAM +------- +.. autoclass:: sionna.mapping.QAM2PAM + :exclude-members: call, build + :members: + References: - .. [3GPPTS38211] ETSI TS 138 211 "5G NR Physical channels and modulation", V16.2.0, Jul. 2020 + .. [3GPPTS38211] ETSI TS 38.211 "5G NR Physical channels and modulation", V16.2.0, Jul. 2020 https://www.3gpp.org/ftp/Specs/archive/38_series/38.211/38211-h00.zip diff --git a/doc/source/api/mimo.rst b/doc/source/api/mimo.rst index 2b0f7007..256b8766 100644 --- a/doc/source/api/mimo.rst +++ b/doc/source/api/mimo.rst @@ -60,32 +60,70 @@ lmmse_equalizer --------------- .. autofunction:: sionna.mimo.lmmse_equalizer +mf_equalizer +--------------- +.. autofunction:: sionna.mimo.mf_equalizer + zf_equalizer --------------- .. autofunction:: sionna.mimo.zf_equalizer -mf_equalizer ---------------- -.. autofunction:: sionna.mimo.mf_equalizer Detection ********** +EPDetector +---------- +.. autoclass:: sionna.mimo.EPDetector + :exclude-members: call, build, compute_sigma_mu, compute_v_x, compute_v_x_obs, update_lam_gam + :members: + +KBestDetector +------------- +.. autoclass:: sionna.mimo.KBestDetector + :exclude-members: call, build + :members: + +LinearDetector +-------------- +.. autoclass:: sionna.mimo.LinearDetector + :exclude-members: call, build + :members: + MaximumLikelihoodDetector ---------------------------------- +------------------------- .. autoclass:: sionna.mimo.MaximumLikelihoodDetector :exclude-members: call, build :members: MaximumLikelihoodDetectorWithPrior ------------------------------------- +---------------------------------- .. autoclass:: sionna.mimo.MaximumLikelihoodDetectorWithPrior :exclude-members: call, build :members: +MMSE-PIC +---------- +.. autoclass:: sionna.mimo.MMSEPICDetector + :exclude-members: call, build + :members: + Utility Functions ***************** + +List2LLR +-------- +.. autoclass:: sionna.mimo.List2LLR + :exclude-members: __call__ + :members: + +List2LLRSimple +-------------- +.. autoclass:: sionna.mimo.List2LLRSimple + :exclude-members: call, build + :members: + complex2real_vector ------------------- .. autofunction:: sionna.mimo.complex2real_vector @@ -131,10 +169,21 @@ References: .. [ProperRV] `Proper complex random variables `_, Wikipedia, accessed 11 September, 2022. - + .. [CovProperRV] `Covariance matrices of real and imaginary parts `_, Wikipedia, accessed 11 September, 2022. .. [YH2015] S. Yang and L. Hanzo, `"Fifty Years of MIMO Detection: The Road to Large-Scale MIMOs" `_, IEEE Communications Surveys & Tutorials, vol. 17, no. 4, pp. 1941-1988, 2015. + + .. [FT2015] W. Fu and J. S. Thompson, `"Performance analysis of K-best detection with adaptive modulation" + `_, IEEE Int. Symp. Wirel. Commun. Sys. (ISWCS), 2015. + + .. [EP2014] J. Céspedes, P. M. Olmos, M. Sánchez-Fernández, and F. Perez-Cruz, + `"Expectation Propagation Detection for High-Order High-Dimensional MIMO Systems" `_, + IEEE Trans. Commun., vol. 62, no. 8, pp. 2840-2849, Aug. 2014. + + .. [CST2011] C. Studer, S. Fateh, and D. Seethaler, + `"ASIC Implementation of Soft-Input Soft-Output MIMO Detection Using MMSE Parallel Interference Cancellation" `_, + IEEE Journal of Solid-State Circuits, vol. 46, no. 7, pp. 1754–1765, July 2011. diff --git a/doc/source/api/ofdm.rst b/doc/source/api/ofdm.rst index 6204a2fc..d2e7b2c7 100644 --- a/doc/source/api/ofdm.rst +++ b/doc/source/api/ofdm.rst @@ -20,9 +20,11 @@ the module provides the :class:`~sionna.ofdm.KroneckerPilotPattern` class that automatically generates orthogonal pilot transmissions for all transmitters and streams. -Additionally, the module contains layers for channel estimation, precoding and equalization, +Additionally, the module contains layers for channel estimation, precoding, +equalization, and detection, such as the :class:`~sionna.ofdm.LSChannelEstimator`, the -:class:`~sionna.ofdm.ZFPrecoder`, and the :class:`~sionna.ofdm.LMMSEEqualizer`. +:class:`~sionna.ofdm.ZFPrecoder`, and the :class:`~sionna.ofdm.LMMSEEqualizer` and +:class:`~sionna.ofdm.LinearDetector`. These are good starting points for the development of more advanced algorithms and provide robust baselines for benchmarking. @@ -177,10 +179,32 @@ KroneckerPilotPattern Channel Estimation ****************** +BaseChannelEstimator +-------------------- +.. autoclass:: sionna.ofdm.BaseChannelEstimator + :exclude-members: call, build + :members: + +BaseChannelInterpolator +------------------------ +.. autoclass:: sionna.ofdm.BaseChannelInterpolator + :exclude-members: call, build + :members: + LSChannelEstimator ------------------ .. autoclass:: sionna.ofdm.LSChannelEstimator - :exclude-members: call, build + :exclude-members: call, build, estimate_at_pilot_locations + :members: + +LinearInterpolator +------------------- +.. autoclass:: sionna.ofdm.LinearInterpolator + :members: + +LMMSEInterpolator +------------------- +.. autoclass:: sionna.ofdm.LMMSEInterpolator :members: NearestNeighborInterpolator @@ -188,10 +212,13 @@ NearestNeighborInterpolator .. autoclass:: sionna.ofdm.NearestNeighborInterpolator :members: -LinearInterpolator ---------------------------- -.. autoclass:: sionna.ofdm.LinearInterpolator - :members: +tdl_time_cov_mat +----------------- +.. autofunction:: sionna.ofdm.tdl_time_cov_mat + +tdl_freq_cov_mat +----------------- +.. autofunction:: sionna.ofdm.tdl_freq_cov_mat Precoding @@ -207,15 +234,64 @@ ZFPrecoder Equalization ************ +OFDMEqualizer +-------------- +.. autoclass:: sionna.ofdm.OFDMEqualizer + :exclude-members: call, build + :members: + LMMSEEqualizer -------------- .. autoclass:: sionna.ofdm.LMMSEEqualizer :exclude-members: call, build :members: +MFEqualizer +------------ +.. autoclass:: sionna.ofdm.MFEqualizer + :exclude-members: call, build + :members: + +ZFEqualizer +------------ +.. autoclass:: sionna.ofdm.ZFEqualizer + :exclude-members: call, build + :members: + + Detection ********** +OFDMDetector +------------- +.. autoclass:: sionna.ofdm.OFDMDetector + :exclude-members: call, build + :members: + +OFDMDetectorWithPrior +----------------------- +.. autoclass:: sionna.ofdm.OFDMDetectorWithPrior + :exclude-members: call, build + :members: + +EPDetector +--------------- +.. autoclass:: sionna.ofdm.EPDetector + :exclude-members: call, build + :members: + +KBestDetector +--------------- +.. autoclass:: sionna.ofdm.KBestDetector + :exclude-members: call, build + :members: + +LinearDetector +--------------- +.. autoclass:: sionna.ofdm.LinearDetector + :exclude-members: call, build + :members: + MaximumLikelihoodDetector ---------------------------- .. autoclass:: sionna.ofdm.MaximumLikelihoodDetector @@ -227,3 +303,9 @@ MaximumLikelihoodDetectorWithPrior .. autoclass:: sionna.ofdm.MaximumLikelihoodDetectorWithPrior :exclude-members: call, build :members: + +MMSEPICDetector +---------------- +.. autoclass:: sionna.ofdm.MMSEPICDetector + :exclude-members: call, build + :members: diff --git a/doc/source/figures/drop_uts_in_sector_topology.png 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-| + The Benefits of using Sionna ################################### @@ -79,5 +79,3 @@ If you use this software, please cite it as: journal = {arXiv preprint}, online = {https://arxiv.org/abs/2203.11854} } - - diff --git a/doc/source/installation.rst b/doc/source/installation.rst index d9a3a0ea..d252e09a 100644 --- a/doc/source/installation.rst +++ b/doc/source/installation.rst @@ -7,7 +7,7 @@ You can alternatively test them on `Google Colab `_. .. note:: - Sionna requires `TensorFlow 2.6-2.9 `_ and Python 3.6-3.9. + Sionna requires `TensorFlow 2.7-2.10 `_ and Python 3.6-3.9. We recommend Ubuntu 20.04. We refer to the `TensorFlow GPU support tutorial `_ for GPU support and the required driver setup. @@ -37,7 +37,7 @@ e.g., using `conda `_. On macOS, you need to install `ten >>> import sionna >>> print(sionna.__version__) - 0.11.0 + 0.12.0 3.) Once Sionna is installed, you can run the `Sionna "Hello, World!" example `_, have a look at the `quick start guide `_, or at the `tutorials `_. @@ -109,4 +109,4 @@ e.g., using `conda `_. >>> import sionna >>> print(sionna.__version__) - 0.11.0 + 0.12.0 diff --git a/doc/source/made_with_sionna.rst b/doc/source/made_with_sionna.rst index 40196030..9f55310c 100644 --- a/doc/source/made_with_sionna.rst +++ b/doc/source/made_with_sionna.rst @@ -10,6 +10,28 @@ List of Projects If you want your paper and code be listed here, please send an email to `sionna@nvidia.com `_ with links to the paper (e.g., `arXiv `_) and code repository (e.g., `GitHub `_). +Bit Error and Block Error Rate Training for ML-Assisted Communication +********************************************************************* +.. made-with-sionna:: + :title: Bit Error and Block Error Rate Training for ML-Assisted Communication + :authors: Reinhard Wiesmayr, Gian Marti, Chris Dick, Haochuan Song, Christoph Studer + :year: 2022 + :version: 0.11 + :link_arxiv: https://arxiv.org/pdf/2210.14103.pdf + :link_github: https://github.com/IIP-Group/BLER_Training + :abstract: Even though machine learning (ML) techniques are being + widely used in communications, the question of how to train + communication systems has received surprisingly little + attention. In this paper, we show that the commonly used binary + cross-entropy (BCE) loss is a sensible choice in uncoded + systems, e.g., for training ML-assisted data detectors, but may + not be optimal in coded systems. We propose new loss functions + targeted at minimizing the block error rate and SNR deweighting, + a novel method that trains communication systems for optimal + performance over a range of signal-to-noise ratios. The utility + of the proposed loss functions as well as of SNR deweighting is + shown through simulations in NVIDIA Sionna. + GNNs for Channel Decoding ************************* .. made-with-sionna:: @@ -32,3 +54,5 @@ DL-based Synchronization of NB-IoT :link_arxiv: https://arxiv.org/pdf/2205.10805.pdf :link_github: https://github.com/NVlabs/nprach_synch :abstract: We propose a neural network (NN)-based algorithm for device detection and time of arrival (ToA) and carrier frequency offset (CFO) estimation for the narrowband physical random-access channel (NPRACH) of narrowband internet of things (NB-IoT). The introduced NN architecture leverages residual convolutional networks as well as knowledge of the preamble structure of the 5G New Radio (5G NR) specifications. + + diff --git a/doc/source/tutorials.rst b/doc/source/tutorials.rst index e7b31174..ee685c82 100644 --- a/doc/source/tutorials.rst +++ b/doc/source/tutorials.rst @@ -33,6 +33,7 @@ For Experts examples/MIMO_OFDM_Transmissions_over_CDL.ipynb examples/Neural_Receiver.ipynb examples/Realistic_Multiuser_MIMO_Simulations.ipynb + examples/OFDM_MIMO_Detection.ipynb examples/Autoencoder.ipynb examples/Weighted_BP_Algorithm.ipynb examples/CIR_Dataset.ipynb diff --git a/examples/5G_Channel_Coding_Polar_vs_LDPC_Codes.ipynb b/examples/5G_Channel_Coding_Polar_vs_LDPC_Codes.ipynb index 63b569b8..207555c1 100644 --- a/examples/5G_Channel_Coding_Polar_vs_LDPC_Codes.ipynb +++ b/examples/5G_Channel_Coding_Polar_vs_LDPC_Codes.ipynb @@ -28,6 +28,8 @@ "\n", "* Turbo codes and iterative BCJR decoding\n", "\n", + "* Ordered statistics decoding (OSD) for any binary, linear code\n", + "\n", "* Interleaving and scrambling\n", "\n", "For additional technical background we refer the interested reader to [4,5,8].\n", @@ -118,10 +120,12 @@ "from sionna.fec.polar.utils import generate_5g_ranking, generate_rm_code\n", "from sionna.fec.conv import ConvEncoder, ViterbiDecoder, BCJRDecoder\n", "from sionna.fec.turbo import TurboEncoder, TurboDecoder\n", + "from sionna.fec.linear import OSDecoder\n", "from sionna.utils import BinarySource, ebnodb2no\n", "from sionna.utils.metrics import count_block_errors\n", "from sionna.channel import AWGN\n", - "from sionna.utils.plotting import PlotBER\n" + "from sionna.utils.plotting import PlotBER\n", + "\n" ] }, { @@ -181,7 +185,9 @@ " \n", " sim_esno: bool \n", " A boolean defaults to False. If true, no rate-adjustment is done for the SNR calculation.\n", - " \n", + "\n", + " cw_estiamtes: bool \n", + " A boolean defaults to False. If true, codewords instead of information estimates are returned.\n", " Input\n", " -----\n", " batch_size: int or tf.int\n", @@ -208,7 +214,8 @@ " encoder,\n", " decoder,\n", " demapping_method=\"app\",\n", - " sim_esno=False):\n", + " sim_esno=False,\n", + " cw_estimates=False):\n", "\n", " super().__init__()\n", " \n", @@ -216,6 +223,7 @@ " self.k = k\n", " self.n = n\n", " self.sim_esno = sim_esno # disable rate-adjustment for SNR calc\n", + " self.cw_estimates=cw_estimates # if true codewords instead of info bits are returned\n", " \n", " # number of bit per QAM symbol\n", " self.num_bits_per_symbol = num_bits_per_symbol\n", @@ -261,6 +269,9 @@ "\n", " u_hat = self.decoder(llr_ch) # run FEC decoder (incl. rate-recovery)\n", "\n", + " if self.cw_estimates:\n", + " return c, u_hat\n", + " \n", " return u, u_hat" ] }, @@ -302,14 +313,14 @@ "# Polar Codes (SC decoding)\n", "enc = Polar5GEncoder(k=k, n=n)\n", "dec = Polar5GDecoder(enc, dec_type=\"SC\")\n", - "name = \"5G Polar SC\"\n", + "name = \"5G Polar+CRC SC\"\n", "codes_under_test.append([enc, dec, name])\n", "\n", "# Polar Codes (SCL decoding) with list size 8.\n", "# The CRC is automatically added by the layer.\n", "enc = Polar5GEncoder(k=k, n=n)\n", "dec = Polar5GDecoder(enc, dec_type=\"SCL\", list_size=8)\n", - "name = \"5G Polar SCL-8+CRC\"\n", + "name = \"5G Polar+CRC SCL-8\"\n", "codes_under_test.append([enc, dec, name])\n", "\n", "### non-5G coding schemes\n", @@ -369,94 +380,94 @@ "output_type": "stream", "text": [ "\n", - " Running: 5G LDPC BP-20\n", + "Running: 5G LDPC BP-20\n", "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", "---------------------------------------------------------------------------------------------------------------------------------------\n", - " 0.0 | 1.6828e-01 | 8.6910e-01 | 107696 | 640000 | 8691 | 10000 | 2.3 |reached target bit errors\n", - " 0.5 | 1.2593e-01 | 6.9640e-01 | 80594 | 640000 | 6964 | 10000 | 0.1 |reached target bit errors\n", - " 1.0 | 8.6875e-02 | 5.0830e-01 | 55600 | 640000 | 5083 | 10000 | 0.1 |reached target bit errors\n", - " 1.5 | 5.0580e-02 | 3.0430e-01 | 32371 | 640000 | 3043 | 10000 | 0.1 |reached target bit errors\n", - " 2.0 | 2.4297e-02 | 1.4930e-01 | 15550 | 640000 | 1493 | 10000 | 0.1 |reached target bit errors\n", - " 2.5 | 1.0675e-02 | 6.6200e-02 | 6832 | 640000 | 662 | 10000 | 0.1 |reached target bit errors\n", - " 3.0 | 3.8984e-03 | 2.5500e-02 | 2495 | 640000 | 255 | 10000 | 0.1 |reached target bit errors\n", - " 3.5 | 8.4609e-04 | 5.7500e-03 | 1083 | 1280000 | 115 | 20000 | 0.2 |reached target bit errors\n", - " 4.0 | 2.6432e-04 | 1.6000e-03 | 1015 | 3840000 | 96 | 60000 | 0.6 |reached target bit errors\n", - " 4.5 | 3.3355e-05 | 2.3061e-04 | 1046 | 31360000 | 113 | 490000 | 4.6 |reached target bit errors\n", + " 0.0 | 1.6724e-01 | 8.5960e-01 | 107031 | 640000 | 8596 | 10000 | 2.5 |reached target block errors\n", + " 0.5 | 1.2503e-01 | 6.9560e-01 | 80018 | 640000 | 6956 | 10000 | 0.1 |reached target block errors\n", + " 1.0 | 8.8070e-02 | 5.1250e-01 | 56365 | 640000 | 5125 | 10000 | 0.1 |reached target block errors\n", + " 1.5 | 5.2178e-02 | 3.1040e-01 | 33394 | 640000 | 3104 | 10000 | 0.1 |reached target block errors\n", + " 2.0 | 2.5391e-02 | 1.5390e-01 | 16250 | 640000 | 1539 | 10000 | 0.1 |reached target block errors\n", + " 2.5 | 1.0280e-02 | 6.4150e-02 | 13159 | 1280000 | 1283 | 20000 | 0.2 |reached target block errors\n", + " 3.0 | 3.3266e-03 | 2.0760e-02 | 10645 | 3200000 | 1038 | 50000 | 0.5 |reached target block errors\n", + " 3.5 | 9.5947e-04 | 6.0882e-03 | 10439 | 10880000 | 1035 | 170000 | 1.6 |reached target block errors\n", + " 4.0 | 2.0158e-04 | 1.3400e-03 | 9676 | 48000000 | 1005 | 750000 | 7.1 |reached target block errors\n", + " 4.5 | 4.0484e-05 | 2.5700e-04 | 2591 | 64000000 | 257 | 1000000 | 9.5 |reached max iter \n", "\n", - " Running: 5G Polar SC\n", + "Running: 5G Polar+CRC SC\n", "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", "---------------------------------------------------------------------------------------------------------------------------------------\n", - " 0.0 | 4.0717e-01 | 9.5040e-01 | 260590 | 640000 | 9504 | 10000 | 4.9 |reached target bit errors\n", - " 0.5 | 3.6886e-01 | 8.9410e-01 | 236069 | 640000 | 8941 | 10000 | 0.0 |reached target bit errors\n", - " 1.0 | 3.1433e-01 | 7.9630e-01 | 201171 | 640000 | 7963 | 10000 | 0.0 |reached target bit errors\n", - " 1.5 | 2.4457e-01 | 6.5050e-01 | 156528 | 640000 | 6505 | 10000 | 0.0 |reached target bit errors\n", - " 2.0 | 1.7356e-01 | 4.8280e-01 | 111080 | 640000 | 4828 | 10000 | 0.0 |reached target bit errors\n", - " 2.5 | 1.1146e-01 | 3.1880e-01 | 71335 | 640000 | 3188 | 10000 | 0.0 |reached target bit errors\n", - " 3.0 | 5.9687e-02 | 1.7420e-01 | 38200 | 640000 | 1742 | 10000 | 0.0 |reached target bit errors\n", - " 3.5 | 2.6588e-02 | 7.9800e-02 | 17016 | 640000 | 798 | 10000 | 0.0 |reached target bit errors\n", - " 4.0 | 1.0742e-02 | 3.2900e-02 | 6875 | 640000 | 329 | 10000 | 0.0 |reached target bit errors\n", - " 4.5 | 3.8188e-03 | 1.1600e-02 | 2444 | 640000 | 116 | 10000 | 0.0 |reached target bit errors\n", + " 0.0 | 4.0980e-01 | 9.5260e-01 | 262275 | 640000 | 9526 | 10000 | 4.8 |reached target block errors\n", + " 0.5 | 3.6786e-01 | 8.9330e-01 | 235431 | 640000 | 8933 | 10000 | 0.0 |reached target block errors\n", + " 1.0 | 3.0912e-01 | 7.9180e-01 | 197837 | 640000 | 7918 | 10000 | 0.0 |reached target block errors\n", + " 1.5 | 2.4575e-01 | 6.5500e-01 | 157277 | 640000 | 6550 | 10000 | 0.0 |reached target block errors\n", + " 2.0 | 1.7330e-01 | 4.7950e-01 | 110914 | 640000 | 4795 | 10000 | 0.0 |reached target block errors\n", + " 2.5 | 1.0759e-01 | 3.1080e-01 | 68859 | 640000 | 3108 | 10000 | 0.0 |reached target block errors\n", + " 3.0 | 6.0220e-02 | 1.7530e-01 | 38541 | 640000 | 1753 | 10000 | 0.0 |reached target block errors\n", + " 3.5 | 2.8487e-02 | 8.3300e-02 | 36463 | 1280000 | 1666 | 20000 | 0.1 |reached target block errors\n", + " 4.0 | 1.0125e-02 | 3.1375e-02 | 25920 | 2560000 | 1255 | 40000 | 0.1 |reached target block errors\n", + " 4.5 | 3.1420e-03 | 9.7091e-03 | 22120 | 7040000 | 1068 | 110000 | 0.4 |reached target block errors\n", "\n", - " Running: 5G Polar SCL-8+CRC\n", + "Running: 5G Polar+CRC SCL-8\n", "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", "---------------------------------------------------------------------------------------------------------------------------------------\n", - " 0.0 | 3.4099e-01 | 7.9280e-01 | 218235 | 640000 | 7928 | 10000 | 16.5 |reached target bit errors\n", - " 0.5 | 2.6363e-01 | 6.3760e-01 | 168722 | 640000 | 6376 | 10000 | 2.3 |reached target bit errors\n", - " 1.0 | 1.7447e-01 | 4.3760e-01 | 111662 | 640000 | 4376 | 10000 | 2.3 |reached target bit errors\n", - " 1.5 | 9.0636e-02 | 2.4000e-01 | 58007 | 640000 | 2400 | 10000 | 2.3 |reached target bit errors\n", - " 2.0 | 3.9188e-02 | 1.0500e-01 | 25080 | 640000 | 1050 | 10000 | 2.3 |reached target bit errors\n", - " 2.5 | 1.1811e-02 | 3.2800e-02 | 7559 | 640000 | 328 | 10000 | 2.3 |reached target bit errors\n", - " 3.0 | 3.6297e-03 | 1.0300e-02 | 2323 | 640000 | 103 | 10000 | 2.3 |reached target bit errors\n", - " 3.5 | 5.1094e-04 | 1.4250e-03 | 1308 | 2560000 | 57 | 40000 | 9.2 |reached target bit errors\n", - " 4.0 | 4.5402e-05 | 1.4571e-04 | 1017 | 22400000 | 51 | 350000 | 80.6 |reached target bit errors\n", - " 4.5 | 1.8594e-06 | 5.0000e-06 | 119 | 64000000 | 5 | 1000000 | 232.3 |reached max iter \n", + " 0.0 | 3.3954e-01 | 7.9370e-01 | 217305 | 640000 | 7937 | 10000 | 16.3 |reached target block errors\n", + " 0.5 | 2.5614e-01 | 6.2320e-01 | 163931 | 640000 | 6232 | 10000 | 2.3 |reached target block errors\n", + " 1.0 | 1.7195e-01 | 4.2970e-01 | 110045 | 640000 | 4297 | 10000 | 2.3 |reached target block errors\n", + " 1.5 | 9.5338e-02 | 2.4580e-01 | 61016 | 640000 | 2458 | 10000 | 2.3 |reached target block errors\n", + " 2.0 | 3.8995e-02 | 1.0390e-01 | 24957 | 640000 | 1039 | 10000 | 2.3 |reached target block errors\n", + " 2.5 | 1.2763e-02 | 3.4967e-02 | 24505 | 1920000 | 1049 | 30000 | 6.9 |reached target block errors\n", + " 3.0 | 2.6419e-03 | 7.5214e-03 | 23671 | 8960000 | 1053 | 140000 | 32.1 |reached target block errors\n", + " 3.5 | 4.2701e-04 | 1.2613e-03 | 21863 | 51200000 | 1009 | 800000 | 183.4 |reached target block errors\n", + " 4.0 | 5.9375e-05 | 1.7100e-04 | 3800 | 64000000 | 171 | 1000000 | 229.1 |reached max iter \n", + " 4.5 | 3.3125e-06 | 9.0000e-06 | 212 | 64000000 | 9 | 1000000 | 229.2 |reached max iter \n", "\n", - " Running: Reed Muller (RM) SCL-8\n", + "Running: Reed Muller (RM) SCL-8\n", "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", "---------------------------------------------------------------------------------------------------------------------------------------\n", - " 0.0 | 2.7252e-01 | 6.4890e-01 | 174412 | 640000 | 6489 | 10000 | 13.0 |reached target bit errors\n", - " 0.5 | 1.9537e-01 | 4.7770e-01 | 125038 | 640000 | 4777 | 10000 | 2.0 |reached target bit errors\n", - " 1.0 | 1.1646e-01 | 2.9560e-01 | 74532 | 640000 | 2956 | 10000 | 2.0 |reached target bit errors\n", - " 1.5 | 5.8455e-02 | 1.5240e-01 | 37411 | 640000 | 1524 | 10000 | 2.0 |reached target bit errors\n", - " 2.0 | 2.3052e-02 | 5.9200e-02 | 14753 | 640000 | 592 | 10000 | 2.0 |reached target bit errors\n", - " 2.5 | 6.8781e-03 | 1.9400e-02 | 4402 | 640000 | 194 | 10000 | 2.0 |reached target bit errors\n", - " 3.0 | 1.7984e-03 | 5.0000e-03 | 1151 | 640000 | 50 | 10000 | 2.0 |reached target bit errors\n", - " 3.5 | 2.7969e-04 | 8.0000e-04 | 1074 | 3840000 | 48 | 60000 | 12.0 |reached target bit errors\n", - " 4.0 | 2.8947e-05 | 8.5965e-05 | 1056 | 36480000 | 49 | 570000 | 116.1 |reached target bit errors\n", - " 4.5 | 2.2188e-06 | 6.0000e-06 | 142 | 64000000 | 6 | 1000000 | 202.1 |reached max iter \n", + " 0.0 | 2.7000e-01 | 6.4760e-01 | 172801 | 640000 | 6476 | 10000 | 12.8 |reached target block errors\n", + " 0.5 | 1.9087e-01 | 4.7100e-01 | 122160 | 640000 | 4710 | 10000 | 2.0 |reached target block errors\n", + " 1.0 | 1.1507e-01 | 2.9300e-01 | 73643 | 640000 | 2930 | 10000 | 2.0 |reached target block errors\n", + " 1.5 | 5.9103e-02 | 1.5370e-01 | 37826 | 640000 | 1537 | 10000 | 2.0 |reached target block errors\n", + " 2.0 | 2.3795e-02 | 6.3450e-02 | 30458 | 1280000 | 1269 | 20000 | 4.0 |reached target block errors\n", + " 2.5 | 7.2339e-03 | 1.9750e-02 | 27778 | 3840000 | 1185 | 60000 | 12.0 |reached target block errors\n", + " 3.0 | 1.6989e-03 | 4.7667e-03 | 22833 | 13440000 | 1001 | 210000 | 41.9 |reached target block errors\n", + " 3.5 | 2.5781e-04 | 7.3300e-04 | 16500 | 64000000 | 733 | 1000000 | 199.6 |reached max iter \n", + " 4.0 | 3.1578e-05 | 8.3000e-05 | 2021 | 64000000 | 83 | 1000000 | 199.6 |reached max iter \n", + " 4.5 | 2.3437e-06 | 6.0000e-06 | 150 | 64000000 | 6 | 1000000 | 199.6 |reached max iter \n", "\n", - " Running: Conv. Code Viterbi (constraint length 8)\n", + "Running: Conv. Code Viterbi (constraint length 8)\n", "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", "---------------------------------------------------------------------------------------------------------------------------------------\n", - " 0.0 | 1.6722e-01 | 7.0050e-01 | 107019 | 640000 | 7005 | 10000 | 1.9 |reached target bit errors\n", - " 0.5 | 1.0500e-01 | 5.4520e-01 | 67202 | 640000 | 5452 | 10000 | 0.5 |reached target bit errors\n", - " 1.0 | 6.2780e-02 | 4.0730e-01 | 40179 | 640000 | 4073 | 10000 | 0.5 |reached target bit errors\n", - " 1.5 | 3.2813e-02 | 2.7360e-01 | 21000 | 640000 | 2736 | 10000 | 0.5 |reached target bit errors\n", - " 2.0 | 1.6231e-02 | 1.8770e-01 | 10388 | 640000 | 1877 | 10000 | 0.5 |reached target bit errors\n", - " 2.5 | 8.0641e-03 | 1.2020e-01 | 5161 | 640000 | 1202 | 10000 | 0.5 |reached target bit errors\n", - " 3.0 | 4.0719e-03 | 7.8700e-02 | 2606 | 640000 | 787 | 10000 | 0.5 |reached target bit errors\n", - " 3.5 | 1.8828e-03 | 4.7900e-02 | 1205 | 640000 | 479 | 10000 | 0.5 |reached target bit errors\n", - " 4.0 | 1.0883e-03 | 3.1250e-02 | 1393 | 1280000 | 625 | 20000 | 1.0 |reached target bit errors\n", - " 4.5 | 6.1979e-04 | 1.9433e-02 | 1190 | 1920000 | 583 | 30000 | 1.5 |reached target bit errors\n", + " 0.0 | 1.6208e-01 | 6.8980e-01 | 103733 | 640000 | 6898 | 10000 | 1.8 |reached target block errors\n", + " 0.5 | 1.0615e-01 | 5.4740e-01 | 67936 | 640000 | 5474 | 10000 | 0.5 |reached target block errors\n", + " 1.0 | 6.0327e-02 | 4.0450e-01 | 38609 | 640000 | 4045 | 10000 | 0.5 |reached target block errors\n", + " 1.5 | 3.2498e-02 | 2.7790e-01 | 20799 | 640000 | 2779 | 10000 | 0.5 |reached target block errors\n", + " 2.0 | 1.6691e-02 | 1.8970e-01 | 10682 | 640000 | 1897 | 10000 | 0.5 |reached target block errors\n", + " 2.5 | 7.9234e-03 | 1.1960e-01 | 5071 | 640000 | 1196 | 10000 | 0.5 |reached target block errors\n", + " 3.0 | 4.0820e-03 | 8.0250e-02 | 5225 | 1280000 | 1605 | 20000 | 1.0 |reached target block errors\n", + " 3.5 | 1.9516e-03 | 4.7400e-02 | 3747 | 1920000 | 1422 | 30000 | 1.5 |reached target block errors\n", + " 4.0 | 1.1066e-03 | 3.1350e-02 | 2833 | 2560000 | 1254 | 40000 | 1.9 |reached target block errors\n", + " 4.5 | 6.0313e-04 | 1.9083e-02 | 2316 | 3840000 | 1145 | 60000 | 2.9 |reached target block errors\n", "\n", - " Running: Turbo Code (constraint length 4)\n", + "Running: Turbo Code (constraint length 4)\n", "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", "---------------------------------------------------------------------------------------------------------------------------------------\n", - " 0.0 | 1.0993e-01 | 7.9340e-01 | 70355 | 640000 | 7934 | 10000 | 3.8 |reached target bit errors\n", - " 0.5 | 7.7438e-02 | 6.1670e-01 | 49560 | 640000 | 6167 | 10000 | 1.3 |reached target bit errors\n", - " 1.0 | 4.6153e-02 | 3.9800e-01 | 29538 | 640000 | 3980 | 10000 | 1.3 |reached target bit errors\n", - " 1.5 | 2.3166e-02 | 2.0600e-01 | 14826 | 640000 | 2060 | 10000 | 1.3 |reached target bit errors\n", - " 2.0 | 9.9047e-03 | 9.5000e-02 | 6339 | 640000 | 950 | 10000 | 1.3 |reached target bit errors\n", - " 2.5 | 3.1516e-03 | 3.0900e-02 | 2017 | 640000 | 309 | 10000 | 1.3 |reached target bit errors\n", - " 3.0 | 7.6615e-04 | 9.6333e-03 | 1471 | 1920000 | 289 | 30000 | 3.9 |reached target bit errors\n", - " 3.5 | 1.6047e-04 | 2.3900e-03 | 1027 | 6400000 | 239 | 100000 | 12.9 |reached target bit errors\n", - " 4.0 | 2.8964e-05 | 6.4444e-04 | 1001 | 34560000 | 348 | 540000 | 69.6 |reached target bit errors\n", - " 4.5 | 6.6875e-06 | 2.1500e-04 | 428 | 64000000 | 215 | 1000000 | 128.7 |reached max iter \n" + " 0.0 | 1.0916e-01 | 7.8380e-01 | 69865 | 640000 | 7838 | 10000 | 3.8 |reached target block errors\n", + " 0.5 | 7.6463e-02 | 6.0200e-01 | 48936 | 640000 | 6020 | 10000 | 1.3 |reached target block errors\n", + " 1.0 | 4.6916e-02 | 4.0020e-01 | 30026 | 640000 | 4002 | 10000 | 1.2 |reached target block errors\n", + " 1.5 | 2.4842e-02 | 2.2510e-01 | 15899 | 640000 | 2251 | 10000 | 1.2 |reached target block errors\n", + " 2.0 | 9.7844e-03 | 9.2300e-02 | 12524 | 1280000 | 1846 | 20000 | 2.5 |reached target block errors\n", + " 2.5 | 2.9223e-03 | 3.0625e-02 | 7481 | 2560000 | 1225 | 40000 | 5.1 |reached target block errors\n", + " 3.0 | 8.1080e-04 | 9.6545e-03 | 5708 | 7040000 | 1062 | 110000 | 13.9 |reached target block errors\n", + " 3.5 | 1.7529e-04 | 2.6605e-03 | 4263 | 24320000 | 1011 | 380000 | 47.7 |reached target block errors\n", + " 4.0 | 3.2750e-05 | 6.6900e-04 | 2096 | 64000000 | 669 | 1000000 | 125.4 |reached max iter \n", + " 4.5 | 8.3281e-06 | 2.3100e-04 | 533 | 64000000 | 231 | 1000000 | 125.3 |reached max iter \n" ] }, { "data": { - "image/png": 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", + "image/png": 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\n", 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" ] @@ -471,7 +482,7 @@ "\n", "# run ber simulations for each code we have added to the list\n", "for code in codes_under_test:\n", - " print(\"\\n Running: \" + code[2])\n", + " print(\"\\nRunning: \" + code[2])\n", " \n", " # generate a new model with the given encoder/decoder\n", " model = System_Model(k=k,\n", @@ -485,7 +496,7 @@ " ebno_dbs=ebno_db, # SNR to simulate\n", " legend=code[2], # legend string for plotting\n", " max_mc_iter=100, # run 100 Monte Carlo runs per SNR point\n", - " num_target_bit_errors=1000, # continue with next SNR point after 1000 bit errors\n", + " num_target_block_errors=1000, # continue with next SNR point after 1000 bit errors\n", " batch_size=10000, # batch-size per Monte Carlo run\n", " soft_estimates=False, # the model returns hard-estimates\n", " early_stop=True, # stop simulation if no error has been detected at current SNR point\n", @@ -506,12 +517,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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uXZf13dLrr7/e5v8tAIxYLGbuv/9+xuFw+NXn6tWrGYVC0Wp/CoWC+fzzzxmGYTp07FRWVjJLly5t9zkNDAxkPv3003bH+eabbzIymcyv14lCofDrvrenuLiY61MikTANDQ3t7uP5vtDW+7/RaOS9X2m1Wmb79u1dMu6z9d5773HjSkxM7LbbKS4uZjQaDQOASU1N5d571qxZ4/V/qjf0xOervsDzffRs/y+2x2q18m7rxRdfbLWt53eTBx54oN2+jxw5wus7IyOj3X2effZZrv21117boftCSH9Ak1wRQvodu93OXZZKpV7XP/fcc3jqqad42wYMGIC4uDiv2Tk3btyIqVOnYseOHdDpdO3ednV1NWbNmoWioiIAQFxcHAYOHIiGhgbuV+Vx48Zxp8Tt27cPtbW1ANhfiqOjo3n9JSUl8dbNZjPmz5/PO81HJBIhPT0dQUFBKCgo4DLIamtrccMNN0Cv1+Ohhx5qd+wAcPr0aTz44INcplRSUhJiYmK4WW1bs379elx11VVgGAZKpRJDhw6FXC5HZmYmKisrAbAZPfPmzcPhw4eRk5OD2bNnw2g0QigUYsiQIdDpdCgqKuKyoaxWK6688kqcOHECCQkJrd625wREEokEycnJ0Ol0EIlEqKysRFZWFjehxLp161BWVobt27dDLPbvX+DWrVuxcOFCOJ1OiMViDB06FIGBgbyZVt1uNx599FEkJibisssua7O/p556Cs899xxvm1ar5TJPa2trkZmZCavVCoCtV9eaTz75BDfffDNX0w0AoqKikJCQAKFQiOzsbJSXlwMADhw4gEmTJmHHjh1er6vOWrduHZYtW8Y7DSw8PBwpKSmwWCw4ceIEdz82bNiAqVOnYuvWrYiKiuL1czbHhL9CQkK4x2LHjh1wuVzdUpajK18vfeF4b3puTp8+zZ0eGBUVhaFDh/rsU6FQ+HXb3c3tduO2227DRx99xG0TCARITk5GVFQUrFYrTpw4gfr6egBsVl1BQQH+/PNPyGSydvsvLy/HNddcw00wkpKSgujoaBgMBhw7dox7z/nxxx/xwAMP4N133221L6fTiSVLluCPP/7gjXXIkCEIDQ1FWVkZsrKykJ+fj9mzZ+Ptt99utS+FQoF58+YBAP766y/u+Bs7dqzP01g965b78vnnn2PFihUAALlcjvT0dKjVauTl5eHMmTMA2P+51113HZKSkjB+/Pg2+/PHr7/+yl0ODw/3ytDqKg8++CAvUxZoPgb0ej1OnjwJhmHgdDrx5ptvIi8vD99//32b/zt8zdAdGxuLhIQEGI1GHD9+HBaLBddddx2CgoL8Hmt+fj7mzp3LyxZWKBQYMmQINBoNKioqkJGRAYZhYDAYsGLFChiNRtx9990++/vwww9x//3387YNHDgQcXFxEIvFMBqNOH36NPf/p73ai/7yPDNozJgxnZ7AqaWqqirMnz+fy3YMCwvDhg0bMHLkyC7p/2w1TRYEsNnk3eXOO+/k3s9Xr17t8zNwX9LVn6/OR55nKgDsa78148aNw+HDhwGgzVICTTzPKNLpdEhNTW13n+nTp3OXN27cCIZhqEwLOb/0bnyXEELa1tGMpqqqKl6m18qVK3nXf/31115ZS9nZ2bw2LpeL+fbbb3m/qF9yySWt3qZnxkRAQAADgBk9ejSzZ88eXruGhgamrKys1X39yT674447eOO/9tprmfLycl6bnTt3MoMGDeLaCAQC5q+//mq1T8+srKbxz5kzh8nMzOS1q6mpYWpqahiG8c5CCA4OZsRiMfPSSy/xssBcLhfz2muv8dp+9NFHzPDhwxkAzPLly3lZrQzDMJs2beIyMJqeo7aEhIQw9957L7N9+3bGbrd7XV9TU8M8//zzvEydtn7hb3nfQkJCGIFAwDz66KNMbW0tr+2ePXuYmJgYrm1cXBzjcrla7btlFsmgQYOYX375xSszym63M3/++SdzzTXXMEuXLvXZ186dOxmRSMT1NX/+fObQoUNe7TZu3MgkJCRw7caOHet3JlZb8vLyGLVazfUbERHB/Pjjj7z7bzQamccee4x3TM6ePZtxu92t9tvRY8Jfixcv5j329957b5c8Dt35eukrxzvDMMzTTz/N7ddeVnVndWUG60svvcR7TO69916muLiY18ZmszGrV6/mZWfed999rfbp+diFhIQwAJhFixYxOTk5vHZFRUXMzJkzeRlYLf/PePrXv/7Fe54vvvhipqCggNcmKyuLmTFjBgOA97+pZQZra+NtOvugPZ6ZVyqVipHL5YxMJmNee+01r2zD//3vf7zs4ClTpvh1G21xOp2895XLLrvsrPv05dtvv+U95mPHjmWOHDnCa1NQUMAsXLiQ1+7ZZ59ttc/MzExGKpXynptNmzbx2pSXlzPLly/3eh7RRpab1Wrl/mcCYCIjI5nPP//cKzO6qKiIWbZsGddOIpEw+/fv9+rPbrczwcHBXLvFixczubm5Pm87KyuLeemll5iUlJRW73dHeB7j99xzT4f38ZXBWlhYyKSkpPDeV0+dOtUl4+0q0dHR3PjeeecdhmHY18Jzzz3HjBw5kgkMDGSUSiUTHx/PXHbZZcyXX37JOJ3ODt3Gd999x93Gddddx7uur2awduX/y76kJzNYPf/XAWBOnDjRattDhw4xQqGQa/vJJ5+02rawsJB3pl9bn5s9NTQ08D7z+ZP1Skh/QgFWQkif1tEv3KtWreJ90Pjwww+562pqanhfBts75SgzM5MLQABo9VSzlqfQjhw5st3T0H3t214w6ciRI7wPLbfeemurbSsrK3mn/aalpbXa1vNLOADmwgsvbDfw5Os01KZTHn254YYbuHZNX0BvuummVtt7fhlQKBRMfX19q23bus7TTz/9xPuC6isY29p9e//991vtd//+/bzn5c8///TZrrKykncq6qRJkxij0djuuH29lhwOBy9oevvtt7cZtCwvL+d9Ufnss8/avd32LFmyhOtPq9V6Beg8tQyyf/3116227a4A62+//eb1vMbGxjKPPPIIs2nTJq8vd/7qrtdLXzreGebcCrCeOnWKKwkhEAiYtWvXttl+27ZtjFgsZgAwIpGo1S/DLR+75cuXt3rc1dXV8cqAPPnkkz7b1dbW8gK8F198catBBKvVykyZMoU3hu4MsDY9fuvXr2+1/Q8//MBr31qgzl8nTpzg9dcdpwbbbDZe4GDUqFGt/h9xuVzMokWLuLYSiYQpKiry2faiiy7i2oWGhrYZVLnmmmu83jdaa//UU09xbQYOHMiUlpa2ef9uvvlmrv0FF1zgdf327dt5/bX2v9BTR4N9rUlPT+du+4MPPvBrn7YCrJmZmbz/bWlpaa0+P55aPvZdsbT2nlVdXc1r99133zE//vgjL8jtaxk8eDBz8uRJvx6j2tpa7jUdHBzMVFZW8q7vqwHWrvp/yTD810lXLp0JjvZUgNVgMPDey4YNG9buPq+99hr3mAqFQua2225j9u3bx9TX1zN2u53Jy8tj3nnnHSYsLIz3v64jP0h7lq3qis+bhJxLKMBKCOnTOvKF+9SpU7wPrDKZjPdF5OWXX27zS4cvnplFy5Yt89mmZYDVV8ZIazoSTLrlllu4ttHR0e3WLlu/fj1vXJs3b/bZzvNLuFQq9cry8qXlh+T58+e32X737t289iEhIW0GRm02Gy+4vWPHjnbH5I+pU6dyfe7atctnm5b3zZ/XyrRp07j2rWU4Pfnkk1ybgIAA5syZM52+H56Z2KmpqX598P3mm2+4fSZMmNDp22YYhjlz5gwve/btt99us73b7WYmTJjAtW8r0627AqwMwzDXXXddq1+iBAIBk5qayqxYsYL5z3/+49eXdIbpvtdLXzreGebcCrDedtttXD833nijX/vcfvvt3D6PPvqozzaej11gYGC7P5B4BsZae128++67XBu5XN7u85GRkcELOHR3gLW9x8/tdvNu52y/TLe8/faC452xdu1a3nF/+PDhNtuXl5fz/h/985//9Gpz5swZXmZYe8HD2tparwCbryBMQ0MDr50/NUUbGhq4DGsATFZWVqv3/8orr2y3v67icrl4tbBbe49qqbUA6/79+3lZwGPGjGm3Zn6T1v4PnM3S2ntWdnY2r919993H+/8ZFxfHzJgxgxk7diwvAxpgf7xsmVnti2dQ/T//+Y/X9X01wNpV/y8Z5vwMsK5cuZJ3O/7MW8Aw7OfBlj8Y+lri4uKYN954o8Pjmjt3brv/Twnpr6gGKyHknGaz2VBQUIAff/wR//rXv3j1Ku+++25ERkZy659//jl3+b777vOr/6uvvhqPPPIIALZWVHtGjhyJMWPG+Df4Dvrpp5+4yzfffHO7tcsuvPBCpKam4tSpU9z+s2bNanOfiy66yKvmpT9WrlzZ5vWjRo2CSCTi6hIuW7YMKpWq1fZSqRQjRozgak9mZmZiypQpHR5XS+PHj+f63L9/PyZNmtTuPrfccku7baZMmcLVs2qtVu1XX33FXb7++usRGxvrz5B98nwt33nnnX7Vk12yZAmUSiXMZjP279+P+vp6qNXqTt3+b7/9xj2XKpWq3edfIBDg3nvv5WZr37lzJ/R6vV91jbvSxx9/jNjYWLz66qu8Ws0AwDAMTp06hVOnTuHTTz+FUCjEnDlz8Oyzz2LcuHF+30ZXvV768vHel7ndbqxdu5Zb78h7/erVqwEAW7Zsabf9smXLoNFo2mzj+Z7V2vO8fv167vLChQvbfT7S0tIwbdo0Xm287tTe61kgEGDy5MlcHcC2anX7o7q6mreu1WrPqj9fPI+t6dOnY8SIEW22Dw8Px/Lly/HBBx9w+7eso/3LL79wdUoDAgJw3XXXtdlnYGAgrr76avz73/9us9369eu5mvCjRo3C1KlT22wPAEqlEkuWLMF//vMfAOzr2bN2YlO9awA4ceIE3G43hEJhu/2erdLSUl697pa1uDtiy5YtuOSSS1BXVweArWv6888/IyAgwK/9m+oUd6XW6hkbjUbe+ltvvQUASEhIwH//+1/MmDGDu85kMuGZZ57hagMbjUZcccUVOHr0KO958/TXX39xz/XUqVNx4403nuU96Tld9f8SAIYOHdotz2tfqSve0scff4z//ve/3PqVV17pd73qK664Amlpabj11luxe/dun20CAgJwyy234Nprr+3w2Dz/j7WsEUtIf0cBVkLIOeOvv/7yu1D6hRdeiJdeeolbr6mpQUZGBrfu7yQD0dHRCAwMhMFgQEVFBUpKStr8AtwVQUBfCgoKuMmiAPb++WPhwoVcwGXv3r3ttu/s+Nub2EQqlSI4OBhVVVUAgAkTJrTbZ0REBHe5rYmemjgcDmzZsgX79+/H6dOnYTKZYLFYwDAM18ZzgpCmyWnaM3HixHbbeL4mfI21vLycd9tLly7167Z9YRgGu3bt4tYvuOACv/aTSCRISUnBkSNH4HK5cPToUUyePLlTY/B8LU2bNq3NYHmTBQsWQCAQcM/Hvn37sGDBgk7dfmeJRCI8//zzuOmmm/Dee+/hm2++4SbqacntduOPP/7Axo0b8cQTT+DZZ5/16/2nK14vff1478uOHj3KTfKi0+lanYyrpfT0dO7ykSNH2p2YoyueZ4D9oaeJv/+XZsyY0SMBVqlUitGjR7fbzp/76S+bzeY1hq7meWx05NhqCrBmZGSgrq6OF8zzfB6nTZvWajDM04UXXthugNVzgjt/3+sB/uu5aeKnJqNGjeIunzx5EitXrsTLL7+M8PBwv/vvDL1ez1vvyCRfnn766ScsW7aMe60sXrwY33zzjV+T0zXZsGFDp267M5ommvOk0+mwfft2r8+TGo0Gr7/+OlQqFRfEz87Oxqeffopbb73Vqx+bzYZbbrkFDMNAIpHg/fffP6cmFOqq91GAnbTuwQcf7Iph9Xk7duzAHXfcwa0PHDiQe39qT01NDe68805888033OcxrVaLwYMHQy6Xo6ysDKdOnUJdXR3++c9/4uWXX8Y777yDG264we/xeR7bTZ/7CTlfUICVENKv6HQ6PPzww3jooYd4GRlNswEDgFgs7tBMpJ4fjvV6fZsB1sTExE6Mun2ewTkAfgcNPNu17MOXzo7fny9mnhl4bc1y6qu92WxutZ3L5cLbb7+Nl156yesLXFtaZpW0xjPQ25r2xpqZmclb9ydo0Zri4mLel4x7773XrwxWgJ9J0JHHqiXP15K/r0WNRoP4+Hhu1nt/Xo/dZcCAAXjllVfwyiuv4PTp09i5cyf279+P/fv34/Dhw3A6nVxbhmHw/PPPQyqV4sknn2y37654vfT1470vO3HiBHfZZrNh/vz5He7DbrfDZDK1mT3ZFc+z1WrlffkcNGiQX+NLS0vzq93ZCgkJ8eu9xd/3an8EBgby1puC5V3F6XTy3gc7c2y53W7k5+fzMhY9jzfP4GZbhgwZ0m4bz9fzb7/9huPHj/vVt+cPiC3f6+Pj47FkyRL8+OOPAIBPPvkEX3zxBaZNm4ZZs2Zh6tSpGDduXIcClv5oaGjgrXcmM/DXX3/F888/z51BsWLFCvz3v/+FSCTqkjF2B18/QD777LNtfpZ86qmn8Mknn6CoqAgAsGbNGp8B1ueeew7Z2dkAgIcffhiDBw/uolH3jK54Hz3fHD16FBdffDH3A0NYWBg2bNjgV7Z/bW0tpk+fzr2vREdH45133sEll1zC+85UXFyMJ598Ep988gnq6+tx4403wul04uabb/ZrjJ7HdsvjnpD+jgKshJBzRlBQkNepujKZDFqtFgMGDMCECRMwa9Ysn18KPE87dDqd+OOPPzo1hvaCcv6entZRngE1hULh9xcTz1OwjUZju1lZnR1/R7OMOtreMwvVk9PpxOWXX8475dNfLTOlWtMVY206xRNgT8/s7Kn5gPcptH/++Wen+vE3wOyL5+sxJCTE7/10Oh0XYK2tre307XelpKQkJCUl4frrrwfABnR++OEHvPjii7ygybPPPotrrrkGAwcObLO/rni99PXjvS/zPD7q6urO6r2+rS+sXZFZ2TIbq2VwsTXdcdq8L525j629V/srODiYt97V7xMtH3N/379aljNpOS7PdX/79Ked5+s5KyurUyUYfL3X/+c//0FlZSV3NoTT6cSWLVu48hgKhQIzZ87Etddei8svv7xbApidea14ZuOGhITgzTff7NPBVQBe/++FQiGWL1/e5j5isRjLli3Dq6++CgA4ePAgGhoaeMHa48eP45VXXgHAlhv45z//2cUj735d9VnwfHHq1CnMnTuXO6aDgoKwceNGpKSk+LX/vffeywVXQ0ND8ffffyMuLs6rXUxMDNasWYOQkBC8/vrr3L7z58/3q7zV+f48kfNb9xfcIYSQLjJs2DBs2LCBt/z888/47LPP8Oyzz2LBggWtZlx01S+oTTXWWtNddcw8g4Ed+UDq2dbtdvPqn/nSE3XYutJrr73GC65OnDgRq1evxoEDB1BZWcmVCGhann766V4Zp+fzd7ZZQT31Wm5LV7we/Q1w9zSNRoMbbrgBR48exezZs7ntTqcTH3/8cY+MgY73zusLx0d364/PW5OWP2B4ZnB2hc6WIGjZrmU/njWd/e3Tn/8FXfF69vVaDg4Oxvbt2/Hpp59i4sSJXj/EWCwWrF+/HldddRXS09Nx+PDhsx5Hy0xOX6fOt2fmzJlc+YXq6mrMnz+/y7Ocu1rL4HxCQoJfP5J4lnJwOp1cNmuThx56iHuPf++99/psrVDSNfLz8zF79myufJBarcbvv/+O4cOH+7V/UVERvvzyS2798ccf9xlc9fTcc89xZ5xZLBZ8+OGHft2WxWLhLvtTQoqQ/oQyWAkh5wXPD7MqlQr19fW9OJqO8xx/R8beNAEEwGakdEc9u97icrnw2muvcet33XVXu/XsPB+PnuSZmVZXV9duZmFbWn4xq6qq6vHJojzH0JHH1LOtv9l6vUWpVOK///0vBg4cyAUoPGvfdic63jvP87EbMmRIlwfoulLLY9nfrPK+kv3dHcLDw5GcnIycnBwA/tUS7oiWj7m/718t27V8//Kc8KyzffriOd5XX30VDz30kF99+0MoFOK6667Dddddh+rqamzbtg1//fUXtm7dyjtusrKycMEFF+DgwYNISEjo9O35ygL25xRxT9OmTcM//vEPXHLJJbDZbNi3bx/mzZuHjRs3digjvzOlQ9ozbNgwLqPUU1BQEMLDw1FRUQGg8xnOLY/7pv6Ajt8fz88fK1aswCeffNKh/fui119/HZs2beryfj/99NNur0/cnuLiYsyaNQvFxcUA2P/vv/32W7vzH3jaunUr78eWRYsWtbuPQqHA3Llz8cUXXwAAN9lYezxfq6GhoX6PkZD+gAKshJDzgueHo4aGBq9Trfo6zw8oLpcLZ86cafeXZwDIzc312Ud/cOjQIe70SaVSiX/961/t7uPvxFZdzfNLpNvtRm5uLpKSkjrVV8sP+pWVlT0eYPV8LeXn5/u1D8MwvLbnwusxLi4OaWlpOHnyJACgrKysR26XjvfO8zw+PCcK64sUCgV0Oh1XIzMrK8uvia46c5r4uWTGjBm8AGthYSHi4+O7pG+1Wg2FQsFlWOXn52Pq1Knt7ud5bAHex5dnXfGmMijt8ee9s6dezyEhIVi6dCk3AePp06fx6quvchlrBoMBL7zwAm/W8o6KioqCRCLhsi5LSko6VU94/vz5WLduHS699FLYbDbs2bMH8+fPxx9//OF3+Z3Olg5pS1sZuUOGDOECov6evdGyP38mTjufHT9+vFueV89szN5QUVGB2bNnc+8XMpkMP/30E6ZPn96hflp+/vXnVP+W7crLyzt8W1313k3IuaL/nmNECCEehg8fzjt9qquzYrrbsGHDeJON7Nu3z6/9PO+n5+lm/YHn7O+DBw/mTYTQmt27d3fnkFo1dOhQ3vj8zQLwJTQ0lJdFtGfPnrMaW2d4vpb8fS2ePHmSl415rrwePbOiJBJJj9xmXzzePU9L78v11SZMmMBdrqqqQl5eXi+Opn1jxozhLm/dutWvff766y+/2p0rz1lLnhOpuN1uvPfee13a/8iRI7nLnTm2goKCMGDAgLPu0592nq/nnnyvT0pKwgcffIAVK1Zw2zZu3HhWfQqFQl6tyLOZ6HDBggX4/vvvuSz9v//+GxdeeGGfnVDHMxhWUFDg1/HYMlDf8sfVoKAghISE+LW0DDx7Xtcfa3H3FzU1NZgzZw5OnToFgP0M8u2332Lu3Lkd7qtlSRJ/A8eeE4v5W4bC8//uuTbxGiFniwKshJDzglQqxYwZM7j1Tz/9tPcG0wkKhYI38/xXX33V7j61tbX43//+x637k6VzLmmvvmRLW7du5QVle5JEIuG9/j766KOz6m/evHnc5d54LXu+lk6ePOnXzNZNp5gB7Om1/s603Zuaso2bREVF9cjt9sXj3TPjv7czetoSGxvLy4rr6+/1CxYs4C7/9ttv7WZJZ2Vl+R1gPVees5bGjh3LC0i9+eabOHbsWKf6OnPmjFfGoOex8cMPP/j1v8Tz/WvKlCleJV48+zx+/LhfWcZff/11u2083+v//vvvswpKdsYll1zCXfY8Jb2zPN/XzrZ8x8KFC/Hdd99xP3zt3LkTCxYs8Gumec/a7F21bNu2rdXbu/TSS7nLBoMBBw4caHeMnqe7x8XFef3/2bp1K/R6vV9Ly/JJbV13rvrkk0+65Xlt+WNKTzGZTJg3bx73+UokEuHLL7/069R+X1q+fvx5DQLsBGtNoqOj223f0NDAC7B6HvOEnA8owNqHuFwu/Otf/0JSUhJkMhkSExPx/PPPw+l09vbQCOkX7r//fu7yl19+2eaH4b7oxhtv5C7/+OOP7Wa/PPXUU9wpZlKpFNdcc023jq+nRUZGcpdPnDjRZv1Ch8OBBx54oCeG1aq77rqLu7xnz56zOtXynnvu4bLTtm/fzvvy3xPmzJmDmJgYbv2xxx5rs/2ZM2fw7rvvcuvXX399j8/8/M033/AmovHHt99+i6qqKm7dn9O3u0pfO949y1z0dJCnozzf69944w1kZmb24mjads0113BZQRaLBbfffnurE2zZ7Xbceuutfk/AdS49Zy29/fbb3CnRDocDS5Ys6XA28rZt2zBmzBiv4PINN9zAXS4vL8fbb7/dZj8//PADL4N15cqVXm3mzJnDe7yfeOKJNvvctGmTX4HycePGYdKkSQDY7wl33nnnWU/A1pFsZs86scHBwWd1uwBbQ7WJvwGetixatAjffvstF2Tdvn07Fi5c6FeQtSelp6dj3Lhx3PrLL7/cZvtjx45h/fr13Hpng2pnY9u2bRAIBNzSH+q0nivMZjMuuugi7hgRCoVYs2YNLr/88k732fJH1/be9wBg//792LlzJ7fuT1mCQ4cOce8xoaGhnSoDQsi5jAKsfcjdd9+NRx99FFOmTMGqVaswc+ZMPPnkk7j11lt7e2iE9Atz5szBhRdeCID9onLJJZdg3bp17e6Xn5+Phx56CC+88EJ3D7FN11xzDVfLiGEYLF26FNnZ2T7bvvfee7yA1i233MKrEdcfjBs3jgtMWK1WPPDAAz6/ONbX1+OKK67AkSNHeniEfBdeeCEvQHf77be3Oyt9Tk6Ozy81gwYNwi233MKtr1y5Eu+//367X5zLy8vx3HPP4e677+7Y4FsQiUR49NFHufX//e9/ePTRR31+8S8vL8fFF1/MnbqpVqtx3333ndXtd8aDDz6IlJQUvPnmm37VMvzll194pyorlUpcffXV3TlEnr52vHuWHDhy5Ijfp7P3hhUrVnAzK9fX12P27NnYsWNHu/udOHECN998M9asWdPdQ+QEBQXhySef5NZ//vlnLF261GvG8OzsbFx44YXYvn273zWXPZ+zNWvW+D2JVl8wfPhwXgAgLy8PkyZNwmeffdZugDE/Px/XXnstZs2axfuBpElqaiouu+wybv3xxx/HTz/95LOvPXv28H7sGD58OBYuXOjVTiwW48EHH+TW161bh+eee85nnydOnOjQe8krr7zClQzZuHEjLr30UtTU1LS5j91ux7p16zBhwgSvWp6vvPIK7rjjDu6049bo9XpeINAzONpZnqc2Hzx4sEsmHL3kkkvw1VdfcY/R1q1bsWjRoj6Xte35WK5bt67VIGtxcTEuv/xyuFwuAOwPZl05uRnp22w2GxYvXswFNgUCAT788ENce+21Z9VvdHQ05syZw63/8ssveOqpp1r93JiVlcUL6Mrlcixfvrzd2/H84Wju3LmdntCVkHMVTXLVRxw/fhzvv/8+7rjjDqxatQoAcNNNN0Gr1eKNN97A7bffzqvTRQjpnC+++ALjxo1Dbm4ujEYjli5dirFjx2LJkiUYNmwYtFotzGYzKisrceTIEfz111/cL8iPPPJIr45dqVTi448/xty5c+FyuVBcXIwRI0bgpptuwqxZsxAYGIjCwkJ88cUXvFPLkpOT282WOBcpFArcfPPNeOeddwAAH3/8MbKysnDTTTchKSkJDQ0N2LdvHz766CMUFxdDrVZj4cKFfp2W2V2+/PJLjB49GmVlZXA4HFi5ciVWr16NK6+8EkOGDEFAQABqampw7Ngx/PHHH9i1axcWLVqE66+/3quvt956C0eOHMGePXtgt9tx++2345133sHll1+OUaNGITg4GDabDXq9HseOHcPOnTuxa9cuuN1uXHnllWd9X+644w6sW7cOW7ZsAQD861//wpYtW3DjjTciNTUVVqsVu3btwvvvv89NRgawM/321qQHhYWFeOCBB/CPf/wDU6dOxaRJkzB06FDodDrIZDLU1tbi5MmT+Pnnn73qHb788su8rN3u1teO97S0NIwYMQJHjhwBwzC44IILMGzYMMTGxvLqxX744YddEtzdvn17hyd1OXXqFOLj4yGVSrFu3TqMGzcO1dXVKC0txbRp03DBBRdg4cKFGDRoEAICAlBfX4+ysjIcPnwYmzdv5jJde/qUxocffhhbt27lnseffvoJP//8M9LT06HT6VBeXs6NLSgoCG+88Qauu+46AOBqUPpy1VVX4dVXXwXDMDhy5Aiio6MxatQoBAUFcV9609PT8fzzz3fzPeycW265BQ0NDXjooYfgdrtRUVGBFStW4JlnnsFFF12EcePGISwsDDKZDOXl5cjNzcX//vc/7Nmzp90g7KpVq7Bjxw5UVFRwGbJNkzxFR0dDr9dj/fr1+PTTT7kzyeRyOT777LNWs+/vu+8+fPXVVzh06BAANqt88+bNWLFiBRITE2E0GrF582Z89NFHsFqtuPLKK/HNN9+0+zhMnjwZr7/+Ou69914AbBA+Pj4ey5Ytw/Tp0xEVFQWxWAyDwYCcnBwcOHAAGzZsgMFg8NmfxWLB6tWrsXr1aowcORIXXHABRo4cifDwcCgUCuj1eu4si6YAtVgs5v2o1lmxsbEYO3Ys9u/fD6fTiS1btnRJdubSpUuxdu1aXHXVVXC5XPjzzz+xePFi/PLLL31mcqiZM2fizjvv5L7nPfbYY9iwYQNWrFiBhIQENDQ0YMeOHVi9ejXvx5C33nqLJgrqw1JTU1FYWMjb1vL9JzU11SvQOG3aNJ91jd9++21s3ryZWw8MDMR3332H7777zq/xzJkzh/djj6fXXnsNEydO5DK8n3vuOfz666+47rrrkJ6eDrlcjrKyMmzatAlffPEF78eZf/7zn359BvKcaKxp0jxCzisM6RMef/xxBgCTnZ3N237mzBkGAPPggw/20sgI6V0rVqxgADAAmOnTp3dJnxUVFcyUKVO4fv1dHnnkEZ/9TZ8+nWuzZs2aDo2lM/t+//33jFQq9WvMgwYNYoqKitrsLz4+nmu/detWv8aQn5/Pux1/dPR2PJ/7p59+2meb+vp6Zvjw4e0+DjKZjPnhhx+Yp59+mtu2YsWKLrtva9as8ft1mpeXx6Smpvr9ulu8eHGrfdXX1zOXXnpph1/LV155pV/3qz0mk4mZMWOG37f76quvttvn2RxPbYmLi+vw4wSAkUqlzJtvvtlqv939eukLx3uT/fv3M4GBgW2OIT8/v0N9evI85juztLzt06dPM4MHD+5wP6tXr/Y5vo4+dh15bTQ0NDBLly5tc1zx8fHMgQMHmN9//53bNmbMmDb7feKJJ9rss+Xrb+vWrbzb84c/76tn49dff+U99v4ugYGBzKuvvsq4XC6f/WZkZDDR0dF+9RUQEODXc15WVsYkJye32196ejpjMBg6dOysWbOGkclkHX4cLBYLrx/P58ufRSwWM59++qm/T1e73n77ba7v66+/vt32/nwWaPLVV18xIpGIaz9v3jzGarV20cjPntPpZK666iq/HneBQMC88MILXXK7nv9z2nsvavLNN9/w9jlw4MBZj6O7/1/2hs68N7V1Xzp6fLZc2nsP/v333xmtVtuhPu+//36/HouKigpGKBQyAJigoCDGZrN18NEk5NxHJQL6iAMHDiAkJATJycm87bGxsYiKiuqSOkWEEFZYWBi2bduGzz77rN2JdmQyGWbNmoWPPvqo3ZpqPWXp0qU4cuQIFi5c2GomjVarxRNPPIEDBw70aNZdT1OpVNi+fTtWrFjR6mMxceJE7N69mzfJRG8aOHAgDh8+jJdeeolXs68lsViMOXPm4M4772y1jUqlwg8//IBffvkFEydObPNULJFIhEmTJuGNN97oskktAgICsGnTJrz99tu8mrgtTZo0CTt37uzV0xyPHDmCTz75BJdddplfGZaBgYG46aabcPz48V4padCkLx3vY8aMwYkTJ/DEE09gwoQJCA4O5mWv9jWJiYk4dOgQ3nnnHSQkJLTZVq1W4+KLL8ZXX33lM2O8uymVSnz//ff4/fffcfnllyMmJgZSqRRhYWGYMGEC3njjDRw5cgSjR4/mlbhor1zA888/jy1btuCaa65Bamoq1Gr1OXfK5sKFC5GdnY1///vfGDt2LFd/ujUjR47Em2++iby8PDz00EOttk9LS8OxY8dwzz338CYE8ySRSHDVVVfhxIkTvIkKWxMREYF9+/Zh5cqVXF1QTzKZDDfeeCN2794NrVbbbn+err/+emRmZmLlypWtjrfJgAEDcNddd2H//v1eGZyXXXYZ7rrrLiQmJrbZh0gkwkUXXYSDBw9yGdNd4ZprroFSqQTAZmu3LGFwNpYtW8bLMv7jjz+wZMkSr4nOeotIJMLatWvx2WefISkpqdV2kydPxtatW/H444/34Oj4PMuqzJs3jyYr6ifmz5+P48eP4+abb273fWTGjBnYuHEj3njjDb/6/vrrr7ns3euvv77NMywI6a8EDNOBSuf9nNlsxl9//YWDBw/i0KFDOHjwIDfj9NNPP43/+7//a7ePuro6vP766/jhhx+Qn58PkUiElJQULFu2DHfffXerbzRDhw6FUCjE0aNHva4bN24camtrkZOTc1b3jxDiW3FxMXbv3o3y8nIYjUYoFAqEhoYiJSUFw4cP5+p89kXV1dXYtm0bSkpK0NDQgJCQEKSkpGDy5Mk+v9z1Z6Wlpdi6dSuKi4shFosRFRWFsWPHtvklprcxDINDhw7h+PHjqKqqgtPpRGBgIFJSUjB27FhoNJoO9VdVVYVdu3ahtLQUtbW1kEql3I93w4cP73B/HcEwDPbv38/dF5lMhoiICEydOrVPBvnz8vKQlZWFwsJCmEwmOJ1OBAQEQKfTIT09HYMHD+5zwUM63s9O0+nTlZWVqKurg0qlQnh4OAYNGoShQ4eeM4/h3XffzdXcfeSRR/plCZi2VFdXY+/evaioqEBVVRUYhkFQUBDi4+MxduzYTk3GZLVasX37duTl5aGmpgYajQZxcXGYMWNGp983q6ursXnzZpw5cwYSiQSxsbGYOXNml0wWZbfbsXfvXmRnZ6O6uhoulwsajQbx8fFIT0/3e+bziooKHD16FPn5+aitrYXb7YZGo0FiYiLGjRuHkJCQsx6rL7fddhs++OADAGzppp6sb92XHDx4ECdOnEB5eTmkUmmf+p85cuRIrnb99u3bvSZJIuc+q9WKQ4cOISMjAzU1NXA6ndBqtYiPj+dKsHTE8OHDcezYMQiFQuTk5LT7wyYh/REFWD1s27at1RmC/QmwFhYWYsaMGSgoKADAZiO4XC7uV9ORI0fizz//RFBQkNe+iYmJCAkJ8TlL8LRp05CXl4fi4uKO3SFCCCGEENJvWCwWxMXFQa/XA2DrcfbGDOOEnI3c3FwMGjQITqcTEydOxN9//93bQyIejEYjgoOD4Xa7MXXqVGzfvr23h0T6uB07dnAT4V111VVYu3ZtL4+IkN5BJQJaCAoKwqxZs/Dwww/jq6++avP0TU9OpxMXX3wxCgoKEBkZiU2bNqGhoQFmsxlff/01AgICcPjwYVxzzTU+91cqla2evmK1Wvt0Bh0hhBBCCOk8f/IdGIbBXXfdxQVXw8LCcOGFF3b30AjpcomJiVixYgUAYPfu3di1a1cvj4h4+vvvv7lTvftKeSzSt7366qsAAKFQiKeeeqqXR0NI76EAq4epU6eipqYGmzdvxiuvvIJly5ZBJpP5te+nn36K48ePAwB++OEHzJ49GwD7JnPllVdyp8GsX78ef/75p9f+UVFRKCkp8dl3SUkJoqOjO3OXCCGEEEJIH7dkyRI89dRT3GfJlg4ePIiLLroIH3/8MbftscceO2fKGhDS0jPPPMPVgHz22Wd7eTTEU1P91dGjR2PevHm9PBrS1x06dAi//fYbAOCmm27CoEGDenlEhPQeCrB6aG3yCH98+umnAICZM2di4sSJXtcvW7YMAwcOBAB89tlnXtePHj0a1dXVXnVWi4qKUFpaSoXFCSGEEEL6qfLycjz33HMYNmwYgoKCMG7cOMyZMweTJk1CeHg4xowZg99//51rv2DBAtx77729OGJCzk50dDQee+wxAMDGjRuxbdu23h0Q4bz44otgGIYmWSZ+efzxx8EwDLRaLZ5//vneHg4hvYoCrF3AbDZzp7a0dqqWQCDA/PnzAbAfIlq68sorIRAI8NZbb/G2N60vW7as6wZMCCGEEEL6DM+Z7g0GA/bv34/Nmzdj9+7dqKys5K4TiUS4/fbb8dNPP0EgEPTGUAnpMk888QQYhgHDMJgxY0ZvD4cQ0gkbNmwAwzAwGAwIDQ3t7eEQ0qv61vS456jMzEyuTk16enqr7ZquKy8vR01NDW8W0eHDh+OWW27Be++9h4aGBkydOhW7d+/Gf//7X6xYsQJjx45ttV+bzcar3+p2u1FTU4OQkBD68E0IIYQQ0sd98803WL9+PbZv347jx4+jpKQEdXV1EAgECAoKwoABAzBlyhQsX74cSUlJsFgssFgsvT1sQgghhJB+jWEY1NXVISoqiveDuC8UYO0CpaWl3OW2aqV6XldaWsoLsALAu+++i/j4ePznP//BV199haioKDzzzDPc6TOteemll/DMM890cvSEEEIIIaSvqqioQEVFBfbu3YvXX3+9t4dDCCGEEHLeKSoqQkxMTJttKMDaBerq6rjLSqWy1Xae13nu00QsFuOxxx5rN6Da0mOPPYYHHniAWzcajYiLi0N+fj4CAgI61Ne5wOFwYOvWrZg5cyZN7kBIP0bHOiH9Hx3nhJwf6FgnpP+j45z0R3V1dRg4cKBfsTUKsPYDMpkMMpnMa3twcDA0Gk0vjKh7ORwOxDtPI/RoBkRSJSCWAWI5IJGzf5vWxXJAGQJEjeB3YDUBQhF7vbDzE5sRQrqXw+GAUqlESEgIfUgjpJ+i45yQ8wMd64T0f3Sck/6o6bXsT/lNCrB2Ac9IttlsbrWd53X9MbO0J+nqT0GU+2v7DWMnACv/4G/7bDFQeoi9LBQDYoXvIO2YlcDIq5v3szcAG//ZHLxtaidp2t+jn/iJgFzL39dqau5fJAPaqd9BCCGEEEIIIYQQQvo+CrB2gaioKO5ySUkJhg0b5rNdSUmJz31Ixwnddv8aSuTe25zW5stuJ2CvY5eW0hbx1231wIGP/bvdW3cAkR6vg8xfgR9v5bcReQZ1Gy8HRAArWgSO9/8XKD3cdkBXogCCBwLRo/n7VucCIgm/rUgC0ORnhBBCCCGEEEIIIV2CAqxdIC0tDUKhEG63GydOnMCFF17os92JEycAABEREV4TXJGOyQ+diwHz7oAYTsBpAxwW9q+z6a8VcFiBoHjvnWPGAqpQfnuHx35Ni0TB388zMNsecYvArsPHTL8uG7vYjM3b7A0+7uxfQMbP7d/miGu8A6wfzuT3DwACoUcphcbA64X/AlLmNbepzgX+fNZH6QWPQG1TsDd9KXu5SV05YK7hl2po6oNKMhBCCCGEEEIIIaSfoQBrF1AqlZg8eTJ27NiBDRs24OGHH/ZqwzAM/viDPVV97ty5PTIuh8MBh8PRI7fVkxwOB8yyUNijxoHxp7ZLy8fgQj9m4GUYgHHz95WHACu3QNAykOu0Ak4bBA4r4GIvu2WBvH0F6kgIUxaw+7jY4K/A4zJcjX1JVXC2GK/IboE/xQRcQgncLfYVO63wylVl3IDDzC6oBQA4bRYwnuM1lECc8ZMftwo4ki8EmOYRCvd+CNFO348xIxRzgVomZixcl3/Ou1648QkIjGea24hl/MCuLACMTAsmcjgQNtij48bniwK4/UrT+1d/fB8jhLDoOCfk/EDHOiH9Hx3npD/qyOuZAqxdZMWKFdixYwe2bt2KvXv3Yvz48bzrv/vuO+Tl5QEArrvuum4Zw6pVq7Bq1Sq4XC4AwMaNG6FUKrvltvqCTZs29fYQAIgAqBoXD1t3ezdVLfOvy/Xreaty2QJIBs2AiLFD6HZA5HZAyDggctt5l02mcOg992UYjNSO8WjjYPdnmv4273/wyElU5TWHYkNNxzHJv9Hi901bwQiaA5uDSzKQ3EpbgdsJ2J2AvQH60gL83eK+zsj6HVrLmXZvMzPyUmRHXMKti11mXHTsNjiECjhESt7iFCnhEDevFwdNgk3SXB9X5LZB6LbDKVLy7gfpO/rGsU4I6U50nBNyfqBjnZD+j45z0p+0Nc9SSwKGYZhuHMs5p7a2lgtQAsCoUaNQVFSEhx9+GP/4xz+47XK5HGq1mlt3Op0YNWoUjh8/jujoaHz66aeYNWsW3G43fvjhB9x0000wmUy48MILsb5FUKmrmUwmaLVa6PV6aDSabr2t3uBwOPDhuk1AWApiglWI0soRFShHeIAMYhFNHNUlnDbArOdKJ7Sateu0wj36Bt6ugsxfIMzb0tiWv5+gcT84rWwG6+L3efuK358AQfXpdofnmvsS3GNvbt5gLILk3ZF+3TXHzdt52a+CY99A/OudAABGogLkGkCuBSPTALLGy3ItoI6Ae8oD/M4MhYDLwU5oJtcCIqlfYyD+cTgc2LRpE+bMmUMzkRLST9FxTsj5gY51Qvo/Os5Jf2QymaDT6WA0GtuNr1EGawsjR45EYWGh1/ZXX30Vr776Kre+YsUKfPLJJ9y6WCzGL7/8gpkzZ6KgoACzZ8+GUqmE2+2G1Wrl+v7yyy+7/T40kUgk/faNLcsgwIYTebxtQgEQoZEjKlCB6CAFogIVSIvUYNFwmlCswyQSQKFuvx3YHF6eYUvZpR0CwLv0wc1b2JIJnrVwPddtdYDVAFHcRIg8X9tCARA9BrCZAKuRXVqpmStRh7D3r4mjvnlMjgbA0QDUlXmXVgiMh2jmI/xtfz4NZP3WvC5WNAZbNc1BV7kWSJ4LDG+RwZy7BZCq+e3EcpqAzIf+/F5GCGHRcU7I+YGOdUL6PzrOSX/SkdcyBVi70IABA3Ds2DG89tprWLduHfLz8yGRSDBkyBBcddVVuPvuuyGVUoZbV6i1eQeh3AxQarSi1GjFgUK2tujkpBCvAOt9Xx9GndXJC8RGB8oRHahEaIAMIiEFuHpNU6Cxo4IHAjf/yd/msDYGXJuCrgb2ryqU304TxQZAmwKzTYujxakAvsZlbTGBmNMC1FuA+nL+9oBIfoDV7Qa+WMrWjfUkkvIDrjINMOspIHpUcxtTKVCw07udXAtIVRSgJYQQQgghhBBCehgFWFsoKCg4q/0DAgLwzDPP4JlnnumaARGfZkW7cc2skaios6PUYEWJwYxSgxWlBguqG+xcuyitwmvfnaf10NfbvbYDgFgoQISWzYK9Y0YiZqSGcdc5XW7YXW4opXTYnBMkcnZRh7XdbvAidmnJ5WgMzhrYQKqvwGXSbEAT7R2ctRoBe11zO1mLUwns9d7BVQBw2YGGKnZpMq3FpHmlR4B1N8Mngag56KoIYjOCPcd9+k+g+rR3YLZpkaoBIZXZIIQQQgghhBBCOoIiReScFK4A5g8J95mubbG7UGq0oNRgQZCSnzFsc7pgsjhb7dfpZlBca0FxrQU3TBrAuy6zrA4Xv7sTQUoJogKbMl/ZhV2XIzpQAZ1aBiFlwZ77RBJAFcIurZlyX+vXuZxsBq3NxAYuPQmEwPRHfQdmmxabCQDjnTnbMmvWE+MCLDWNS613UPjEOuDIF63vLxACsgBgyBLg4rf51237FxsU9gzIcoumOWArpInCCCGEEEIIIYScXyjASvodhVSExFA1EkO9a4jKxCJkPTcf+nobig1sEJZdrCiubbxstMBgdiAqkJ/9WmJgTxmvNTtQa3bgZKnJ5+1LxUIce3ou5JLmQNOJEiNqzXY2EKtVQCGlIFS/JxIDymB2aUmmBmY+1vb+bjebBStR8bdHjwLm/8sjEOsrQGsCFIHefVoNbd8m42b3dzm8r9vzXvv7A8ClHwHDrmhery0Etr7ouzYtL4s2kM26pQxaQgghhBBCCCHnGAqw9mMOhwMOh49AyTnO4XCgrPo0vl33DiJjEhAfm4ro8ASIOpA5F6QQIUihxtBI3xM5NdickImFvMdPIgTGxAeizGhFuckGl5vxua9aJoIIbjgczaeAf7IrH98fKuHWg1USRGkViNTKER3IliSI1MqRHKZGYqjKV7fkfCRSssWF3R7HcWACMDqh/X0ZBmhx/Asm3AWkXgRBU11amxECq7Fx8jA2OCuwGeFWR8DtuS/DQGwzeU/85YNTKAPjsa+gphDiY1/7sSfgeDCPDcI2ch/6AuPyPgfWb4UrMAZMQCQQEMn9hZSOFULOdU3/Z/vj5xVCSDM61gnp/+g4J/1RR17PAoZhfEeJyDln1apVWLVqFVwuF7Kzs7F27VoolcreHla3OHhkHbQZ1dy6U+iGWcXArhTBrZJBqFZBptZCGxiJhMAhXX77bgYw2YEaG1BrF6DWxk68VWMD5CJgRQq/vuaqDCGyje1n5o0PdWN5En/fH/KFUIoZBMuAIBkQJGUQJAPElOhHegrjRpA5FxKnGRIXfxG3WD8ecy0MquYAcLjxMCbkvdn+TUCAX0asYcsUNBpSvBZJVRta3ccuUsEqCUJVwGCciLmGd53CVgWnSAmHSEkTfxFCCCGEEEII6TCz2Yzly5fDaDRCo9G02ZYCrP2QyWSCVquFXq9v9wVwLnI4HHjx6esQdMbabtv6cDEef/073ra3vngUdjgRHB6FiKiBiNLFI1IdiVBFKMTC7knq/vVYGbLK69iJuIzsZFyVdTa0TIK9e2YC7rkgiVu3OVxIf/ZPn32GqqWIDJQjSqtAdKAcV42NRXxI/wyok3OYwwyYStisWZuJlymLxm2CxrIErqUf83YV/HI3xMe/avcm3CkL4Lr8M9428erxENTkgpEom7NeNVGN2a9RzdmwwYm8rFlCSM9yOBzYtGkT5syZ47OuOiGkf6BjnZD+j45z0h+ZTCbodDq/AqxUIqAfk0gk/faNLThxCKTJVjRU62GvNgJGG2T1bojc/Ew1cZDa6zGwb8uC3CqAGbnIww5kid2oUzpRr3TCpZFCEqyBWqfDgklXYlLidG6/pt8iBJ3Ihrt0dJzXNofLjQqTFSW1lsZJuawYPzCYN94So73VPqvq7aiqt+NYMVsLdtGIGN6+W7Iq8OL6rMbJuBoDsUHNk3OFa+SQUhos6W4SLaDUtt8OQMtXo+OiN/CHezxmjU2D2FwBmMoAUwlgKgXqmi8LtdEQeh7nDMNeD0DgMAM1uRDU5Pq+0UXvAqOubV43lQJ/vwtoogBNJKCJZi+rIwCx1HcfhJCz1p8/sxBCmtGxTkj/R8c56U868lqmACs5JyVGjsKCBQt4L3bG7UZFxRkUnMlCaXEuqiuKkZKQzNvPYq2H3MoPkEqdQoSYpAgxSYFyADADOIMi9VHAI8B6NG8f3vj8EYiDA6DShSA4MgYRwdGIVEUiUhWJCFUEIlQRkIlkft0HiUiImCAlYoJazzqNClRg/T1TUWqwoKRxQq4Sj8uVdTY05aC3nJSrQG/G6cp6nK6s99m3QACEB8gxJEqD/14/lnddudEKhVQEjVzcqYAyIV1CJIFVGgwmejTQ2j82hgGcNv42lwMYsqQ5GGsqBey+jwNoIvnr+hxgzyrfbVVhjYHXxmXuC4BE3ny9202TdBFCCCGEEELIeYgCrKTfEAiFiIgcgIjIAcB4323EQilm3HUXSopzUV1ejLrKSthrjIDJCgG/9CkiovkTCRVkH8fwTDUABoAegB41koM4o3SizmNBoAJf3vo/KCTNAc88Yx4a7A2IVEciWB4MocC/IIxULMTgKA0GR/lORbc72SzY4loLQlT87DqLwwW5RAirw+1zX4YByk1WhAZ4B4Qf+u4odp7WQy0TIzpQgajGibiiAhWIacyCjQpUIDxABrGIAkqkFwkE/CAnwGaaXvIef5vVxMt65bJhQ/g/wjRlvvrUUMkuZUcAkRRY8Br/+o1PAEe+BAKi+IFYTRR/myKI6sISQgghhBBCSD9CAVZyXpFIpRg9dT5Gt9judrlQV10FQ0U59GVFKCvJw/DUCbw21upar/7kDhHkRhF0xuYgpV3ihuIufjbpVz++hWP5B1CvdMKiBlQ6HUIDI7jM16a/AzQDEBMQ4/f9kYqFiA1WIjbYOwv2zplJuGNGImrNDpQaLCiuZbNeSw1sSYKSWgtKDFZEt8h8BYBSgwUAUG9z4lRFHU5V1Pm8/XtmJeOBOSncus3pwrcHihET2BSElSNATqeHkD5ArmGX0NS22yXPBW74vTEI65EB27TUlwOMGwiI9A6Smkq5GrOoymz9NoYsAS7/hL/tyFpAFtAcjFWHAUJRp+4qIYQQQgghhJCeRQFWQgAIRSJowyKgDYtA/NARPttcOPc6lCWMh6G8DFVlZ6AvK0ZdVSUcBn7w0a31zgh1HS/BuOJg3jaLtAx1yjPIVDqxrzH7NT19Ip5fzJ9x/a2Db0EtVXOlCCJVkQhV+jchl0AgQLBKimCVFOnRvutgulvOtAVgUlIIIgPlbH1YgxV2l+8s2JgWwdkygxVP/nSCty1AzmbBRgc2Z75GBykwa1AYVDJ6CyJ9jDIYiJ/U+vUuJ5vFajV5X6fSAUED2UCry+Z9fRN1OH+dYYDfHgCcluZtAhEbxNVENma+NtaCTbsYCBrQobtECCGEEEIIIaR7UXSDnJPcTsBhc/Vo8WxtWDi0YeFe250OB0xVlTBWlMFQWQ6p3DsjVGuRwQl+wEVhF0FhFyHM0LxNqHLy2ljtFhz/dp1HCQIH6hVOMGIhQhWhzfVf1RFYmrwU8Zr4Dt8vodD7VOXnLxnKXXa7GVQ32L3qwJYaLEgOV/P2KzFYWnaFOqsTWeV1yCrnB6L3PTGLF2D9/XgZNmVU8IKw0Y2lCZRSeqsifYRI3Hyqf0sXvc7+ZRjAXAPUlXpnwNaVAhFD+ftZavnBVQBgXICpmF08hafzA6yFu4Hf/9EcgPWcmEsTzQZpZfzjlBBCCCGEEEJI16KoRT/mcDjgcDh6exhdzuFwoKFYgjUP/Q2FRgJNiAIBOjk0IXJodOwSoJNDGSCFwEfwsDsEhIYhIDQMMRjOjdHTpXc/AUNFGYyV5TBVVsBYUQ5DZTnMBn7ZgQGxg3n75hacxOAC7/qrDfLGgKuiCCWqfGQpHJgUNB5Riuagz67SXXjj0Bvs5FvKxqVxIq5wZTjCleF+T8gVKBciMEKFwREqr+s8xxsXJMMLiwejxGBFmdGCUqMVJQYryo1WOD0yZaViIbRSIW/f/fnVWHe4xPftKySICpRjQIgS4wYE4erxcX6Nm5zbml4f5+T7mFQDhGiAkEG+r/e8T4wAgovfhcBUCtSVQVBXyl6uL4egoYq/mzKMt69Afxri8mNA+bFWh8LINGCCBsK18k/+FfpswGVnSxJQXVjSS87p45wQ4jc61gnp/+g4J/1RR17PAoZhvM8PJuekVatWYdWqVXC5XMjOzsbatWuhVLY+Q/25zJAhQ32htO1GQgaKMCdCRlp5m50WAURSBoI+UN7Q7XTC2VAHR50Jjvo6KKNiIdU0n8pvKi1E5baNfvUVsngRglTNGbYHCzehqPQobwKuBoUTbo85qdQCNcJF4bhBfQOvr1p3LcQQQyVQ+T0hV1vcDFDnAGptQI1NAIsLmBzOf+v5+JQQR2vav60hQW7cMohfsuDXM0JIhQwiFECkkoFODvRQbJ2QbiV0OyB3GCB31EDhqEWZdhTcwub3voTKPzCk5CsI4buMR5N6aRj+HMKflGts/r8RZdgPAHAJJLBIgmCVBrN/JcGwSoJgkQbBJI9Fgzyi6+8cIYQQQgghhPRhZrMZy5cvh9FohEbje/LxJhRg7YdMJhO0Wi30en27L4BzkcPhwLoPt0FmC0VdtRUWU+u/KAwcocOclWm8bd88dwDGSguUWik0IfLm7NdQBQIas2AVARII+kA2l81shv5MPowV5TBWVsBYyf41VZXDYmquASkQi3H7f9ZCLG4umfD+Ow/Bvi+X158bDBoULrbUQGPQVR4Vhg9Wfstrd9fWu/B32d+QCCUIV4Y3Z78qmzNgm8oTKMTeJRE6o8HmRKnRijKjFaUGa+NkXFZ2MVhQbrLB5WZwy9QBeHhu88RabjeDEc//CYujOcAkFQuRoFMhOUyF5DA1t8QEKSCiyOs5w+FwYNOmTZgzZ06PlgM557hdQEMlBHVlgInNgG3KhIWpFIK6MjaD9Sr+cS5aMw/C0oPtdu8adyvcc15o3sAwEP14Exh1OBAQCaZxYi4mIJItSSD2LzOeEICOc0LOF3SsE9L/0XFO+iOTyQSdTudXgJVKBPRjEomk376xBQx0YMGCEZBIJHDYXDBVW2DSW2GqssCot8Ckt8BUZUFIlJr3GLjdDOpq2IxWs9EOs9GO8jzvyWrEUiFmrRiMpNFh3DaHzYX6WisCQuQQS3om/VWi1UI9dAQw1Ps6m9nMBlwrymGpr4NCwc9WjmNCcRr8AKsQAgRYxAiwiIFqdpvZ5v06Ue4sxShHIOpUTtQp9MhSluGgwgWmRWzy5qE3455R93DrDrcDn538jA2+qtkArE6h82tCrkCJBIFqBQZH+77e4XKjsLoBcomIN94z1WZecBUA7E63z7qvH18/BhcMas7yNVocMFkciA5U+KxFS/qG/vxe1jUkgCwOCG69dIYAgFd++NClQHgaYCprrhFrM3rtKwqMhcjz8W+oBjJ/bn04Sl1zLdi5zwO65Obr7Ga2vqwswK97Rs4fdJwTcn6gY52Q/o+Oc9KfdOS1TAFWcm7ySLyWyEQIiVIjJKr9iVycdheSRoXBpLfAqLfCYrK30s4NuZp/IJXnG/HLW0cAAaAOlEGjUzTWfFVAG6poXFf0WParTKlE2IAEhA1I8Hn9BTfchpHzL2Zrv1aUw1BRDmNlOQwVZbA1NHDtpgyew9uPYRhE5woQ49TytrsEDBoUTt6EW7oUfk3WSnMl3jr0Fm+bSCBCqJKdkCtCFcH9XTBwAbQy/m20RSISIinMOygTFSjHxvunIbuiDtkV9cgur0N2ZR0Kq81wufkJ+skt9t+UUYGHvjsKpVSEpDA1ksMCkBKuRkp4AJLD1YgOVPSJTGZCusXEO7232eqBujLAVNIYeC0B4ifz25h810rmmPXsUn6cDbB6OrkO+PlOQBrQPFmY5xIQBWhjgJBEQNI12fGEEEIIIYQQ0t0owErOSZr9+5H/xpuQJyVCmpgEWWIiZEmJkCYmQhwU1Op+UrkYc24cwq3brU7UVVthrGrOejXqrTDpLdCG8r/cm6oaZ/lmgPpaG+prbSjN8b4NuVqCG1+dwgvM1ZY3QCAQICBYDpHk7Gua+iMgRIeAEB3i0od7XWetr+eCrYHhkbzrGmprIHB613MUMQJozBJozM2B54hZ/ABpTtYhTD0SgjqlE/VKJ0yNf8vd5ShvKOe1vSD2Al6A9ff83/Hz6Z+5ibiaShBEqiIRrgqHVOS75q5YJERKeABSwvnBU5vThbyqBmRX1CGnoh75+gZEB/Kf05wKNsPVbHfhWLERx4r52XsqqQhJ4QGYkBCMxy7kl5ogpF+SqQFZMj/rtKWwwcB9xxuzXks8MmBL2L91ZezidrIlAzyZStm/9jpAf4pdfAmIAh7M5G+ryQPkgYAyuNN3jxBCCCGEEEK6AwVYyTlJVlEJV2UlGior0fD3bt51opAQyBITIU1MgHLUaGgvXthqP1K5GCHRaoREt5/9GhAsR/LYcDYQq7fAUue79qtC7Z3BuufnPOQdrmKzX4Nk0OqaM1657NdQOeSqnsl+lavVkKuTEJ6Q5HWdMjAQN771AZf1aqgsh9EjC9Zha540bGTKJN6+ilo3Eku9H0uXkEGd0oE6hRN1KifqVW7oFDpem8yaTOwq3dXqmEPkIUgMTMS4iHG4dfit7d5HmViEtEgN0iJbr5OSEh6A2WnhyKmsw5kaM1pWpG6wu3C0yACtwvu0gMfWHQfDMEgOb856DQuQUcYr6f9EYiAwjl1a43YBDVVswNaTJgoYMLW5JIHT4nt/nfd7E366AzizG1CFAroUdglNbb6sjQHo+COEEEIIIYT0AgqwknMSIxBAqNXCbfSuF+iqroa5uhrmffvgLK/wCrBWrVoFoUoFWWISZEmJEEdE+BUUixsSgrghIdy63eJka79WeWTA6i1Qh8i99jV6Zr/W2FBfY0NJtsGr3cg5cZi0tDmwwDAMijJroNEp2OxXcfdnvwqFIgRFRiMo0rsYKsMwsJiMXNkBjS6Md72zxrueLQCI3AIE1ksRWC8FqgBpiBYiIb+Obf22E5hSEsIrQVCndMIqdQMCoNpajeryaogE3vVvvz31LTQyDYaEDEGMOsbvIOfS0TFYOjoGAGCxu5BbVc+VGsipYEsNFNVYkBLGDxIxDIPfjpaizubkbdfIxY3lBZqDrkNjtNDIqQYROc8IRUBAhPf2kdewC8CWerEaGoOtZc0ZsLUFQPgQ732rGrNdG6rYpbDFDzISFZt5O+1hIK31H9YIIYQQQgghpKtRgJWck/QLL8LYVe9CWFcH2+lc2PNyYTudC1tuLmy5p+Gq0gMAZEmJvP0YtxvVH/0HjLU5C1OoVEKa1FhmIDEB0sREyJKSIImOhkDYekBTqhBDFxMAXUz7k7UkjQpFUIQSpip2Mi5rg+/sV3Uwf/ZtS50Dv75zFACbmKUObqz5qpNDE8rPgJUpxd2ePSkQCKDUBkKpDURUivcp8+OXXIlBk2dw5QfYrNcyGCsrYKwoh9PB1ryNjPauG5ug10BV4p396pYIYVUDRrkd5ep6DA6N5V/PuPHmwTdR76gHAGikGqSFpGFIyBAMDhmMISFDEK2ObvexUUhFSI/WIj2aX/bAbHfC3qJkQmWdzSu4CgAmqxMHCmtxoLCW2/bhtaMxd0hzoElfb0N2RR1SwwMQoqbZ1sl5TCAAFEHs4iug6slpB4YsAfTZbKC1odK7jaMBKDvClibwVJkJfHud76zXlhm2hBBCCCGEENIJFGAl5yyBQABxSAjEISFQjR/Hu85lNMKWmwdxML8eq6O0lBdcBQC32QzrsWOwHjvG2x77wftQT5/e3KfBAGdtLaSxsRCIO3bojFkwkLduMztgaqz1amys/WrSW7wm6jLpm0+fZRigrtqKumorSnyULbz6mQkIDFdy67XlDaivtUGjU0AdLINI1P3Zr2KpFCExsQiJifW6jnG70WCohaGiDEKRdxaqsbLCZ59ChxvKWkAJMSIRiPTB/Ky4QkMBXPVmoDFWabKbsLdsL/aW7eXaaGVapAWn4aExDyE1OLVD90kpFUPZovxruEaOE8/Mw+nKxozX8jpkV7JZr2VG/uurZW3YnTl63PfNEQBAiEqKZG5SrQCkhLGXg1S+680Sct4SS4GFbzSvW2oBfQ4bbNWfar5sKGQDqJ6qstjArD7bu19NDJv12hR0HX0D0MYPa4QQQgghhBDiCwVYSb8k0mqhHDXSa7s4LAwDvv2mMdv1NOync2HLy4OjuBgtC3DKkvg1AOu2bEXZ449DIJFAOmAApEmJXJkBWWIipPHxEEj9C4zJlBKExkkQGtd29qtcLcGoeXEwVlm5EgQ2s3fmpEAABLQoTZC9rwIH1hew1wsFCAiWNdZ6VXA1YNnsVzlkyu4/hV0gFEIdHAJ1cIjP629+978wVlawNV/Ly2CorGBrv1aWw1hZCbeLvd/J6WN5+4lr7bjyz1gIgpQwhQqQp6pGXkA1jCon0Ji0arQZsadsDxRi/iRXBysOYlfJLi7TNULlX7kIAFDLxBgRG4gRsYG87SarAzmNJQZOV9YjNljJuz67cWItAKhusKM6rwZ78mp4bXRqGSYkBOPd5aP8Ggsh5x1FEBA7jl08OayAqMX7mbkaEMkAl827H1Mxu+RtBZQhwNiV/OuPfgPUlwO6VCA0BQiMZ8sfEEIIIYQQQogHCrD2Yw6HAw6H71PRz2VN96lT900ggDgtDeK0NKg8NrstFjgKCmDPzYM9LxeOwjNgdDrebVhy2OwnxuGALScHtpwc1Hn2LRJBEhsLxfhxCPvnPzs+Nh9UQRKMWRjP29aU/VpXbW3MgrXCYXPBzbjgdri4dobKBu4y42a4tsiq5fUXnRqIi+4ayttWcFQPiVwMjU4OVaAMQlH3TxwjEEsQGBWDwKgYr+vcbhdMVZUoP50N3cAE3vNSlZ0DAGBqzQioBYZDjuGIhkilgDtSjapgOzKVpagMsCJCHsHbd9uZbfgk4xNuPVAWiMHBg5EWnIa04DQMDh6McGV4h0ovKETAsCg1hjVmI7tdTribnxaMidfiuglxOF1Zj5zKelTV27360NfbUFNv83qN3/XVEdTZnEgOU3ssKgT00xqvZ3Wsk/OUCHC52aXJiBXAsGsAYxEE+mwIqrMh0OcA1TnsutUAAHCHJMPV4rUmOvQphB61XhmxHAhOBKNLBhOSDEaXAiYkBQhJBMTe9bdJ++g4J+T8QMc6If0fHeekP+rI61nAMC3nzSbnqlWrVmHVqlVwuVzIzs7G2rVroVQq29+R+CXg8BGoT56EtLISUr0eApfLZ7uG1FSU3HgDb1vkF19C4HTCHhYGe3gYbGFhsIeFgZF1Xw1OS4UYtloRXGYBnBYhnGYhGKd3oFAVa0dQOj+zq3SzCm5H42myAgYiBQOx0g2xwg2x0g2RsnFd5e71ZK66wlwYs0/CWl0FuN2tthMplRiweDkvWPpx/cfIc+a12b9KoMIo6SjMU8zrsjF7anAA5RagzCxAuUWAcjNQZhFgVAiDpQOb7w/DAI/vF8Hs8n4OtVIGkQoGEUogQsFgUCCDICrvSkj7GAYypwlqaykAAaoDBvGunnf8bsid3pMptpQVcQlORV7KrQsYFwIb8lAvj4JDrGpjT0IIIYQQQkhfZTabsXz5chiNRmg0mjbbUoC1HzKZTNBqtdDr9e2+AM5FDocDmzZtwpw5cyCR9E7mHuNwwFFcDHteHuy5uY2Zr3lw5OdDe+WV0D38UHNbhkHehIlgzGavfsSRkZAmsBNrSRMToJg0CZIIHzNvd8WYGQY2s5PNftVbYapmyw5EpwYiaXQY185uceKTf+z2q885N6Vh4HAdt15fa0NxVi00Ojk0IXIoA2UQCrs/+xUAnA4HqvJzUZqdidJTmSjLyYS1vp67PmH0eCy8/1HePr+ufhVGZx1qdQxy1VU4Zs+GwWbw6vvq1Kvx4OgHuXWGYfDYrscQr4nnMl5DFaFdOsmYw+WGxKNursHswNy3d6LW3P4vaO9cOQwXpje/jipMVuw4XY3kMDWSQlVQyc6Nkxf6wrFOzm+C0sPNma6Nf1GbD0GLibScl3wIZkhzgBX6HEg+mAgAYFRhjRmv7MRaTZmvCIhk67uc5+g4J+T8QMc6If0fHeekPzKZTNDpdH4FWM+Nb9mkUyQSSb9+Y+vV+yeRQJqSAlVKCm8z43KBsdkg9BiXU69vtRtnWRmcZWUw72JPQY15bxWUsc0TRDlKS1H/11+QJiZClpQEcXDwWQ1bKpUiIFAJJLXehnEJMP2qFBirLDDprdwkXA6bd8ZucISa9xxUF9Vi+9ocbl0oEiAgRN5Y67Wx7mtjHVhdTNfO3i2RSBA3ZCjihrDlDhi3GzWlxSjJykDJqQzEDh7KG6vT4UDRvgNwOthT9BMADA9JRHBSApyRKlQEWZEhKERGbSaGhvH3Lasvw8YzG3m3r1PouFquTX9DlaFncX/466FaCQ49OQf6ejtyKurYybUaJ9bKrqiH0dIceB0cHcgb7+HiKjz240luPTpQgZTGybWalqQwNRTSvllbsr+/l5E+LH4cu3hyOYCafHZyrSp2gi1xzCj+QWvI5S4KGiohaKgEPEoNAACkAewEWyt+AWRt1+M+H9BxTsj5gY51Qvo/Os5Jf9KR1zIFWAnpQgKRCIIWZRnEOh1SD+yHs7wcttxc/gRbublw1zVXcpUlJvL2NR88iPJnnuXWRUFB7IRaHhNsSRMTIQ7tuuxJqVyM9On8OqgMw8Ba72CDrXoLTFVs4FWj408aZaziZ+m6XQyMlRYYKy287QqNFDe+MoW3Le9wFWwWBxeMVWllEJxF9qtAKERITBxCYuIwbPZ8r+trSorgcvKz0Oqq9airbg6IpyuUmJsyE+MmD+G1y6jJ8OpPb9Fje/F2bC/ezm0LVYRi7UVrEaHqmqxkgUCA0AAZQgNkmJTUnDnMMAyq6mzIrqhHdkUd4kP4pyTnVPCqBaPEYEGJwYKtp6o8+gaGxQTi5zsn89q63UyPZSETck4QSdgJr0JTgLSLfbfRRAFjbgSqstlAbEOVdxt7HVCTC0hb/Ni08Z9A9ka2f10qoGu8LV0KIKVyA4QQQgghhPRFFGAlpAcIhEJIoqIgiYqCeupUbjvDMHBWVcHeGHiVxPADm7bTubx1V20tzAcOwHzgAG+7NDERif/7jd/WZIIwIKBLAq8CgQCKACkUAVJEDNS22m7AUB2kcnFj9iu7GPVWOFtkv2pbBGYB4NjWIpRkG7h1oVgATYjCI/tVjsBwJUJjA6AKPPsCo2EDEnDXmq9RmnMKpacyUJKVgdKcLDhtzfVo7RYzCo4ewtzb7uHtm+4agI8GvoSaYCdO2fKRUZ2BjOoMmOwmXrt6Rz1CFfws1i8yvsDe8r28bFedQoezIRAIEKaRI0wjx5Rk777mDomARiFhs14r6nG6sh71Nn5wmWEAmVjote+1H+9FSa0FyeEBXNZrclgAEkJVkEv6ZsYrIb0uejS7NDHXAPocj6zXbHZRR3iXCSg/wbbTnwLwK/86TUxz4DV5NpA0u9vvCiGEEEIIIaR9FGAlpBcJBAJIwsIgCQuDauJEr+s1Fy2AJDKCl/XqrPLOhJKEh3ltK7rtdtiystjyAo01XpuyXiXR0RCIuj44FhKtRkg0PxuLYRhY6hxssLUx8KrUSL32NVbxs1zdTgaGCjMMFfys2DEXDcD4ixO4dZfLjdMHKhEUoURguBJSuf9va1KFEgOGjcSAYSPZvpxOVBXmN5YVOImSrAxIZDIEBPODlie3bcbhDWzgIygqBpelDkV06hUQxgbjjKgKGTVswFUmkkHUYhaw3WW7sb14O7YVbeO2hSvDMThkMC/oGqII8ft+tCc9Wov06ObAOMMwKDVakV1Rx5UYyKmow4jYQK99s8rqUN1gR0G1GZsyKrjtQgEwIETVWGJAjQXDIjEoov/VfCakSyiDgbjx7OLJ7WOyRIEQEMkAl837OlMxu+RuASQKfoDV7QL+9wAQksQGYENTAG0cIPT+4YQQQgghhBDStSjASkgfJk9JgbxFnVeX0Qhbbh4bcM3Ngy03F4oRw3ltGIZhyw+YzbAePw7r8eO86wUyGaQDB0KWmIigq5ZBOWZMt90HgUAApUYKpUaKiITWs19nXD0IxiozV37A1Fj71elw89oFRfBLMJiqLNi8pvmUfZVWisAIFYIilOwSrkJghBLqIFm72bwisRgRicmISEzG6IsWNwaHTV7tSrKab6+2tBi1pcU4sZWtyarUBiIpdTCmD1qIAYNHee17xnTGa1uFuQIV5gpsLdrKbVuZvhL3jb6vzfF2lkAgQHSgAtGBCsxM9Q7ON7HYXYgKVKDB7oS1xfPgZoA8fQPy9A3YcBJICg/gBVhLDRZ8vb+Iy3odqFPxJu0ihAAQ+vih69p1bLDUUNhcYqCqMeNVfwqwGtl2oan8/QyFwMFP+NvEciAkuTnrtanUgC4VENFHQEIIIYQQQroKfbom5Bwj0mqhHDUSylEjW23DWK1QjhoFW24uHEVF7PnfntfbbLBlZcGWlYWAeXN519ny81H19juQJSZyNV6lAwZAKPXOOu1K8ekhAPhZmwzDwGyyw1RlgVFvgaHcjPAB/CzJ2nJ+hmuD0Y4Gox0lp2p528UyEVa8MAlydXORamuDA2KJEOJWJndig8PeQeFJV1yN4swTKDmVgYrc03C7mk+3NxsNyNn3N3L2/Y3xS67AlGXX8e7Pd/O+QoWzGhnVGTipP8lluzY4GviPhyaet6636HH1/672ynQNlAf6HHtXUEhF+PXuKXC7GRTXWhon1qpDTmOt19OV9bA52cBrSjg/c/lokQHv/Nk84ZlYKMBAHZvxmsxNsKVGfAgFXgnxIhQBwQnskupRQ5phgPpKNtjaMsBale3dj9MKVBxnF0/3nwS0HiVpqnPZMgahKYC89R/CCCGEEEIIIb5RgJWQfkioUCB29XsAALfVCnt+fnOZgdxc2HLzYC8sBFwuyJKSePvaMjNRt2EDeNMiiUSQxsbyJteSJSZClpbWZZNr+SIQCKDSyqDSyhCZFOizTXCkCpMuTUJtRQMM5WbUVphhrXd4tRMKBZCp+G95+/+Xj2Nbi6EJYeu7NmW7stmvKigCJD7vX+LocUgczc4s7rDbUHE6ByWnMlCSdRIlpzJht7BB3+jUwbz9astK8MkDdyA0fiCiBw3GxYPG49ax10MVFIwzpjM4WX2SDbxWn8RQ3VDevhnVGShtKEVpQyk2n9nMbY9WR3NB16bAq1bWtQESoVCAuBAl4kKUmD04nNvucjMoqjEju6IOCTp+gDW7op637nQzyKmsR05lPeAR69Gppdj/xGze41zbYIdGQTOPEuJFIAACwtmlpYHTgJu3NGe96nPYeq81eQDjUYpAqgY00fx9D3wM7H6XvayOAHTJbABXl9p8OSDSu14sIYQQQgghBAAFWAnp94RyOeRpaZCnpfG2M3Y77IWFkMbF8ba3nFgLAOBywV5QAHtBAeo3/wkAEIWEIGXXTl4zy5EjgFAIaUIiROqeme06MFyJkXP598FSb+eCrbXlZhjKGyCSiLyCpYZyM8AAJr0VJr0VZ07W8K6XKcUIDFciZVwEhs3kT0DWRCKVIWZwOmIGpwMA3G4XqovOoCQrA1Gp/Me85FQGGMaNyoJcVBbkcnVctWHhiEodjJjUwRg/6AqEjI6FoEXdxGpLNZRiJcxOfsZuSX0JSupLsKlwEwBAKpRiz9V7IBE2ByidbifEwq5/uxcJBRigU2GAzvu5vmJsDFIjAtgar5Vsjdfcqno4XPxs6vgQldfzcvdXh7GvoAYJOhW0LiHcx8owfVAEglXdm0VNyDlNqvSeXAsAnHY2yKpvnFzLafMOlOo9sl/ry9mlYEeL/gOAUdcB81/kb3e7qc4rIYQQQgg571GAlZDzlEAqhSw52Wu77o7boVm4kFfjtekyY2uedEWWmOi1b+Xrb8C8fz8AQBwRwSszIEtKgiwxESJt959+qlBLoUiStpr12iQ4SgVrgwO1FWY4rN6TzdjMTlTkmxCdGsTb7nYz+PaFfdDoFI2Ta6m4SbbkKglC4wciNH6gV38isQShcQNQVVTIK9tgrKyAsbICmTvYGqyB4ZG48e0PeYHHJclLsChxEQpNhVyma0Z1BjJrMmFxNk8QlhyUzAuuAsDDfz2MrJosNsNVx5YWSAtO6/JMV0+RWgUitQrMT4/gtjlcbhRWNyC7scRATkU9UsIDvPbNrqiD3elGVnkdACH2fnccAsFxpEdpMTVZh6nJoRgdHwSpmII6hLRLLAXCBrFLa4YsATRRzbVezXrvNvY670Cq2w28mgCow9narqGpjTVeU9jMV2nP/NBGCCGEEEJIb6MAaz/mcDjgcHifKn2ua7pP/fG+9RXC2BgoYmOgmDGD28a4XHCWlsGenwd7bi5EOp3Xc2DLPc1ddpaXw1lejoZdu3htRDodQu65B5ollzT3zTDdWmqgNeMWD+Bu32yyw1BugbHSDEOFBYYK9m99rQ0anYx3X016C6pLGlBd0oD8o/w+FQESBIYroA1XIjBMiZRxYVzd1+QJU5A8YQpsDQ0oO52F0lOZKM3OREXuabgcdq6PwMgoOJ1OXr/bv/gYIrEYUalpmJE8GfPj2LqMLrcLhXWFyKjJQGZ1JiJVkV7Pywn9CZSby1FcX4yNhRu57THqGAwOZoOtTUFXtYR/qn9Xiw+SIz5IjjmDdNw2z/E6XG6MjNUip7IBhdVmuBoD0QwDHC8x4niJEe9ty4VSKsKrS9Mxd7CPU6UJIR0z5HJ2aWKugaCaDbYK9NkQVJ+GQJ8NV3AyGM/3F2MxJJZawFILVGUBmfxuGW0smJAUMLokuMff4V2aAPQ/nZDzBR3rhPR/dJyT/qgjr2cBw7SY/Yacs1atWoVVq1bB5XIhOzsba9euhVKpbH9HQrqC243AXbsgq6iEtKoS0ooKiCxWn01Ll1+F+uHDuXVJZSXiVr8PW3gY7GHhsIeHwR7GLk6Nplfr/rmdAAT8yb5tNSJU7VcA7vbHFTGjHmJF89ustVoEW7UIErUbYhW7CAQu2Gr1sFRVwFpVDkVENAJThnD7MG438r7/FIxH0FWqDYI8NBzy0AgoQsMhVgX4DFI7GAfW1K9BmasMDrT9z2GRYhHGycZx607GCSeckAvk7d7P7uBwA3l1ApwyCJBlEKDEzL9/jw53ItLjLa7SApSaBUjWMFBRCVdCup3GfAajC9+HylYOEeNss+2mwa/BLAvj1kNNxxBlOIA6eRTq5VEwKAfCLvbOaCeEEEIIIaS3mM1mLF++HEajERqNps22FGDth0wmE7RaLfR6fbsvgHORw+HApk2bMGfOHEgkFEXpqxiGgau6GvbcXNhz82DPY/868nIR9dFHkKWkcG3rN29G+f0P+OxHqFZDkpAAaWIi5OlDEHDxxRAqFD11N1rFuBnU19rYTNfK5oxXQ4UZFhMbyBRLhbjh1UkQCJsDg3t+ysOxP0t4famCZAgMUyAwXIHAcCUCwxUIilBBqWVrjurPFGDt4/e3OR5VUDCiUtIwYelVCIryzhJzup0oMBWwma41mciozkC2IRs2V3PZhy/mfYHBIc0Tc+0t34vbt9yO+IB4Nsu1Mds1NTi12zNdAe9jvarOhr9zq7HzdDVyqurx420TeEHld7acxr+35kEoAIZGazElKQRTkkIwPEYLiYjKCRDSbdxOwHCmMds1GwJ9Dpv9Wp0Nga0OjFgO58OFvF+qhFufg+jvt3ndMEEDwcSMBRM9Bu7oMUDYYKAb6kcTQnoefX4npP+j45z0RyaTCTqdzq8AK31q7cckEkm/fmPr7/evX4iMhCIyEpgyhbe5ZUkAocMBcXg4nBUVXl246+thO3YMtmPHUP/77wi+/HIIPZ53t9UKobx3MiyDI6QIjvDOuLJZnDCUm2E22SCV8SdmMlV6Z/U21NrQUGtDySkDt23A0BBcdCeb5RuZmIzbPvgcxzbvg6HiNPRFOagqzAPjdnv0UYOcvbsw49qVvOOiuqQIDbW1iExKQVpoGtJCmyfecrqdyDXkIqM6AyerTyItNA0SUfO+pwynAACFdYUorCvEhsINAAABBIjXxLM1XUOGYGjoUIwMG9mRh65Dmo71qGAJLgtW47Kx8T7b7cplJylzM8DRYiOOFhuxalse1DIxJiaGYFpKKKYl6xAfQnUhCelaEiA8lV08MQxQXwGBsRgSWYv36erTaElQmw9BbT5w/FuIAECiAkYsBy56rdtGTgjpWfT5nZD+j45z0p905LVMAVZCSI9reSq7dtEiaBctgquuDvbcXHZirdONk2udzoWjtBQAoBw1CkIpP2BZ8uBDsGVnQzl+HFTjJ0A5fhwkYWHoTTKFGOEDff+6NfXKFAye2oDa8gYYys2oLTejtrwBNjP/9NrAcH55D6UmEMe2y+C0p0EgSEPIQCHkyhow7lJYTYUwVuRDplJBHaLj7Xd8y0Yc/O1HCEUihA1MRHRqGqJThyAqNQ2qwCCkBqciNTgVS5KXeI01SB6EobqhOFVzCnZ3c41YBgwKTAUoMBVgff56JAclY92idbx9C4wFCFOGQSnpuTIlD8xJxV/Zldiercepijpue73NiU0ZFdiUwQbwb52WgMcWpLXWDSGkqwgEQEAEu7S0ZDWgz4GzIhMFe35DgqwGwrKjgEdWPRwNvjNY//cQO6FWzFggPB0Q0cdZQgghhBDSu+gTKSGkzxAFBEAxYgQUI0bwtrsbGmDLyfFqz7hcMO/bB3ddHYxFRTB+/wMAQJqQwAu4ioOCemL4ftHoFNDoFBg4rDkQyjAMrPUO1FaYG4OuDYhNC+btV2+wwWl3N7YH6mvcqK8JBBAIYDBESjccrjp8/NBOXHTHMEQmBQIASrMyAABulwvlp7NRfjobB//3MwAgKDIKUamDET1oMOKGDIM2jB8EuTT5UlyafCkcbgdyDbk4qT/JZbtm12bD4WZLIQwJGYKW7t5yN87UncFAzUA201U3BINDBiM1KLXbgq5TknWYkqzDExcBFSYrduTosSOnCjty9KhpaA4QD43R8vYzmh1Y83c+piaHYniMFmIqJ0BI95NrgZgxYMKH42SxBvELFkAocAPlx4GifUDxfnaJHcvfz1gM7P+oeV2iBKJGssHW2HHsX3Xv/shGCCGEEELOPxRgJYT0eUKVyivoCgCu2lrI09JgOXIEjL05gGbPy4M9Lw+Gr74GAMhSUxHx1JNQjh7dU0PuEIFAAEWAFIoAKaIaA6MticRCjF+UgNqK5sxXh83l0YcQApEWNrMTSq2M2z5ywSJItu5FUcZJMK5qXp+1ZaWoLSvFyW2bMebiSzH9mhu56xiGgdvlgkgshkQowaDgQRgUPAhLsRQA4HA5kGPIQUZ1BuI1/FP26+x1KDAVAAByjbnINebi17xfAQBCgRAJ2gQMDhmMwSGDMTtuNsJV4Z1+7FoTrpHjstExuGx0DNxuBhllJmzPqcKObD0mJ/KzfHfl6vHW5hy8tTkHAXIxJifqMC0lFFOTdYgNpokCCekxYhkQM4ZdmrScKqB4P3/dYQYKd7FLk8B4Nth64SuAkv9jFSGEEEIIId2BAqyEkHOWWKdD/Gefwm21wnLkCBr27IF57z5Yjh8HnM2n3NtOnYKoRRarLT8fjuJituyAqu/X5FRqpBizYAC3zjAMGgz25oBrhRmG8gYY9VYEhDTXOkybPB2GqhhUFo8B47bA7SyD21kCt7MEjKsCABukLcqSY+8veRi/KAEAYKqqwCcP3onIpBREDxqMqNTBiEoZBJmSfawkIgkXJG3J5rJhafJSZFRnIMeQA6e7+blwM26cNpzGacNp/JL7C9KC03gB1oqGCpQ1lCE1OBXiLvoXJRQKkB6tRXq0FnfMSPK6fkdOFXe5zurEhpPl2HCyHAAwUKfC1GQdpiWHYkJiCNQy+rdJSI9qUVIGqRcBN28BivYDxfvYv8Yz/DaGQqC+ErhkNX97/nbAagRixgEBXf/DDiGEEEIIOX/RN0VCyDlPKJdDNWECVBMmAABc9Q2wHDqIhr17Yd6zF87qakgHDuTtY/z5Z1S//wEgFkMxbBhXUkAxcgSEMpmvm+lTBAIB1EEyqINkiB3UdoaWXCVBSLQahgohXMIEiKRsEJVhnGBcFXA7S2Co1KKiwMTtU5KVAafdhqKM4yjKON50qwiKjEPskCGIHTwE0YOGIKBFzVcA0Cl0+L9J/wcAsLvsyKnNwclqtrxARnUGcmpz4GScEAqEGBQ8iLfvxsKNeGX/KxAJREjQJiDIHISgsiBMjJkIibB7iuXfMysZI2ODsD2nCjtP62EwO7jr8vUNyNc34LPdhRgdH4Qfbp/ULWMghPhJLAWiR7MLbmO31ZWzma1F+4DiA0DpISB6FCBq8Z6x+z0g+3f2cmAcW04gZhxbhiB8KNs3IYQQQgghnUABVkJIvyNSq6CeNg3qadMAAG6bzWtiLfPefewFpxOWQ4dgOXQI1avfh0AqhWLkSKgmjIdy/Hgo0tMhkJ7bX7pHzonDyDlxcLsZ1NdYuYm12KxXHWrLB8BS50CQx8RaLpcTmtAwmKoqPXpiUFtWiNqyQhzbvB4AoNBEYMrypzFgqA4ancLrtqUiKYbohmCIrrlOq81lQ05tDgpNhV71WDOq2ZqxLsaFHANbd3ff1n0IkAZgRswMzIqfhUlRk6AQe99WZ0VqFbhibCyuGBsLl5vBiRIjduRUYXuOHocKa+F0s6coT07iB5MZhsHjP57AiFgtpiaHIiqw68ZECOmAgAgg7WJ2AQCXA2jQ89swDJvx2sRwhl1OsLW7IZY31nIdA6QvZS8TQgghhBDiJwqwEkL6PV8ZqcE3XI+G1BSY9+yFvaCA287Y7TDv3Qvz3r0AgKCrr0bEk//sqaF2K6FQwE2yFZ8ewrvO2uCA29Vc63DozLlIGjsDHz+4AS5HSWNZgVIwrioAze1sZhl2fJMDdZCcC7D+/d2XcNgAhz0ccUMGISQ2EIGhSogk7ORRMpEM6bp0pOvSvcY4PXY6JEIJTlafRK4hFy6GLWFQZ6/Dr3m/4te8X6EQK3D9kOtxx4g7uvohgkgowPDYQAyPDcRdFySj3ubEntxq7Mipwpw0/inFuVX1+GrfGXzVGLNJDFVhanIopqXoMCEhBEop/YslpFeIJIAmkr+NcQOL/t08gVbJIcBpab7eaQXO7GaXkGR+gNVWD1SdAiIoy5UQQgghhPhG3/4IIeclzdy50MydCwBwVFTAvHcvV1LAUVLCtVOO5c9g7dTrUfbU01yGqyw5GQLhuT/rvFzlffq9Qi3FbasWwqS3oLbcDEOFGfqialTkZ8NQnguntRhCCTvBVVAEm4nqdrtwaP0vsJkbAABH/xBCIAqHSBINVfBAhMYnQxcbhqAIJYLClQiMUEKhbg5YzB8wH/MHzAcAmCwmvPvbuzCEGrCjdAfqHfUAAIvTAo1Uwxurm3HDYDMgWN61E9qoZWLMHhyO2YO96zXuzOFnyOVWNSC3qgGf/F0AiUiAMfHBmJrC1m8dHKmBUCjw6oMQ0kOEImDQRewCsFmuFSc9SgvsA2oL2Oti+O/7OLMH+HIpIJIBUSPY62PHsX81UT15LwghhBBCSB9FAVZCyHlPEh4O7aJF0C5aBACwFxezAdc9e6Ecx/+ibd63D/VbtqB+yxYAgCgoCMrx46EaPw7K8RMgHTjAqxzBuUwkFiIoQoWgiKaJwOIBjALDMLDUOVBb3gBDhZnLXq0uLuKCqyw3GFcZnK4yGEsPwFgK5O4NhlAcBZF8LNTB4bjhX1N4t6kvrodYKoRCI8cQ6RAsmLwAjJDB3rK9+PPMn9hatBWz4mbx9jlWdQwrNqzA6PDRmBU3C7PiZiFCFdF9DwyAaybEY1hsIHZk67E9pwpHigxwNZYTcLgY7M6rxu68ary/LReHnpwDIfrP64KQc55IwgZLo0YA425mt9VXsjVcQ/m1obnSAi4bULSXXXY3XqeJYWu4xowFYicAMaN76A4QQgghhJC+hAKshBDSgjQmBtKYGAQuXep1nfnwEd66q7YWdRs2oG7DBgCAODQUygkToJ4yGdrFi3tiuL1CIBBAqZFCqZEiOiWI266LjceNb32A0weOouDocVQVnoLFVMnbl3HXwGWvgUg+lst8BQBDRTlsDfXY+UMNynPrIRQJIFIqsd2Yg8iEQKQNGInJE6bgqYlPQSjgZw3/eeZPuBk39pfvx/7y/Xh538sYqhuKWXGzMDt+NuI18V3+GIhFQoyKC8KouCDcOzsZRosDuxvLCWzPqUJRDXv68eQkHcQi/nif+vkEpCIhpqaEYtyAYCikoi4fHyGkg9RhwKAF3tujRgHDrmSzXWvy+NeZioGTxcDJH4GIYcBtO/jXW42AXNt9YyaEEEIIIX0CBVgJIaQDwh97FIGXLYV5zx407N0H8/79cNfVcdc7q6pg+vVXOMpKvQKsrvp6iNTqnh5yjxIIBAiKjMbYi6Mx9mI2UGE2GlCSnYmSzJMoOnkSVWfyIJEpMXTmEI/MWODYnxuw/+fvAYEEQlEkBOIoiKxxyNwlRNbf5QAAsUyEsLgAhA/QIHlsOELjAgAAYcowxGviUWgq5Po7rj+O4/rjeOvQW0gOSsbsuNmYGz8XSUFJ3XLftQoJ5qdHYH46mzlbWN2A7Tl6xAfzJ/KyOlz4Zn8RbE43/rMzH1KxEOMGBGNqsg7TUkIxKCKgX2VBE3LOS53PLgA7eRZXVqCxlqujMWs/dpz3vu9PZcsRxI4FYhrLCkQOByTynhs/IYQQQgjpdhRg7cccDgccDkdvD6PLNd2n/njfyLlBlJCAgIQEBCxfDsblgi0rC5a9e2HZtx+WQ4fAWCyQjx3Le40yDIOCefMhDAiAYtxYKMeNh2LsGIiCu7ZmaF8kUaowYMQYDBgxBgDgsNlgqqpASEwcu974OJVknmR3YBxwO88AzjNwYQ8gUEMkTYVIOggOJgylOQaU5hgQFKVAYCQbpFiWvAyXRC3FscxsHMFubKncjGxDNjeGnNoc5NTmoLSuFE9PeLpH7neURoplo6N49xEAjhcZ4HC5uXW7042dp/XYeVqPl37PQqhaismJIZiSFIJZaWFQy+hfNem/zrn/6VItkDCbXQDA7QQqMyEsOQAmbDAYz/tRXwmJofFHn4yf2QUAI5KCCR8KJmYMmOgxYKLHAppogH5YIf3YOXesE0I6jI5z0h915PUsYBiGab8ZOResWrUKq1atgsvlQnZ2NtauXQulUtn+joSQruN0Ql5cDKdGA6dH8FRaXoEBb77p1dwWEQFzYiLMiQmwJCTArVD05Gj7FOPpLFgqSmGpLIfL0uCzjUAUBLF8MkTSFERMq4dY1fwvzFIuRvVh9vETq1xgNBZUqguRqTyEDPlhuIUuXKu6FqmS1OZ93BZstm7GYMlgDBAPgEjQM6fqNziAbJMApwwCZBoEMNh9B1aeHuVEsKxHhkQI6WJqaymGFX2GIHMexG5rm23/SnkaBlViD42MEEIIIYT4w2w2Y/ny5TAajdBoNG22pQBrP2QymaDVaqHX69t9AZyLHA4HNm3ahDlz5kAi8Z75nJC+yHr0KPSvvwHr8eOA0+m7kVAI2aBBiFy1CmJdSM8OsA9hGAZ11VU4c+IY9vz+K6zlJXC7XNz1F6y8DwGh6YgbEsw7lX7vz/k4urnYZ59CkQACnQ0pqbGITgpCwshQAMBv+b/hqd1PAQACZYGYHj0dF8RegPER4yEVSbvxXjZjGAb5ejN25lZj52k99ubXwmx3IUGnwh/3Tua1fevP0zheYsSUJB2mJIYgKUxF5QTIOe28+J/udgFVWRCW7Ieg5AAExfshqMnlrmZEUjgfygfEzb+mCPd/CMGJ78FEjwUTPRpMzFh2Qi063sk56rw41gk5z9FxTvojk8kEnU7nV4CVzjvsxyQSSb9+Y+vv94/0L5IxYxDw1Vq4GxpgPnQY5r170LBnL6wZGYC78XRxtxvOkhLIw8MgEDZPitSwdx/AuKEYMQJC+flRty8kMhoaXRjOmO2YOXUKCg7tR+aubajMz0P69CmQyJofh9yD+3Bo/c8ISxiDtEkJqC6xQ19cD7er+fdDt4sBKqTIrKiAodSK1HHsqfs7StkJaULrY1HnqMHPeT/j57yfoZKoMC16GmbFz8LU6KlQSrr3bIDUKClSowKxcmoi7E43Dp2pRYPN6fUe92dWFbLK67A9pxoAEK6RYWpyKKalhGJKkg7Bqp4JChPS1fr3/3QJEDOCXXAzu8lcAxQfAIr3QWA1QaJoUZ+7aA9Qeohd9jduU0c01nJtrOcaNQKQnL9nPZBzU/8+1gkhAB3npH/pyGuZAqyEENKDhCoV1FOnQD11CgDAZTLBfOAAGvbsgXnvPkjj43nBVQDQv/cezHv3QiCVQjFiBJQTxkM1YQIU6ekQSPt/QE0RoMGw2fMxbPZ82MwNvOAqAGTu2IozJ47izImjEInFGDhyLGYunw51aCpqSqyoKDChIt8EY6UFABA2oPmXx+cmP4c5cXOR/Q4DsVUGo0yPSnUhqtRncKgmExtzN0MsFeLqtKtx/+j7e+T+SsVCTEjwzmBusDlhMPNrAFWYbPj+YDG+P1gMgQBIj9JiarIOl42OQUJo/55QjZBzmjIYSJnLLr5Yar231ZcDmb+yCwAIxcDUB4GZj3ffOAkhhBBCiF8owEoIIb1IpNEg4IILEHDBBQAApkX5ALfNBsvhw+x1djvM+/bBvG8f9O/8GwKFAsrRo6GaMB7K8eMhHzwYAlHP1BDtLTKlirfOuN2oLini1l1OJ07v343T+3dDqlAiedwkDJoyHbOuGwe71Y3KQhNU2ubTcFUSFaYEzkCe9W8AgNamg9amQ3L1aLY/gQs1ylIoarXItJVi4LBQyNUSMAyDams1dApdD9zrxrHKxNj92AXIqazH9uwq7MjRY29+NawONgOaYYDjJUYcLzFi7MBgXoDV5WYgFIDKCRByrrj+NzbLteQgULwfKNrHXraZmtu4nYA6jL+frQ748bbGLNexQNRIQEr1+AkhhBBCuhsFWAkhpA8RiL3fliNfeB4Ne/fCvGcvHMXNNUYZiwUNO3eiYedOAEDUq69Ae/HFPTbWvkAgFOK6V/6N8txsZO38C1l/b4fZaAAA2C1mnPxrM07+tRlKbSDm3HI3ksaM9+5DAIyYHYuKAhOqCuvgbAxYAoCIESG0IRauk8CWk1m48p8ayNUSZNdm4/JfL8cYzQRMjZqGOYNnICYgpvvvr0CAlPAApIQH4KapCbA6XDhYWIvtOVXYka1HRpkJUpEQ4wcG8/b74VAx3t6cg6nJOkxNDsXkpBAEKvt/9jMh5zRlMJA8h10AtparPpsNthbvY0sMxIzj71NyEMj6jV0ANss1PB2IHdccdA0aQLVcCSGEEEK6GAVYCSGkDxPKZNBefDEXOLUXl8C8dy8a9rIlBZwVFVxb5Tj+F+36nbtg+O47KMePg2rCBEgHDuyXGYwCgQCRSamITErF9GtXoujkcWTu2oacvX/DbjEDAMxGAzS6UN5+brcLQqEI6iA5Jl+WzG5zuVFd2oDKAhNXWqCmrAFgALFMhOAoNoN2U+EmMGDgPqmB+Y8ofCndB0vQBoQP0GD0sCEYMSQVUnn3/4uVS0SYnKTD5CQdHrsQqKqzIbPMBKWUf9s7cvQoMVjw9f4ifL2/CEIBMCwmENOSdZiaEooRsYGQiISt3AohpE8QioCwNHYZvcJ3m5JD/HW3Eyg7wi77PmS3qUKB2PHAFZ+xfRJCCCGEkLNGAVZCCDmHSGOiIY25FIFLLwXDMLAXFMC8dx9sp09DEh7Oa1v/11+o++MP1P3xBwBAHBoK5fjxXMBVEhPT7wKuQpEI8cNGIH7YCMxaeTvyDx9A1s6/YKyqQGj8QF7bg7/9hMyd25A2ZQZSJ02DRhcKoUiI0NgAhMYGYMjUaACA3epEVWEd6g02CIXs4xWmDENSYBLCMuMBAGp7INQVgXBXAPv3VmAfyoEgG6ISgpCSGoeo5EAER/LLG3SH0AAZQgNCfV4nFQthd7LZuW4GOFJkwJEiA97ZchpqmRj/z959h7dVnQ8c/0qyvC15771iO3vvvcgEAmEk7L1LaWmh4wctpRtKC2GUEUggZYWRBcTZe+/YjuO9ty15W+P+/lBwUBwgCUlkK+/nee4T6Zxzr851dDRenfuekQkB3DQ0ismpIWfdXwjRA4x+HHrNtM1wLT41y7U6Czi96B/N1VCb2zW4evgj279RQ8EvTma5CiGEEEKcBwmwCiFED6VSqXCLi8MtLu6s9W3HjtndN1dXY1y1CuMq26Wj2vBwPIcPRzdzBt5jx17y/l5uWlc3koePJnn4aKxWS5dgcub2zVQX5lNdmM+WDxYTmdqHlNHjSR4xGg+f0wthubq7ENHLz27fG3rdwA29bmAdB8k+UoapUoOL+fQl9ypUUO9O+f5WyvefoPfYcCYsTLE7RmNdG95+bpclyP3yzQNp7bCwp6COrafyt56obOysb2o3k55RydBYP7sAq8Wq0NxhRucuK8EK0SOo1RCcYtsG3WYra204I5frPlsQ9Uxb/gG1J223vYJOpRQYYktDEDEIXC/9j0RCCCGEED2VBFiFEMJJxby/lLbMLFp277LlcN23H6WlpbPeVFaG4fPPcQkMsAuwKoqCpb4eF3//sx22R1KfMVOro7UFzRn5bksyj1GSeYwNi18ntv8gUsZMIHHwcLTu7t973ClXD2TK1QNRrAon8gvYcfAAhblVqKo88G+JQKPYHjckzhawbTG18MK+FxgXMJFjL3bg7q0lJFZHSJyO4FgdITG2HK+XgoerhvHJQYxPts1wrTS2sfVkDVtP2gKudc0djE2yn/16tNTAda/tYECUb2f+1v6RelwknYAQPYeHLyROtm0AViuYmu3btNSdDq6CbZbriTW2DUClgZA0W7B16D2220IIIYQQopMEWIUQwkmpNBo8+vTGo09vAu6+G8VkovXYMVsO1127aT14EKW9Hc9h9gs/mYqLyZ02HbekJDxHjMBr+DA8hw5Fo9c76EwuPlcPTxY+/yL1FWVkbd9M5rbN1JfZFhCzWizkHdhL3oG9uLi5Me/XzxDVu98PHk+lVpGSEEdKgm02cU1rDRvyNjJMOw5DcUfnDNhtpdv4OPtjdtUfYyb309ZkovBYLYXHajuPpQ/ysAVb43SExOoIjvFBfQkCmiE6d64fHMn1gyOxWhUyyo2khPrYtdmaXY3FqrC/sJ79hfW8tO4kOncXRifagq1jkwKJ8pcVyoXoUdRqcLMf67h6wa2f21IKFO+xzXZtazhdr1ig4qht63Od/b6GUqjNgYjB4OZ9ybsvhBBCCNEdSYBVCCGuECqtFs+BA/EcOJDABx7A2t5O66HDePTra9euedcuANpPnqT95Enqly4FlQr31FRbwHXEcDwGDUbj3fMvF/ULDWfkdTczYt5NVBXkkbV9M1nbN9NUZwt4KhYLQbHxdvu0t7Tg6u6OSv39Qc9Aj0Bu6D3fdif5dPnG4o0AmNUdFPlmENQUjYfZPiBhqG7FUN3Kyb2VqDUq7n1pnF2qxPZWM65uGlTqi5daQK1W0SeiawBd76klMdibnKqmzjJjm5mvjlXw1bEKAOICvZjbP5yfT03usr8QoodwcYOESbYNbLNc63JPBVv3QPFeqMoAlRrCB9rvm7kSvv61rS64ty2tQNQw22zXgATJ5SqEEEKIK4IEWIUQ4gqldnPDa/iwruVeXrj37Uvb8eO2L9kAikJbRgZtGRnUvfMOaDR4jRhB9NtvXeZeXxoqlYqQuARC4hIYt+AOSrKOk7VtM1arBXcv+wDo5qVvUXD4ICmjx5EyejxBMXHnnEf19yN+z+ToyawrWsfmwA9o6mhC1x5AcFMMwY0xBDfFENgciYtiSxMQGOmNi9Y+vcHGpZkUZ9YTHONjm+F6ararl97t4vwxvuO2kbHcNjKWsoZWtp2sYcvJarbl1NDQYupsk1/TTEl9a5d9sysbSQjyRnMRA8FCiMtErYbAJNs2cKGtrL0RqrLA9YxZ6yV7bP8qVqg8atv2L7aVefidyuU6DOLGQbT9FRNCCCGEEM5CAqxCCCHs6GfNQj9rFpbGRlr27juVw3UP7VlZpxtZLKhcur6FGFauRBsWhke/fqhcXbvU9wQqtZqotL5EpfXtUmc2mcjevZ325mb2rljO3hXLCYiMJmX0eFJGj8c3JPQHj+2p9WRKzBSmxEzBZDGxu2I36wrXsaFoAzntBwBQWzXcF/ko47TTcXU/HVytaqki2DOYygIjHa1mSrLqKcmq76z39nOzBVw7Uwvo0LppuvThQoT7enDD0ChuGBqFxapwrNTA1pPVbDlZw4HCesYlB9q1N7aZmPHvrXi7uTAmMZBxybaUAuG+HhelP0IIB3DzOfviWL3ngbv+1CzX47ZA67da6+HkWttWNa9rgNViBo18HRFCCCFEzyefaIQQQpyVxscHn0kT8Zk0EQBzfT0te/baAq67duM5YoRde8VkouKZZ7G2tKDy8LClIziVUsA9Le2sAdmeprXRQHhyKgWHD6Ccmt1bW1LE9o+Wsv2jpYQlp5A6ejy9Ro7FU+/7g8fSarSMiRjDmIgx/H7E7zlQdYD1RetZV7iOKYPH0Ms/srNtniGPq7+4mn5+/RmjvxE3k472Rovd8Zrq22mqryb3YDUA425Kpu+E08ewWhVQlJ+cz1WjVtE/ypf+Ub48MimJxjYTLmekS9iRU4vFqmBoNbH6aDmrj5YDkBDkxdgk20Jbw+P98XTt+c8JIa54qbNtG9hmuZYesM1q/Tafa2udrS7qjCsmLCZ4qS+ED4K0uZA83TbjVQghhBCiB5JvNkIIIc6Ji58fuunT0E2fBtAZYPxW2/HjWFtabHWtrTTv2EHzjh1UA2pvbzyHDMFzxHC8hg/HrVevH8xh2l35+Acy76lnaTEayN61naztmyjNyuisL8/Oojw7i43vvcltf/sPgdGx53RcjVrD0NChDA0dyq+H/rpL/YaiDQAcqT/MkdDDEAK93fsz1nUaMW0pmMpdqCpqxNx+OugaHKuzO0b5yQZWLTpMcMyptAKnUgt4+7mdc4qDs/Fx13YpC/B2ZUafULbn1GBsM3eW51Y3k1vdzLs7CtBqVAyL8+fdO4ehvQSLeAkhHMDNB+LH2zYARYG6PFug9czZqwVbobEcTqy2bWoXiBsPqXMgZTZ4B13+/gshhBBCXCAJsAohhLggZwZItTExhP35z7Ts3k3z7t2YKyo666xNTTRt2kTTpk0AJKz9Btfo6MvZ3YvKU6dnwLSZDJg2E2N1FVk7tpC1bRPVRQUA+AQEEhBpf36Gqkq8/Pxx0XYNSH7X2YKdAe4B9PLrxYn6E6cawfH2wxxvPwxAVFwUU8ZOYbTXJHwagqkqMBIYaZ87trLAiLnDStnJBspONnSWe+hcbcHWUwHX4Bgf3Dx/uI8/ZmisP0Nj/TFbrBwpNbA1u4atJ6s5WNyAxaoAYLIoNLaZuwRXDxTVkxTsfdbArRCih1GpbAtdBSR0rWupA69gaK6y3beaIXe9bVv9BESPsgVbU+eAPuLy9lsIIYQQ4jxJgFUIIcRF4eLnh++8a/Gddy2KomAqLKR5957OHK6W2lpbu/AwtFFRdvvWvPkm7Vkn8Bw+DK8RI7rUd2e6oGCGXX09w66+npqiArJ2bMHDR9clAP3VoheoKSokafhoUseMJzKtD2r1ueVIvTbpWq5NupZiY7EtjUDROg5XH+6sL24sZnHmYoqii3hp4kukjQ7vcgy1RoVPgDuNtW125a3GDgqO1FBwpAYA/3Avbv4/+5lmiqJc0CxXF42aQdF+DIr242dTkjC2mdiZW8uW7Gq2nKxmbJJ97lZFUXh02UFqm9uZ2TeM+YOjGB7nj1oWyhLC+fS9Hnpfa5vdmrkCMleCodhWp1ihcJtt2/kKPH7UFqwVQgghhOimJMDqxEwmEyaT6ccb9jDfnpMznpsQzkQVEYH3vGvxPhVw7cjNpXX3HlCpMJvNdm2NX39D+/HjGFevBsAlLAy3IYPx8famffRo0OsdcQrnTR8WwfDrbgbsX6OMNVWdqQSObVzLsY1r8fL1I2nEGHqNGkdwXMI5BTBDPUJZ2GshC3stpKqlik0lm9hQvIH9VfuxKBYmREywe9x2Szv/2P8PxkWMY/iY4fQeH0ZrYwdVhY1UFzZSVdBIVWETHa2n/z8Co727vL4u/+sBNFo1QTE+BJ/adEHu5x109dDApOQAJiUHACmYLFa7x8qvaaa0oRWAzw6U8tmBUiL9PLhuYDjzBobLIllOSN7TBeFDbNukP6AqP4TqxGrUWStR1eUCYEmegfWM9wzVwaUoEUMgKEUCrz2EjHUhnJ+Mc+GMzuf5rFIURbmEfRGX0aJFi1i0aBEWi4Xs7GyWLVuGp6eno7slhBA/SGUyEf+n59G0tZ213qrV0tQ7jcYBA2hOTgbNuc367E5MjUbqju6nqaQQxdz1TVrro8M7JhGf2ARcdb7nffwWawtZpixStal4qE8HIU+YTrC0eSkArrjSS9uLNG0aydpk3FRugC1ForlFRUeDBpNBg5u/BY/Q08EMqwnK1vl0eUyVVsFVb7FtvhZc9VY0bj/tI0VDO6SXqtlfo6LVYh80UaGQrFcYHqzQ10/Btec9DYQQ50pR8GkrJaxhH1X6fjR4xndWuZkauOrYYwA0uYVS5juEct8hNHjESbBVCCGEEBdVS0sLCxYswGAwoNPpfrCtBFidkNFoRK/XU1NT86NPgJ7IZDKRnp7O1KlT0f5ILkMhRM+gmEy0Hc+gde8eWnfvoe3QIZT29i7tgp//E7q5cx3Qw4vD1N5O/sF9ZO/cQsGhA1gtZ8zKUqm559XFePhcnNfuP+35E5/lfNal3FXtysiwkUyKmsS4iHHo3b5/hnB9eTNr38rEUNX6o4839+f9CY3/6X1vN1lIz6xi+cEytufWcuYnlSBvVzb9YhyuLrI4Vk8n7+nifKn3v4Pm6191KVd0kVhTZqGkzEGJGArnmIJFXB4y1oVwfjLOhTMyGo0EBgaeU4BVUgQ4Ma1W69QvbM5+fkJcUbRaXIcOQTd0CDz0ENb2dhr37iXjzbfwz8rCajCgcnXFd/p0NN8Z9+35+SgmE+7JyQ7s/LnTarX0HjuB3mMn0NbURPbu7WRt30xxxlFQFKL79kfnH2C3T1l2Jn7hkXh4d51F+mOeHv40E6ImsK5oHZuKN2HsMALQYe1gc+lmNpduRqPScHXi1fxh1B/OeozgaF9u+eNI2ppNVBUaqSowUlnQSGW+gdbG07NxVSoIidGj1Z7+aHFscwnHt5UR/O0iWrE6/MK8fjSnqlar5drB0Vw7OJrShlY+21/CJ/tLKKprAWBkQiBeHm52+7SZLLhrJaDSU8l7ujhnaXNBBWSsgKIdtnytgMpYgmbPG7DnDdviWWlXw8x/yKzWbkbGuhDOT8a5cCbn81yWAKsQQohuR+3mhufw4VTV1jJ46lTad+3GVFyExtvbrl3tm29h+Owz3Hr1Qjd7FvpZs9CGd13gqTty9/am3+Tp9Js8nca6Gk7s2Ip/eKRdG6vVwooX/kxrYyNxAweTOmYC8YOGonVzP7fHcHFnYvREJkZPxGQ1sa9iH+uL1rO+aD01rbZFrSyKBb1r1xmsVS1VBHsGnz6Wl5botACi02wBYEVRaKxro+pUsLWtyYSru/3HivI8AzXFTdQUN5GxtQwArZvGlsc1VkdInC3o6u33/ecT4evBo5OTeHhiInsK6vhkXwnXD7b/O7V2WBj11/UMifVn/uBIJqYEo9XI7FYhnJIuDIbfb9uaquHEaluwNX8zWE9dFdBcBdVZXYOriiIBVyGEEEJcEhJgFUII0a2ptFp8Jk3sUm5tb6dx7VoA2k+coPrECapfeBGPIYPRz56Dz/RpuPj5Xe7uXhAf/0CGzL62S3nxsaM0N9QDkLtvN7n7dqN19yBp6AhSxkwguk9/NC7n9lauVWsZGT6SkeEj+c3w33Ck+gjrCtexrmgdU2Km2LUtbypn2vJppPqnMjl6MlNippDgm2DXRqVSoQvwQBfgQeLgYM6mo9WCSoXdJf6mdgul2Q2UZjd0lqWNCWfiLSk/2H+1WsWI+ABGxAd0qfv6eDn1LSbSMypJz6gk0NuVawdGMH9IFMkh5z/zVwjRQ3gHweA7bFtrPWR/A5krIWcdpJ6RTsZqhUVDIay/rS5xCrh5n+2oQgghhBDnTQKsQggheiarlaDHH8e4ciWthw93Frfu20/rvv1UPP883mPGoJs9C59Jk1B79LwV6HXBwQyedQ1ZO7bQXF8HgKmtlYytG8nYuhEPnZ5eI8eQMnoC4ckpqM5xZpZapWZA8AAGBA/gF0N+0aV+fdF6ADLrMsmsy+SVQ68Qp49jSvQUJsdMJs0/7Zwea9ZD/ehoM1Nd1Ehl/rfpBYw01dvn1/UNsV+Q0dRh4ePn9xIY5U1wtI6gGB+Con1w8zj7xxaTRSFE50al0XbcmqYO3tyaz5tb8+kf5cv8wZHM6R+O3kMuVxPCaXn4Qf+bbFt7U9f64t1Qm2Pbji0HF3dbkDV1DiRfBR6+l73LQgghhHAeEmAVQgjRI6k9PPC/ZSH+tyyko7gY46pVGFauoiMvz9bAZKJp40aaNm4k5n/L8Bw40LEdvgB+oeFMuO0ext1yJyUZx8jctpmTu7fT3tIMQKvRwKFvVnNi5zYeeH0JKs355yA9W6DUz92P3gG9OV57vLMs35DPm0ff5M2jbxLmFcbk6MlMi53GwOAf/ru6ursQkexHRPLp2cTNDe1Ungq2VhUYCUu0T1FQW9JEQ2ULDZUt5Oyr6izXB3sQHKMjKNqH4BgfgqJ8cPVw4YYhUcwbGMHWkzV8sr+Y9IxKTBbbtNnDxQ0cLm7guVUZ3D4qlt/MTD3vv5EQooc528xUQwm4+0Jbg+2+uQ2yVtk2tRbix9uCrSmzwSvwcvZWCCGEEE5AAqxCCCF6PNeoKAIffJCABx6gPTMTw8pVGFevxlxVhTYyEo8BA+zat+zbh8rFBff+/c951qcjqdUaovv0J7pPfybf/SD5h/aRtW0zefv3YDZ10GvkWNRnBFezdmwhPCkFXdDZL9//IbPiZzErfhZlTWVsKNrAuqJ1HKg8gIItaFneXM77me+Tb8zn9Smvn/fxvXzdiB8QRPyAoLPWN9a1odGqsZisduWGqlYMVa2c3FtpK1DBvS+Ow9XDBReNmokpwUzoFUR9i4kvD5Xyyb4SMsptC3u1m614usoiWEJcsfrNh97XQMFWW87WrFXQXG2rs5psaQVy1sHGP8MTWaCWPM5CCCGEOHcSYBVCCOE0VCoV7mlpuKelEfzLX9Cydx/WpsYuQdSqf75A66FDaKOibItjzZmDW3y8g3p9fly0WpKGjiRp6EjaW1rI2buT4Dj7/KgthgbWvPxPFKuViJQ0UkZPIHnEaDx1XRez+iHh3uHcknYLt6TdQk1rDZuKN7GuaB27y3djtpqZEm2fu9VsNfP87ucZEzGGUeGj8HC5sLQMSUNCiB8YRH15M1WFjVQXNlJV1EhtSRMW8+mgqy7QA9cz0gZsWnaC8pMNRMb48Jc+sTQNU5NeXs+K4+VcN8h+cazC2mZ+vfwI8wdHMaNvKJ6u8rFICKem0ULCJNs26wVb2oCMFba8rcYSW5vkq7oGV49+ChGDwL9nvE8IIYQQ4vKTbxJCCCGckkqjwWvE8C7lHcXFtB46BICpuJja116n9rXXcUtLRT97DrpZM9GGhFzm3l4YN09Peo+f3KX8xK5tKFZbILI0K4PSrAw2vvsGMf0Gkjp6PAlDR+Dqfn7Bz0CPQK5Pvp7rk6/H2GFkS8kWRoWPsmuzv3I/n2Z/yqfZn+Lh4sGYiDFMjp7MuMhx+Lie32JTGo2awEgfAiN9YLStzGKxUlfW3Blwdffq+jGmqsBIfUUL9RUtZO+2zXQNVsEvQ3w4saKAumgfgmN0BEZ58+n+Enbl1bErr45nVhxndr8w5g+JZFC0X4+Y2SyE+AnUGogZZduu+guUHbAFWpOm27drqYPP7gPFAiF9IW2uLZVAUArI64QQQgghTpEAqxBCiCuKi78/YX/+M8ZVq2jetcu2sjTQnpFJVUYmVf/4B57Dhtlmts6cidrLy8E9Pn8Jg4bR0dJC5rZN1JYUAWC1WMg/uI/8g/twcXUjYchwUsdMIGHwsPM+vs5Vx+z42V3KNxVv6rzdam4lvTCd9MJ0XNQujAgbwZToKUyMnoi/u/8FnZdGoyYoypZ7Ne0s9YpVQeOiRq1RYT2VgxVAUegMup7YVQHA8KvjOVja0Nmmuc3Mp3uK+XBvMfFBXswfHMW8QRGE6NwvqK9CiB5EpYKIwbbtTCfW2IKrAJVHbdvG5yEgyRZoTZsLYQMk2CqEEEJc4VSKoig/3kz0JEajEb1ej8FgQKfTObo7F53JZGLNmjXMnDkTrVZWhBbCWV2OsW6qqqLx668xrFxF29Gj9pUaDUlbNuMSEHBJHvtyUBSFmqICMrdvJmvbZhprq+3qw5JTWPDcPy/a45ksJnZX7GZd4To2FG2gvr2+Sxu1Ss302On8fdzfL9rjnslislJb1mRLL1Bk22pLmrBaT3/kmfNYf6JS/TlQVM/He0vYtb+c6+tdqFUrVLhYqdRYqXSxkpISwAOTkhgWd2FBYfHD5D1ddHsNRXBsuW12a+n+s7fRR9sCrVOfk9yt30PGuhDOT8a5cEbnE1+TGaxCCCGuWNrgYPxvuw3/226jPT8f4+o1GFeupKOwEK9Ro7oEVw0rV+ES4I/n8OGoNN1/wSSVSkVQTBxBMXGMvek2SrMzydq2mRO7ttHWaCR19Hi79oqisOuzD4kfNIzg2Pjzvkxeq9EyJmIMYyLG8PsRv+dA1QHWF61nXeE6Kltsl+tbFSt+bn5d9q1oriDUK/TCT/Y7NFo1wTE6gmNOfwiyC7oWGgmO1qFSqRgc48/gGH8OBvix46OTBFlVBHWo6XtqP+veJg7lZdLUy5+gU+kFgmN9JIWAEFcK32gY83PbZiixBVozV0LhDji18B+GIls+1zODq4oiM1uFEEKIK4QEWIUQQgjALS6OoEceJvDhh2g7dpzOL86nKGYzlX/9K5baWjRBgehnzkQ3ew7ufXr3iGCbSq0mMqU3kSm9mXjHfRQePUhoQrJdm/KTJ9jx8Qfs+PgD/MMjSRkznpTR4/ELDT/vx9OoNQwNHcrQ0KH8euivOV57nHWF61hXtI4pMfaLY9W01jDt02kk+CYwNWYq85LmXbRga2d/7IKuEV3qPTxcCIj0pq6sGeU7M13VqDDVtpO5o5zMHeV4+7nR54FUjpUauXpAOL6erjTVt+Hh7YpGKzPXhHBq+kgY8aBta6qCrNWQuQLyt0DqXPu2igL/nQAhfWyzW+MngIubI3othBBCiMtAAqxCCCHEd6hUKjz69ulS3rxzF5baWgAs1TXUvbeEuveW4Bobi272bPSzZ+EaG3uZe3thNC4uxA8c2qU8a8fmztt1ZSWdwdbQxGRSR48neeRYvP3O/1J5lUpFn8A+9Answ88G/axL/cbijSgo5DTkkNOQw5tH3mRq7FRuS7uNPoFd/y8uhZQRYaSMCMPcYaGmtInqwkYqC42U5hlorm7rDLoGRfvwzvYCVh8p5/nVmUztHcLg7Hbaq9sIiPAmKMaH4FMzXf3DvdC4SNBVCKfkHQxD7rRtrfXAGT+0lR86vR16H1x9IHm6LdiaOAVce15+byGEEEJ8PwmwCiGEEOfAc+gQIl56CePqVTRt2oxiMgHQUVBAzSuvUPPKK7j36YN+zmx8b7wRtXvPWxxp5HU3ExARTdb2zZRkHussr8jJpiInm01L3iaqTz/6TpxKyhnpBc7V2Wb7+rn50T+oP4erDwNgVsx8lf8VX+V/xcDggdyadiuToiahUV/6tAwurhpC4/SExuk70wSYOizUltjSC2h8XEj//CAAHRYrXx0uJ9ngjguqznyvGaf2U7uoCIzw7kwtEN07AG8/mcEmhNPx6Jr2hNpc0HqBqdl2v6MRjn1q21w8IHEypF1tC7q66y9vf4UQQghx0UmAVQghhDgHand3dFdNR3fVdCwGA43p6RhWrqJlzx7bpaBA27FjmCoq8Fu40MG9vTAePjr6T51B/6kzMNZUc2LHFjK3b6a6IA8ARbFSdPQQPv4BFxxgPZspMVOYEjOFyuZKPsn+hI9PfNy5QNbBqoMcrDpIhHcEDw14iLkJc3/kaBef1lVDaLye0HhbEGRF5Gg+2VfCFwdLaW7s4ITWQohFTYBVheo7s9isZoWqwkaqChs5vrWMmQ/1swuwtjWZaKxvs8101chMVyGcSt/rIWUW5G60pRE4sQbaDLY6cytkrbJtXkHwixNwGX5AEkIIIcSlIwFWIYQQ4jxp9Hp8r78e3+uvx1RRgXHNVxhWraQ9IxPdzBmoXOzfXmteew3XxES8x49H7erqoF6fH11gEEPnXsfQuddRW1JE1vbNZG7fjKGyoktwtb2lhU1L3qTXqHFE9+6H+gIXAAvxCuGRgY9wT997WJ23mvcz3yenIQeA0qZS6tvqf/J5XQwpoTp+PzuNX1+VwsYTVXyyr5glJ6pRWxSCLWpCzSpCLGqG6rxpqWvrTOcbHO1jd5yCYzWsfzcTjYuagEhvgmN8Ome7+od5opagqxA9m9YDUmbaNnMHFGy1BVuzVkNzta1NwqSuwdUTX0FYf9Cdf/5rIYQQQjiGBFiFEEKIn0AbGkrAXXcScNedtOfmdkkNYK6upvrlV8BqRe3jg8/0aehnz8Fz6BBUFxiIvNwCIqMZfeOtjLrhFipysgmJT7Srz9m7k2Mb0zm2MR1PvS+9Ro0ldfQEQhOTL2gBMHcXd65Lvo55SfPYWbaTJZlLOFx1mHlJ8+zalTaVUtlcycDggQ5ZaMzVRc303qFM7x1KVWMbXxws5eN9JeyvaiIl1JNFPxuJqd1CTXEjtaXN5DS2EqyyEqb3AKCqsBEAi9lKVYGRqgJj57E1WjWBkd4ER/sQnuxH4uDgy35+QoiLyMXVlhYgcTLMehGKdkHmSkieZt+uvRE+vh0s7RA51LZ4Vuoc8I9zTL+FEEIIcU4kwCqEEEJcJG4JCV3KjGvXgtUKgLWxEcOnyzF8uhyXkBB0M2einzMbt9RUhwQIz5dKpSIsqVeX8pN7dnbebjE0cPCrlRz8aiX6kFBSR48nZfQEAiKjLujxRkWMYlTEKBraGvBxtZ8BuvjYYj468RG9A3pza9qtTIudhlatPf8TuwiCfdy5b1wC946N51BxAy0dFlQqFa7uLoQn+RGe5MeMf28lq8LI2KQg5g+OJCHGh6ShIVQXNdJQ2WJ3PIvJSmW+kcp8I4aa1i4B1sLjtfj4ueMb6ola3f2fO0KI71BrIHa0bTvTybW24CpAyV7blv57CO0LqVfbgq3BKZe3v0IIIYT4URJgFUIIIS4hvxtvxDU6GuOqVTSmr8PaYgukmSsrqVu8mLrFi3GNj0c/dw4B99/fIwKtZ5r1syfJP7iPrG2byTuwB4vZDIChsoJdn33Ers8+Iig2niGzriFt3KQLegxfd1+7+4Z2AytyVwBwvPY4T219ihf3v8jNKTczP3k+ejfHLBqjUqkYGN11wZtjpQYyy20zVLdkV7Mluxq9h5arB4Qzf3oqSf6e1BQ3UV3YSFWRkerCRgzVrQAEx+jsjqVYFb558ximNgsubhqCok4vpBUU7YNviARdheixwgfB+Kdss1urjp8urzhq2zb+CQKTbYHWib+V3K1CCCFENyEBViGEEOISUrm44D12LN5jx2J9tpWmjRsxrFxF09atcCoQ2ZGXR9OWrQQ+8ICDe3thtK5uJA8fTfLw0bQ1N5GzZyeZ2zdTfOwIimKbvVtdkEdjXe1Fe0xPF09+N+J3LM1YSlZdFgBVLVX8+8C/+e+R/zI3YS63pN5CrD72oj3mTxGsc+PxKUl8ur+Eknpb4NTQamLJzkKW7CwkJdSH+UOiuGZUOAOnRQPQ3mKiuqgRbz/7tBMNVS2Y2iwAmNstlOcYKM8xdNZr3TQERnkTHKOj/+QofPzt9xdCdGP+cTDxadtWk2PL2Zq5EsoOnG5Tkw3Za2Hy/zmun0IIIYSwI6snCCGEEJeJ2sMD3cyZRL32KklbtxD67LN4DhkCgG72LLu2itVK2a9/jeHLL7E0NTuiuxfE3cubPhOnMv93f+K+195l4u33EpqYDEDKqHF2bWuKCvj8738ka/tmTG1t5/U4Wo2WuQlz+Xj2x7wz/R0mRE1AhW3WZqu5lY9OfMScL+bw8PqHqW29eIHdCxXs487jU5LZ8uRElt0znGsHRuCuPf0xLKuikedWZTDu7xtp7bAFT908tUSm+OMb4ml3LFcPF0ZcE0/CoCB8AroGT02ngq6H1xejWBW7uuriRrL3VNBQ2dKlTgjRzQQmwtgn4L6N8PgxmP4XiB4FqGwzWM/03hxY/QvI2wwW82XvrhBCCHElkxmsQgghhAO4+Pnhd9ON+N10I6ayMtQ+9vlFWw8dwvDlCgxfrkDl7o7PpInoZs/Be8xoVK6uDur1+fH282fQzKsZNPNqjDXV6AKD7Oozt28mb/8e8vbvQevmTuLQEaSMGU9s/0Goz/GyV5VKxdDQoQwNHUqRsYj3M9/ni5wvaDXbZonm1Oc4LF3A2ajVKkYlBjIqMZA/XN2b1UfK+XhfMQeLGgCY0CsYD1f7c69r7sDf6/T/uZfejcFXxXbeb2sy2dIKFDVSVdhIdWEjjXVtuHm6dAnAntxTycH0IgBc3TUERfvYpRfQB3mgkvQCQnQ/vlEw8iHb1ljZNTVAVRbkb7Fte98CD39ImWnL2xo/HlzcHNNvIYQQ4gohAVYhhBDCwbTh4V3KmjZu6ryttLVhXPMVxjVfodHr8bnqKvRzZuMxaBAqdc+4GOXM4CpASebp/IKm9jYyt20ic9smfAKD6D9lBn0mTsXLt2s+0+8TrYvmN8N/wyMDH2F59nKWZS1jQeoCXNT2H3dW561mRNgIAjwCLvyELgKdu5abh0Vz87Bocqoa+WR/CeOT7f9ObSYLE/+5iYQgL+YPiWJ2vzB83O0X8nL31hKdFkB02unzaW3swFjb1iWnb1WRsfN2R5uF0uwGSrMbOstcPVwIivYmaUgIvcdGXMSzFUJcND4hXcsqj4HG7fQCWa11cPB92+amg+TpkDoXEieDq9fl7a8QQghxBVApiiLXhzkZo9GIXq/HYDCg0+l+fIcexmQysWbNGmbOnIlW65jVooUQl96VPtYVq5XWgwcxrFpF41dfY2lo6NLGJTwMvxtu6LG5WxWrlZKs42Rt20z2rm20NTfZ1as1LiQNH8XQudcREpdw3sc3W81YFAtumtMztwoMBcz5Yg6ualdmxc/ilrRbSPZL/snncqmsOFzGY/872HnfXatmZp8wrh8SyYi4gPNezKokq46KPINtpmtRI0317WdtN2BqNKOvS+y8rygKe1blExDuTXCMDz4B7hdlQbYrfZwLcVG1N8LJtZCxAk6mg+ks6WU8A+AX2aC5vPNsZKwL4fxknAtndD7xNZnB6sRMJhMmk8nR3bjovj0nZzw3IcRpMtZB268fgf36EfDkk7Ts3EnjqtU0b9qI0mrLV2ouK6etsKhH/41Ck1IITUph7K13UXj4IMc2pVNwaD8oClaLmRM7thA7YAj+kdEXdHw1akzW03+fJceXANBh7eDznM/5POdzhocOZ2GvhYwKH4Va1b1mBGtVCikh3mRV2oLPbSYrnx0s5bODpUT6eTBvYDjzBoYT4etxTscLSfAhJOF0OooWYwc1xU1UFzWe+reJFkMH/hEeds+rxto29q0u6Lzv5ulCYLQ3QVHeBEb7EBTljbe/23kHXWWcC3ERqd2h11zbZmpFlbcR9YlVqLK/RtVum71ujR6FxarAd14XVQVbUIJ724Kvl4iMdSGcn4xz4YzO5/ksM1idyKJFi1i0aBEWi4Xs7GyWLVuGp6fnj+8ohBCix1C1t+OdkYHPoUN4ZZ+k5O67aE08PdNQ3dpK+NKlNPbtR2O/vli9et6loKamRgw5mRhzT6BSqYi9+mZUmtP5BjsM9SiKgpuv/3kfu95az672Xexr30c79rM3g9RBjHQbyQDXAbiquk+eW0WBkmbYXaVmf42KFot9EFOFwpAghVsSrRfl8SztKlQahe9mVmgpd6Hu0A8HcdVaK1q9Ff/+rWi6z59PiCueymomsCmD8IZ9VOoHUKEf1FmntnYw4+jDaKwd1HinUO47lHLfwbRpzz09ixBCCOGsWlpaWLBgwTnNYJUAqxP6dgpzTU2N06YISE9PZ+rUqXLpgRBOTMb6j7PU1aHW6+2Cj8bPP6fq/56x3XFxwXPUKHxmzcRrwgTUPexHN7PJRH1ZCUExcXblq//9d3L37iS8Vxr9plxFwtARaFzO7znSbGpmRd4K/nfif5Q0ldjV6V31PDbgMa5NvPYnn8PF1m6ysC6rmuUHStmWW8u3n+IeGh/Pz6ck/vDOP0Fbs4mKPCM1Radnu7Y2dv1FX+um4fa/j7RLXZC9pxJDVWvnbFcvX9fOma4yzoVwLNWJNbh8eluXcmvEUJSU2VhTZoNvzE9+HBnrQjg/GefCGRmNRgIDAyVFwJVOq9U69Qubs5+fEMJGxvr304Z0Xeik/fDh03fMZlq2bKFlyxZUnp74TJ6Mfs5svEaORNUD/qZarRaPRPv8qI11NeTt3w1A2YkMyk5k4Kn3pe+kafSbchW6wOBzOrav1pfb+tzGwrSFbC7ZzNKMpeyr3AeAocOA3kPfLZ93Wq2WawZFcc2gKMoaWvnsQAnLD5Ry47Bou/6W1Ldw97v7uG5wBNcMjCDYx/2nPa6vFp9BniSdmvimKArNDe2duVxt/xrxDfHEzc1++mregRqKjtd13vfw0RIUrSM4xgf/CE8sbSoZ50I4SmhvGP0zW97W+vzOYnXpXijdi2b9MxDaD9LmwujHQfPTxqmMdSGcn4xz4UzO57ksM1idkCxyJYRwBjLWL4yiKLRnZWFYtQrj6jWYKyq6tNH4+xNw990E3H2XA3r403S0tXJ80zoOrV1DXWmxXZ1KpSZ+8FAGTJ1JTL+BqNTnl081ozaD9zPe50jNEb64+gtcvnON/PGa41Q0VzAhagIateYHjnL5KYrSJffpv9ed5F/rsgHQqFVM7BXE/CFRTEoJRqu5NHlmFUXB1G7B1d3Frmzxr7addbbrd3n7uTFkZiy9x0Zckr4JIX6EokDlcchcYQu2Vmfa1wckwiP74AIXt5P3dCGcn4xz4YxkkSshhBDiCqVSqXBPTcU9NZXgX/yCln37MK5chfGbb7AabYucWOrqQLk4+TovN1d3DwZeNYcB02dTknGUQ+lfkbNnB1aLBUWxkrtvN7n7duMbEsbNf/onnjr9OR87LSCNP4/9MyaLyS64CvD64dfZVLKJSO9IFqYu5Nqka/HSdo/8tmdbWCq/pqnztsWqsC6zinWZVQR4uXLNwAhuGBJFr1CfLvv91H58N7j6rflPD6Wq0Eh1YSNVRY1UFzbS1mwfcG2qb0etsT+P9hYTGdvLCU/0JTDaG80lCgwLIbAFTkP72LaJv4Gak6eDreWHIHVO1+DqhwvBPw5S50LEEDjPH7WEEEIIZyIBViGEEMJJqdRqvIYNw2vYMEJ+/zuat27FsGoVTZs2o5s1y65t24kTlP36KfRzZqObORNtWJiDen1uVCoVUb37EdW7H031dRzd8A1H1n1NU10tAG5eXnj4XNhVHNozLoEtNhazuWQzACVNJfxt799YdGgR1yVdx4LUBYR7h/+0k7kEXrppII9NTuLT/SUsP1BCpdG2oFdtcwdvb8vn7W359IvU89ikJKakdU01cbGoVCp8/N3x8XcnYaAtfYOiKDTWtlFd1EhFfgOZ+wswN7oSluhrt295roEdy3MAcHFVExqvJyxBT1iSL6FxerRu3WsmsRBOJTAJxv7CtjUUgfqM2Wh1+ZC1ynZ7x8vgEw6ps22B2OhRoJGvmUIIIa4s8s4nhBBCXAHUrq74TJ6Mz+TJWNvaULvb5+Q0rlxJe1YWVVlZVP3zBTyHDEE3Zza6adPQ+Po6ptPnyNvPn5HX3czwa24gd/9uDqd/RcqocXYzOxVF4atFLxKV1peU0ePQup17TtIInwgWTV7E0oyl7CzfCUCTqYn3Mt5jaeZSpkRP4da0WxkQPOBin9pPEh/kza+uSuEX03qx9WQ1n+wrIT2jkg6LbfbykRIDxrYfvnT/UlCpVOgCPdAFehDd148al0ymTxuPm7t97tbynIbO2+YOKyVZ9ZRk1QOgVqsIjPYhPFFPRLIfsf0CL+cpCHFl8Y3uWlZ2EFQaUCy2+41lsOe/ts0zAFJm2Wa2xo0HF9eu+wshhBBORgKsQgghxBXmzOAqQEdh0ek7ikLL3r207N1LxXN/wnvcOPRzZuM9YcJZ9+0u1BoNScNGkTRsFGemmC8/mUXm1o1kbt3I5vffpve4yfSfNhP/8MgfP65KzdjIsYyNHMvJ+pO8n/k+q3JX0WHtwKpYWVu4lrWFa+kX2I/Xp76Oj+vFvfT+p9KoVUzoFcyEXsHUN3ew4nAZH+8rprC2hRl97Gcq7yuoY0t2NdcPjiI6wPPy9dFF3SXVQerocHwCPCg72UB5TgNN9e2ddVarQlWBkaoCI6XZDV0CrG3NJty9JP+bEJdMn3kQPwFOfGVLJZC7ASwdtrqWWjiwxLZ5BcHPM4ALy90qhBBC9BQSYBVCCCEEkS//h46CAgyrV2NcuYqOggJbhclE0/r1NK1fj9rLi+Bf/gK/m292aF/PxZnBuvyD+zpvtzc3c+CrFRz4agXRffozYNosEoYMR6358UvOk/yS+MOoP/DYwMf4OPtjPsr6iNq22s7H7G7B1TP5ebly+6hYbh8VS6WxDQ9X+3NeuquQLw+V8Z8NOYyI92f+4Chm9A3F0/Xyf2T0DfbEN9iTPuNsC18Za1spzzFQltNA+ckG6itaAAg/I7UAwMfP70VRFMISfQlP8iUsUY9/qBcqtQR5hLhoPP1h4ELb1maEk2ttwdaT6WCyjU/CB9pmsJq+M1u+zQDu554fWwghhOgJJMAqhBBCCABcY2MJevhhAh96iLbjGRhXrsS4Zg3m6moArM3NuAQH2+2jWCyg7jr7sLsZfeOtJAwezqG1azixYwtmk22mVdGxwxQdO4y3fwB9J02n3+TpePsH/OjxAjwCeLD/g9zd527W5K9hacZSbk271a6Noii8efRNpsdOJ0YXc0nO66cI0dnPRm4zWViXUdl5f1deHbvy6nhmxXFm9wtj/pBIBkX7Oez/WhfggS7Ag17DQwFobeqgPMeALtD+PBrr2misawPg5N5KTu61nZOblwthCbZga3iiL0ExPrJwlhAXi7sO+l5v2zpabDNaM1dA0jT7dlYzvDrKtjjWsHuh1yzJ1yqEEMIpqJQzr6ETPZ7RaESv12MwGNDpLmyBj+7MZDKxZs0aZs6ciVYrl/8J4axkrHcPisVCy549GFatomX3HhLWrEblejqfnvHrr6n617/Qz5qNbvZs3OLjHNjbc9Pa1MjxTes4nL6Ghopyu7peI8cy+/Ffn/cxFUVBQUGtOh2w21O+h7vX3o0KFeMjx3Nr2q0MDR3arYPRVcY2PjtYysf7ismrbu5SHx/kxfWDI7lhSBSB3m4/+fEuxTivLW1i68cnqcw3YO6wfm87F62a+U8PxT/c66I8rhDi+3071mfFW3FZfsfpCp9wGHwHDL4dfEId1T0hxEUgn92FMzqf+Jr8XCiEEEKI76XSaPAaORKvkSNRrFZUavsZf4ZVqzAVFlHz6qvUvPoq7r172xbHmjETbUjw9xzVsTy8fRgy+1oGz7yawqOHOLR2DXn796AoVvpPm2nX1mwyYTF14Ob5w0E4lUqF6owcgx+e+BAABYVNJZvYVLKJFP8Ubkm9hRlxM3DVdL+FX4J17jwwPoH7x8VzoKiBT/YVs+pIOU3tZgDyqpv5+9cnGBkfcFECrJdCQIQ31/x8IBaLleqiRspPnkorkNtAe7PZrq0+2MPufvbeCqoKGwlPtM109fDufv9HQvRsKvBPgLpc293GMtj0Z9jyd9uiWMPuheiR0I1/iBJCCCHORgKsQgghhDgnZwZXFasVpaPD9kX41AUxbceP03b8OFV/+zueI4ajnz0Hn2lT0fh0v9ykKrWa2P6DiO0/CGNNNdm7thGZ2seuTfaubaS/+QppYybSf9pMgmPjz/n4z4x8ht4BvVmWtYyqlioAsuqy+N323/HSgZe4qddN3NDrBvzc/S7qeV0MKpWKwTF+DI7x4//mpPH1sQo+3lfMrrw6koK9GRDla9d+T34dXm4aeod3n7yKGo2a0Dg9oXF6Bk6LRrEq1FU0U55joDynwdbGxf45fXJvFQVHaji8rhgAv1BPwpJ8OwOuugCPMx9GCHEelJRZ0Hsu5G+CPW9B9legWG2pA45/ZtuCe8OoR2FA98/3LYQQQnxLUgQ4IUkRIIRwBjLWew5TZRXGNWswrlpF2/HjXepVrq6E/fnP6GfPckDvfpr//f5JyrIzO++HJacwYNoskoePxsX13GY3mqwm0gvSWZqxlGO1x+zq3DRuPD3saa5Lvu6i9vtSKaptoaqxjSGx/nblc17extFSA2lhOm4YEsnVAyLw8/rxv093GueKovDOk9toazJ9bxtvPzfCEn1JHR1GVIr/97YTQtj73rHeUAz7F8P+96Cl5nT58Adgxt8uf0eFEBesO72nC3GxnE98TTL7CyGEEOIn0YYEE3DnHcQt/5T4NasJfOhBtNHRnfVKRwfuaal2+1hbW20LZHVjFrOZ4Lh4XD1Oz1osz87iq1de4L8P3cGWZe9iqKr40eNo1Vpmxs9k2axlLJmxhKkxUztztbZb2kn0S7xk53CxRQd4dgmuZlUYOVpqACCj3MizKzMY/uf1PPTBfjaeqMJi7Rm/5atUKhY8O5wZD/RlwJQogmN1qNT2lyk31bdzcm8lxupWu3JTh4WKPAMW8/fnfBVCnIVvFEz+P3giA+a9BVHDbeVD7rZv11IHGSvAYu56DCGEEKIbkBQBQgghhLho3OLjCXrsMQIffZS2o0cxrFxFR2EBbvH2l9bXvfsu9f/7EN3Mmehmz8a9d1q3W/xJ4+LC5LseZOzNt5O5bROH1q6hpqgAgNZGI3u//JS9K5YTN2Aw42+5i4DI6B88nkqlYmDwQAYGD6SksYRlWcvIa8ijf1B/u3abizdT1VrFnPg5uLu4X6rTu2gifD340zV9+GRfMYdLbIHWDouVNUcrWHO0ghCdG/MGRTJ/cCTxQd4O7u0P8/B2JX5AEPEDggAwtVuoyDdQfrKBshxD58JZYYm+dvuV5zSw8j+HcdGqCYnXEZZoSysQEqfD1V0+bgvxo1zcoN9821abCwEJ9vUH34f039sWxRpyJwy6HXxCHNNXIYQQ4iwkRYATkhQBQghnIGPdeSmKQt6s2XTk5XWWucbFoZs9C/2cObhG/3Cg0lEURaH0RAaH164he9d2rN/OpFKpuPflt9EFnf+iXoqi2AWWFUXhhlU3kFWXha+bL/OT53Nzys0EeQZdrNO4pE5UNPLp/mI+P1hKTVOHXZ1Wo2Lfb6ei9zw9nnvaOLdYrNQUNREc62P3/7Z7RR771hR0aa9SqwiK8rbL4yoLZ4kr0U8a61YrvDwQ6gtOl6ldZFEsIbqZnvaeLsS5kBQBQgghhOi2rM0tuMbGwnc+fHfk51Pz8ivkTptOyaOP0XYi23Ed/B4qlYrIlN7MeuxJ7nt1MWNuug2fwCDiBw3tElw9uWcHpScy+bHfsc+ctZtRl0FWXRYADe0NvHn0TaYtn8Zvt/22s7w76xXqw29npbHz6cn899bBTE0LQXPqMvuJvYLtgqsAxfUt9KSf+jUaNSFxui7/byGxOpKHheDt72ZXrlgVqgobObyumK9eP8qX/zp0GXsrhJNQqWDWi9BrJpxKr9K5KNbiGfDaaNj7NrQ3ObafQgghrmhyzZIQQgghLiuNtxdRry7C0tCA8Zu1GFetomXv3s76xvR0GtPT8bnqKkJ+9STa8HAH9vbsvHz9GH7tDQy9+jramuy/1FvMZja88zpN9XUERcfSf9osUsdOwNX9x1eg7x3Qmw9nfciSjCWsLViLWTFjtppZkbuCFbkrGBo6lFtTb2V81PjOPK7dkVajZlrvUKb1DqW6sZ0vDpbSJ0Jv16bdbOG613ejsWgo0+ezcHhclwBsTxHbL5DYfoEANNa1UXaygfKcBspzDdSVNXe2C0vUd9n3ixcP4Kl3IzxRT1iiL/5hXl1yvwpxRVOpIHGybWsogn2L4cCS04tiVR2H1U/AumfhpmUQN9ah3RVCCHFlkhQBTkhSBAghnIGM9SuLqbwcw8pV1C1dgqXa9qVZ5e5O4rp0XAIDHdy783Nyzw5WvPBnuzJXDw/Sxk1mwLSZP5qr9VuVzZX8L+t/fJL9CcYOo11dnD6OT+d8iqum515uvuZoOQ99cKDzvodWw3WDI7hjVByJwd07V+v5aGsyUZ5ry+EaneZPVOrpRcKaDe28++vtdu3dPF0IS/QlLEFPeJIvQdE+aFy6bzBdiHNx0d/Tze1w/AvY+yaUnPqBTusJT2SCh+9PP74Q4rzJZ3fhjM4nviYzWIUQQgjhcNqwMALvuxf/W2+h/qOPqH3zLfRXX90luGppakbj7eWgXp6b2AGDueqhn3N47RrKc04A0NHayqFvVnHom1VEpvVhwLRZJA4dgcbl+7+AhHiF8Pjgx7mv332szF3J+5nvU2AsACDJN6lHB1cB/DxdGZXgz47cOgBaTRbe31XE+7uKGJccxJ2jYxmfFIS6h8/mdPfWEtc/iLj+XfPoNlS04OKqxtxh7SxrbzFTcKSGgiO2HxpctLa0BJPvSMPHv/sveibEZeHiBv1vtG1lh2DvW+Dq3TW4uvEvoNbIolhCCCEuOZnB6oRkBqsQwhnIWL+yWVtaUCwWND4+nWWWpiZyp03He+wYAh980JbHtZurzMvhcPoaMrdtxtzRblfnHxHFHf9chEp9brMTrYqVbaXbWJqxlIcHPMyA4AGddSaLied3P8+1SdfSP6j/xTyFS8pkMvHu8jUUucWx/GAZLR0Wu/r4IC8eHJ/A/CFRDurhpfftwlllOafSCuQYaGs22bVRu6i491/jcNFqOsvKTtbT2mQiPNEXD5+eHWwXzs8h7+ltBnghFUzNoNZC2lwYei9Ej5BFsYS4BOSzu3BGMoNVCCGEED2a2tOzS1ndkiVY6uowfLkCw6rV6OfOJfDBB3CNPrdL7h0hJD6Rafc/xrhb7iJj83oOpX9FfVkJALH9B51zcBVArVIzLnIc4yLHdan7uuBrlp9czvKTy+kX1I/b0m5jcvRkXNTd/6NesAfcMTOVJ2ek8vHeYt7dUUBJfSsAedXNnKhodHAPL61vF84KidMxcGo0ilWhvqKlM+BaltOAj7+7XXAV4MiGEnIPVgPgG+JJeJJvZx5XnwD3LgtxCXHFKdwBZttrCVYTHFtu20L6wNC7oe8N4OY86UiEEEI4Vvf/1C2EEEIIAWh0ejR6PRaDASwWDJ9/jmHFCvTXXG2b0RoZ6egufi93L28GzbyagTPmUnz8CIfWrqb/1Bl2bVqMBj557rf0mTCF3uOn4O597l/8V+Wt6rx9pPoIv9z8S8K9wlmQuoB5SfPwcfX5gb27B527lnvGxnPn6DjWZVayeHs+e/LruH1UrF27+uYOTlQ2MjzO3ymDiCq1Cv9wL/zDvegzLgKAjjazXRtFUSjLaei831DZQkNlCxnbygDw9nMjLNEWcI3uHYAu8McXWBPC6fSaAT87DPveObUoVq2tvPIYrPo5pD8D/W+GofdAULJj+yqEEKLHkxQBTkhSBAghnIGMdXE2lqYm6t9/n9rF72I1GE5XuLjge+21BD5wP9qICMd18CfYu2I5Wz5YDICLqxspo8cxYNosQuITf3Tfdks7a/LWsCRjCTkNOXZ1ni6eXJt0LQtTFhKl616X2v/YOC+pbyHSz34286KNOfzjmxOkhum4c3Qsc/uH437G7E5nZ7UqFB6rpfykbYZrdWEjVuvZP9KPuymZvhNO//hgtVhRFGThLHFZOfw9/WyLYn3LNxoeOwzncUWBEKIrh49zIS4BSREghBBCCKek8fYm8IEH8Fu4kLqlS6l79z2sRiOYzTR88gkNX3xB5L9fwmfSJEd39bzVlhZ33jZ3tHNsYzrHNqYTmpBE/2mz6DVqLFpXt7Pu66Zx49qka7km8Rp2le9iacZStpZuBaDF3MIHmR+wLHMZfxj1B65NuvaynM/FcGZw1WSxsnRnIQCZ5UZ+9ekR/vZVFguHR3PLiBiCdVfGIlBqtYq4foHE9bMtAmfqsFCZb7SlFDjZQEW+EXO7LZ9tWKKv3b5lOQZWv3KYkDjdqVmuvoTE63B1l68Fwol1WRTrTTj6KZjbYMhdXYOrrQ1dF8wSQgghfoB8khJCCCFEj6Px8SHooYfwv+UW6t5bQt1772FtakLt5obn4MGO7t4FuerBxxk65zoOr1vD8U3r6WhtAaAi9yQVr73E5iVv0XviVAZMnYlvaNhZj6FSqRgZPpKR4SPJM+TxQcYHrMhdQZulDZVKxbCwYZfzlC46FfD0zBTe2V7A4eIGAGqbO/jPhhxe25zLrL5h3Dk6jv5Rvo7s5mWnddUQ2cuPyF5+wKmFs4qbqMg14B/uZde2PKcBs8lKaXYDpdkNgC0tQWCkN+GJvoQl6QlL8MVTJwtnCScVPgCuXgRTn4NDy6D/Tfb1tbnw6ghInSOLYgkhhDhnEmAVQgghRI+l0ekIevQR/G+7lbr33kPt5YVGr7dr07hhI+6909CGhDiol+cuIDKKSXfcz9ibbidz+2YOrV1NdUEeAG3NTexf9Tn6oGAGXjXnR48Vr4/n9yN/z2ODHuOT7E+obqkmwts+fcLHJz6mob2BG5JvwNfd91Kc0kXlolFz9YAIrh4QwYGiehZvL+Cro+WYrQomi8IXh8r44lAZg6J9+ef8/sQHXZkL2Gg0akJidYTEdr2UTeOixsffnca6ts4yxapQXdRIdVEjhzfYZlLH9A1g9sP9L1ufhbjsPP1h1CNdy/e+DZaOMxbFugf63QCuXl3bCyGEEEiAVQghhBBOQKPXE/TYY13KzfX1lP3ylyhmM7433kjAvfegDQ52QA/Pj9bdnX6Tp9N30jTKT57g8NrVnNi5FbXGhbRx9ukPGutqUKs1ePn6nfVYejc99/S9p0u5yWLi9cOvU91azZtH3mROwhxuSbuFeH38JTmni21QtB+Dov0on5nC0p2F/G9PEfUtJgCyK5sI8jl7OoUr3aDpMQyaHkNjXZstpUCOgfKcBurKmu3aeem7/v22f3oSXaAH4Um++Id5oVLLrD7hhDz9wcMfWuts9yuPwarHIf3/YMACW7A1MMmhXRRCCNH9SIBVCCGEEE6rbvG7WFtsl9rXL11Kw8cf43fTjQTccw8uQUEO7t2PU6lUhCenEJ6cwvjb7qEqLwc3T/sZVLuWf8ixjekkDRtF/2kziUztg+ocLmc9VH2ImtYaANosbXyS/QmfZH/CmIgx3Jp2KyPDRp7TcRwtTO/Br65K4bHJSXxxsJTF2wsYnRiIj7v9AhtfHCwlLVxHcoiPg3ravfj4u+MzLJTkYaEAtDWbKM81dC6c9W26gW+1NZk4tO50nmA3TxfCEvS2PK5JvgRF+8jCWcI5jPsljHwEMr6APW9C6T5bebsRdr9u2+InwPhfQ8woR/ZUCCFENyIBViGEEEI4Lf87bkexmKlf9j+U1laU9nbq3ltC/Ucf43fzzQTcczcuAQGO7uY58dTpiR1gn1+2vaWFzK2bsFosnNi5lRM7txIQGU3/aTNJGzsJN0/Psx8MGBo6lNXzVrMscxmfnfyMFrMtEL2tdBvbSreR6JvIrWm3Mit+Fm6a7j8b1F2r4aZh0dw4NIp2s9WurqGlg6c+O0KbycroxADuGh3HxF7BqGUGZid3L63dwllnKstpsLvf3mKm4GgtBUdrAdBobWkJwpN86T85CncvWUFa9GBad1tu1v43QdlB2PvW6UWxAPI2weA7HNlDIYQQ3Yz8zCyEEEIIp+Xi70/Ik0+SmL4W/zvuQOVmCxQqbW3ULV5MzpSpVP7jH5jr6hzc0wtjtVoYOGMOHrrTeWdrS4rY8M7rvPHAbax7axHVhfnfu3+UTxS/HvZr1s1fxy+H/JJwr/DOupyGHJ7Z8QzzvpyHxWq5pOdxMalUKty1Gruyj/YW02ayBV2359Ry93v7mPTCJhZvz6ep3eyIbvY4sX0DmP/0EEZfn0j8gCDcve0DqBaTlbKTDRz4uhCN1v4rhrGmlRZjx+XsrhAXT/hA26JYT2TCtD+BXxx4h0LKbPt25UegcCcoimP6KYQQwqFkBqsQQgghnJ5LYCAhT/2agLvvovatt6j/34coHR0ora3Uvf0OGr0vgffd6+hunjcPbx/G3nw7I69fwMnd2zm0dg1lJzIAMLW3cTj9Kw6nf0V4rzTmPvH09+Zp9XH14fbet7MwdSEbijawNGMph6oPATA+ajwateas+/UUC4ZHo9WoeW9nAYW1tpm6BbUt/GFlBi+szWb+kEjuGBVLTIAsYPN91Bo1wTE6gmN0DJgCiqLQUNlC2ckGynMMlOU00FjbRlCMD1pX++fLri9yObmvCt8QT8IT9YQl+RKW4Isu0L1HpKEQAji1KNajMOJhaCgEzRmztDf9FU6slkWxhBDiCiUBViGEEEJcMVyCggh5+mn877qb2jffpOHjj1F7eeG3YIGju/aTuGi1pI6ZQOqYCVQV5HE4fQ2ZWzdharddztpcX4uHruuK8l2Oo3ZhWuw0psVO42j1UZZmLmVBiv3fpqGtgae3Pc2NvW5kXOQ41Kruf0GUj7uWu8bEcfuoWDZmVbF4Rz7bc2yXtje1m1m8vYB3dxRw24gY/nB1Hwf3tmdQqVT4hXrhF+pF77ERADTVt9HaZLJrpygKZTkGABoqW2iobCFjezkAHjpXwuL1hMTrCIvXExTjg4u2ZwfzxRVArQb/OPuyhmLI/sp2u3NRrGdOLYp1tyyKJYQQV4Du/4n4CtHU1MSzzz7L7NmzCQ0NRaVScccddzi6W0IIIYRT0oYEE/q735KQvpaIF/6Jxtt+llHVv/9N9X/+g8VgcFAPL1xwbDxT732E+19fwqS7HiAgMpp+U2agPmMW6vaPPyD/4D4Uq/Wsx+kb1Je/j/s7kT6RduWfnvyUbaXbeHTDo1z9xdV8mPUhLaaWS3Y+F5NGrWJKWggf3DOCrx8fy01Do3A7tTCTokC0zGD9Sbz93AmKsl9EzGpRSB4aQmi8DrXGfrZqq7GDvEPV7Pwsl8/+eYDjW8vs6hW51Fr0FF5BcM1rEDHkdFm7AXa/Bq8MgSVXQ+YqsEhKEiGEcFYyg7WbqKmp4Q9/+ANhYWEMGTKE1atXO7pLQgghhNPThoSgDQmxKzNVVlH39jsoHR3ULX0f/9tvx//229D49KzV5908PRk4fTYDps3CesaX+rqyEnYt/x8A+pBQ+k+ZQe8JU/D8Ti7X77OjbEfn7QJjAc/vfp6XD77M9cnXc3PKzYR6hV7cE7lEUkJ1/PW6fvzqqhT+t6eIzw6UMH+IfTA5t7qJzw6UcOuIWEL17g7qac+mcVEz6rpEAEwdFqryjZTlNFCRZ6Qy30B7y+nnZliC/fOvPKeBde9mEhqvJzReT1iCnoAIL9QamSMiuplzWRQrbxPoIuH+LeDVMxZXFEIIce4kwNpNhIWFUVJSQkREBG1tbXh4eDi6S0IIIcQVqWXf3s5ZndbGRmpeeYW6JUvwv+N2/G+7DY23t4N7eH5UKhUaF/tcgZlbN3beNlRWsOWDxWz/+H16jRzLgGmzCE1M/t7cmG9Ne4stJVtYmrGUPRV7ADB2GHnn2DssOb6EqbFTuS3tNvoE9oxL7f29XHl4YiIPTUjocs6Lt+fz/q4i3ticx8y+Ydw5OpaB0WfPYyt+nNZVQ0QvPyJ62f6GilWhvqKFijwDFfkGAiLtx1Z5roHG2jYaa9s4ubcSABdXNSFxus6ga2i8HncvbZfHEsJhvl0Ua+pzcOgD2Ps21J9abNAnVIKrQgjhpCTA2k24ubkRERHh6G4IIYQQVzz9rFl49O9PzeuvY/j8C7BYsBqN1PznZerfW4L/nXfid8stXdIK9CQjr19ASHwSh9aupvDIQQAsJhMZWzaQsWUDwXEJDJg2i5TR49C62c/cVKvUTIiawISoCWTVZbE0Yylf5X+FyWrCrJj5Kv8rvsr/ir+O/Suz4mc54vQuyJnB1TaThRWHbJesm60KKw6XseJwGQOifLlzdCwz+4ahlZmUP4lKrcI/3Av/cC/SxoR3qe9os+CiVWM2nU5jYe6wUnqigdITDZ1lMX0DmP1w/8vRZSHO3XcXxcrdAHvfhN7z7NsoCnx6F8SNk0WxhBCih5NPhUIIIYQQZ3CNjCT8T38i4euv0M+bBxpb/lKLwUD1Sy+RO2UK9R9+5OBeXji1RkPi0BFc/9vnuOulNxg86xrcvU7PHqzKz2XtG/9h1b///oPHSfFP4fkxz7P2+rXc3+9+/NxsMxO9tF6Mjxx/Sc/hUnPXakh/YjyPTkrE38u1s/xQcQM/+/AQY/62gUUbc6hr7nBgL53byGsSuOelccx/eghjb0wiaUgw3v5uXdp5+Lh2Kdu07AT71hRQcqKejjbJeykcSK2GpCmw4CPof6N9XdFOOP6ZbVGsF1Lhq6egJsch3RRCCPHTOOUM1paWFjZv3sz+/fs5cOAA+/fvp6ioCIBnnnmGZ5999keP0djYyAsvvMDy5cvJz89Ho9GQnJzMTTfdxKOPPoqra9cPckIIIYRwLq5RUYT/+XkC77+Pmldfw7ByJVitWBoaUDqcI7DmFxbBhNvuYfRNt3Jix1YOr11NRe5JAHqPn2zXVrFaURQFtcZ+waxAj0AeGfgI9/S9hzX5a2jqaMLb1f5y7xf3v4jJYmJB6gKifKIu7UldJCE6d34xrRcPT0xkxeEyFm8vILPcCEClsZ1/fHOC/6w/yfIHR9En4sfz14rzp9GoCY7RERyjo99E2/Omqb6NijwjFbkGyvMMhCf62u3T3mLi+JbSzvsqFQREehMWryc0wZZWwCfA/XvTYAhx2eSsP33720Wxdr8G8RNh6D2QfBVonPIruxBCOB2nfLXes2cPM2fOvOD9CwsLmTBhAgUFBQB4enrS3t7Ovn372LdvHx988AHr16/Hz69rDi5FUWhvbz+nx1Gr1RKoFUIIIXoA15gYwv/2VwIeuJ+a116jZe8+fG+8wa6Nub4etbs76h6aR13r6kafCVPoM2EKFTnZZGzbSMLg4XZtCo8c5Js3/kPfSdPpN3k63v72uQTdXdyZl3TGJbCAod3A/zL/R5uljWVZy5gYNZFb026lr1/fS3pOF4u7VsMNQ6KYPziSXXl1LN6eT3pmJYoCQT5upIbpHN3FK4q3nzuJg91JHBx81vrKAqPdfUWBmuImaoqbOLrZFnj11LsSGq9nzPwkfPxlATPhIJN/DymzbHlaj313UayNtk0XCUPuhEG3g3eQY/sqhBDiBzllgBXAz8+PQYMGdW4///nPqaio+NH9zGYzc+bMoaCggLCwMJYsWcKUKVOwWq188skn3HvvvRw8eJBbbrmF1atXd9m/sLCQuLi4c+pj7969OXbs2HmfmxBCCCEcwy0ujoi//x1rSwtqN/tLlatf/BeNGzcSeO89+N54I2r3nhu0CU1MJjQxuUv5ofQ1NNXVsvPTZez67EOSho6k/7RZRPXu+4OzAY/WHEVBAcCqWFlftJ71RetJ9U+ld0dvplqnoqX7L1SkUqkYmRDAyIQAiutaeG9HAbGBXmjU9uf+uy+OEhvgxQ1Do9C5d//zcjZRqf4s/MMIKvINVOQaqMgzUFvWzKmnIAAthg7yD1Uz+fZUu33Lcw20GjsIidfhpe+ajkCIiy5ikG2b9u2iWG9BfYGtzlgCG56D6hNw3ZsO7aYQQogf5pQB1rFjx1JXV2dX9tRTT53Tvu+99x5Hjx4FYPny5YwcORKwzTa98cYbsVqtLFiwgDVr1rB+/XomT7a/dC4wMJDFixef02OdbQasEEIIIbo/taen3f2OkhIaPv8czGYq//JXat96m4D77sP3hvldArE9ldVqQa3RoFKpURQritVK9u7tZO/ejn94JP2nzaL3+Em4eXZdpGVMxBjSr0/nk+xP+DDrQ6pbqwHIrMskk0wOfnWQZ0Y9w8DggZf7tC5YlL8nv5ud1qU8r7qJ93fZUlP9Kz2b+UOiuH1ULHGBsnjN5aJSqfAN8cQ3xJOUEWEAtLeaqfw24JpvpDLPgC7IA1d3+69Dx7aUkL27EgBdoDuhCfrO1AL+4d6o1ZJWQFwidotirYc9b8LJtYACQ++2b2tuB6tZFsUSQohuxCkDrJoz8oKdj/feew+AiRMndgZXv+umm27it7/9Lfn5+SxZsqRLgNXb25s77rjjgh9fCCGEED2Tz+TJNH7zDQDm6moqn3+e2rfeIuD++/C9/nrUPTwtkFqtYe4Tv8FYU83R9V9zZP03tBgaAKgrK2Hju2+w9X/vkjpmAsOuno9vSKjd/n7uftzX7z7u7H0nXxd8zdKMpWTWZQKQa8jltq9uY2HqQp4adm4/indX23JqOm83d1h4d0cB7+0sYGKvYO4cHcuYxEDJ/ekAbh4uRKcFEJ1mS2thtSq0NnbNo1yRa+i8baxpw1jT1hlw1bppCInTEZqgJ75/EEHRPpen8+LKolZD0lTbVl8AmSshyj5dC0c/hW+ehgELYcjdEJjokK4KIYQ4Te3oDnQnLS0tbN++HYAZM2actY1KpeKqq64CYO3atZetb0IIIYTovlwjI4n890vEffE5PlOndJabKyup/ONz5E6/ivoPP3SKhbF0gUGMvvFW7nt1MbMf/zWRaX0668zt7Rxd/w0drS3fu79Wo2VOwhw+mv0R/538X8I14Z11Ae4B37tfT3HbyFjWPTGOhcOj8dDafvRXFNiQVcWtb+9h+ktbWLa7iNYOi4N7emVTq1VdUgAoisKY+UkMmh5NWKIejdb+q5Kp3UJJVj37VhdQcqLers5ittJQ2YKiKAhx0fjF2ma1nvmjzN43oc0Au16FVwbDkmsgazVY5XVFCCEcxSlnsF6ozMxMrFYrAH369Pnedt/WVVRUUFdXh7+//0V5/FdeeYWGhgbMZjMAR44c4U9/+hMA48aNY9y4cWfdr7293W5hLaPRltjfZDJhMpkuSt+6k2/PyRnPTQhxmox10RNpEhIIefFFfLOyqHv1NZo3bgTAXF5OxbN/oGHVaiIXv+PgXl488UNGED9kBLUlRRxd/zWZWzcREBmFX0SU3dgtz87C09cXfbD9rNb+/v15wPsBGuIaSC9OZ2HyQqcY8zF+7jw7O4XHJyXw8f4S3t9dTLnBtnhNdmUTv/n8KGuPl/PmrYMc3FNxpsg0XyLTfAFb0LS2pJnKfGPn1txg+5EkKMbL7rlamW/kyxcP4+blYpvlGqcjJF5HULQ3Lq4XfnWds5D39IvI3IYmMAVVVSaqMxbFUnSRWAfdgXXAQvCSRbHE5SXjXDij83k+q5Qr5GfW2NhYCgsLeeaZZ3j22WfP2mblypXMnTsXgMOHD9OvX7+ztvvyyy+55pprADh69OgPBmMvpI9n80P9fvbZZ/nDH/7QpXzZsmV4npEjTgghhBCXj1tJCQHr1uGdmQVAxXXXYRw21MG9unSspg7MrS246nw7yxRFoXjNcjoM9XiGRaFPTsUzLAqV2n52oKIoXS6dX9e6DrVKzTi3cbioeu68AIsCR+tUbC5Xk9doO8c7ky0MCDj9MfzbT+SSPaB7M7eq6GjQ4BFsRvWduGljvhZD1lkWtlMpaHVW3HwtuPpZcPW14OJxRXz9EpeY1txIdO1W4mo24NVRZVdnUblQ5juMrLB5tLgFO6iHQgjR87W0tLBgwQIMBgM6ne4H2/bcT6qXQGNjY+ftHwpMfrfuu/v8VAUFBRe039NPP80TTzzRed9oNBIVFcW0adN+9AnQE5lMJtLT05k6dSparazMK4SzkrEunMZ999F27BiGjz8h4be/QfWd53NHQQFtBw/hM2c2Khfn/FhWmnWcXIPtcuqW8mJayovxCQyi76TpJI0ez/bde846zrPrs3nm62ewKBZytbn8ZthvGBrSc4PTc4DfAMdKjXx+qIxfXZWMi+Z0kHlHbi3/WHuS20dGM7NPKK4uksmrJ8k/XMMJl0oq84y0t5hPVygqTAYNJoMGCsEn0J2bn7F/Hp/txwVnI+/pl8qNoFgx525Avf9tVDnrUKGgUcxENuwi9JbXQBfh6E6KK4SMc+GMvr1C/Fw45yf5K4ybmxtuZ1mhWKvVOvULm7OfnxDCRsa6cAbagQPxGTiwS3nVG//FuGoV9W+9ReBDD6Kf7XyB1uCYOMbcfDtH1n2Fsdo2y6qxppodH7/Prs8+xDMiBmPf3oTG2y/SktGQgQpb0KmwsZD719/PnPg5/HLoL/F3vzjpmRxhYGwAA2O75ppduruYY2VGnlx+jL+vPcktw2NYOCKaQO+un/FE95M8JIzkIWEoikJDZQsVeQYqcg2U5xmpL2/ubBeWoO/ynrb87/tRqW11ofG2zcOnZy+K933kPf0SSZ1h2+ryYf9iOLAUVcwotAGx9u1OrgP/OAhIcEg3xZVBxrlwJufzXHauT/A/kY/P6ZVAW1q+f3GG79Z9dx8hhBBCiHNlKi3FuHq17XZREeVPPU3ta68T+PBD6GbNQqVxjryNnjo9w6+Zz9C588g/uJ/Da1eTf/gAKApWs5mmwlyW/fYJBl41h/G33IX61HnPT55P/6D+/HHnHzlcfRiAlXkr2VyymScGP8G1SdeiVjnHLM92s4WqxtP59Ksb2/nXumwWbcxhTv9w7hwdS58IvQN7KM6VSqXCL9QLv1AvUkfZFnBrazZRWWCkItdAcKz91WUdbWYq8w0oCpTnGDrL9cEencHWsAQ9fmFeqNXOPctVXAT+cTD1jzDhaWhtsK8zd8AXD0JzFSRMgqH3QvJ0UDvHe40QQjiac3wqvUjCw0+vYltaWvq97b5b9919hBBCCCHOlTYigpgP3sdz5IjOso7CQsp+9WvyZs/BsHIVisV5VoRWqzUkDB7GvKf/wN3/fpOhc6/D3dv2Q7VitVJfXtolL2uyXzJLZizh/0b+Hz6utrbGDiPP7nyWO7++k5z6nMt+HpeCm4uGLx8ezScPjGRm31C+jaN1WKwsP1DC7Je3ccMbO/n6WDkWq+Tv7GncvbTE9A5g+Nx44voF2tU11bXjE9A1d6uhqpUTuyrYvOwEHz63h7ef2EJlwblfpiiucFoP0IXZl2WusAVXAXI3wIc3w7/7w9YXoLnm8vdRCCGcjARYvyM1NRX1qQ/2x44d+95239aFhobi799zL1ETQgghhGN5DhpEzOLFxCxdguewYZ3lHfn5lD35JHlzr8awejWK1erAXl58viGhjFt4J3f95038+w3GzdOLiXfcd9Y8lGqVmvnJ81lxzQpmxc/qLD9QdYD5K+ezr2Lf5ez6JaNSqRga68+rCwez5VcTuX9cPDr30xeb7cmv44H3D3C8zPADRxE9jX+4F7f+aRR3/G00M+7vy4Cp0YTG61G72I+FjjYLvsEedmV5h6rZ9EEWWbvKaahq4QpZu1hcqIRJttmtvjGnywzFsP6P8GIqfHYfFO85veKeEEKI8yIpAr7D09OT0aNHs3XrVr7++muefPLJLm0UReGbb74BYNq0aZe7i+fFZDJhMpkc3Y2L7ttzcsZzE0KcJmNdXEm0AwYQ/vZbtOzdS92iV2nbvx+Ajtxcql9ZhMfkyU6TMuC7FJUa/z6DmPfgz/D08bEb73n791CSeYzh827EzdMLvYue50Y8x+zY2fx5z58pbiomVh9Lml+a071OhHhr+eXURB4aH8sXh8pZsquI3OpmBkbpSQ3xsjvfNpMFd63zPTeuNK6eaqL6+BLVxxcAi8lKTUkTlXlGKguMdLRYUGvt3xNzD1WRvauS41vLAHD31hISpyMk3ofQOB2BUd64uHaP54a8p3cDWh8Y9hAMfQBV7nrU+95GlbseFQpYOuDIR3DkI6xx47EsWO7o3ooeSMa5cEbn83xWKVfIT52xsbEUFhbyzDPP8Oyzz35vu7fffpt77rkHlUrFzp07GT58uF39xx9/zI033gjAunXrmDx58qXs9nlZtGgRixYtwmKxkJ2dzbJly/D09HR0t4QQQghxPhQFj9xcAtPT8SgopPzmm2kc0L9LG5x41XGr2UzR6k8wNzehcfcgcOBwvGMTO2e4mhQTW9q2kKRNItol2m7fDqUDV5VzLRCkKHDCoMJFrZCosy//xxENOleF8aEKvXwVJE3nlaNiqyfmph8IoKoUXHVWvKI68IoyX76OiR7Ds72K2Jr1xNRuwdViW4ztZPAMMiJudnDPhBCie2hpaWHBggUYDAZ0Ot0PtnXaAGt9fT2W7+QtGzRoEMXFxTz55JP86le/6ix3d3fH29u7877ZbGbQoEEcPXqUiIgI3nvvPSZPnozVamX58uXcc889GI1GZsyYwZo1ay7rOZ0ro9GIXq+npqbmR58APZHJZCI9PZ2pU6fK6oRCODEZ6+JKpygKbfv24z5ooN3s1fbMTKqeeQa/Bx7Ea+KEs15W31N83zgvyTjGin8+h7mjo7MsvFcaE+64j8ComLMdCoDMukwe2vAQD/R7gOsTr0fj5Iu37M6v45Z3TqdIiA/05LYR0VwzIBwvN7lQzdmZ2i1UFTRSmW88tTXS0do1kDpsTiwDpkV13rdarGRsqyAkzoeACC/UmkufNU7e07s5UyuqjC9QH1iM5Zo3wC/udF1zNZqVj2IddDtK4jRZFEt8LxnnwhkZjUYCAwOv7ADrtzNWf8ztt9/Ou+++a1dWUFDAxIkTKSgoAGypA6xWK21tbQAMHDiQ9evX4+fnd7G7fVF8G2A9lydAT2QymVizZg0zZ86UF24hnJiMdSHOrvihh2nasAEA97Q0Ah95BO+JPTPQ+kPj3FBVyaYlb5Kzd1dnmUqtZuBVcxg1fwFunl527S1WCwvXLOR47XEA+gT04f9G/h+pAamX/kQcJD2jkj+sPE5JfatduY+7CzcNjeK2kbFE+cvVTFcKxapQX9lCRZ6BilwDFXkG6itauPYXAwlPOv29parQyCd/sQXmXVzVhMTqCI3XE5qgJzROj7v3xX/Plff0HmzLP2HDc7bb+igYcicMuh28An94P3HFkXEunNH5xNfkp+2ziI2N5ciRI/zzn//ks88+Iz8/H61WS+/evbn55pt59NFHcXV1rkvPhBBCCNH9WVtbMVdXd95vy8ig5KGHcO/Th6BHH8Fr3LgeGWg9G31wCFf/8nfkH9zHhnffoKGiHMVq5cCaL8navpnxt95N6pjTgeV2Szu9/Ht1BliP1R7jptU3sTB1IY8MeARPrfMFGqemhTApJZj0jEoWb89nd34dAI1tZt7cms/b2/KZlhbK3WPjGBorC7M6O5VahX+YF/5hXqSNDgegrcmE1t1+xmFF3umF0swdVkqzGyjNbugs8wv1JCReT1i8ntRRYagk78SVLXfj6dvfLoq16a/Q+1oYei9EDnHqlDVCCHGunHYG65VMZrAKIZyBjHUhzk5RFJo2bqL6lZdpz8i0q3Pv34+gRx7Ba8yYHhFoPddxbu7oYN+qz9n9+ceYO9o7y0dcdxOjb7jFru3+yv38cecfyTPkdZaFeIbw9PCnmRzdfXLnXwrHywws3l7AikNldFisneVz+ofz8s0DHdgz0Z00VLVQdLyuc6ZrY13bWdt5+7lx+19Gd9nXU+eKq/v5zdOR9/QezGqBnHWw9y04mQ6cET4I7QfD7oU+14Or8/2QJc6djHPhjGQGqxBCCCGEk1KpVPhMmoj3xAk0bdhA9cuv0J6VBUDb4SMU33sfHv37E/naq7j4O8esRRdXV0bMu5G0sRPZ+N6b5OzdiYurG30nTevSdnDIYD6d8ynvHn+XN468QbulncqWSh7f+DgToibwm2G/Icw7zAFncen1Dtfzz/n9eWpGCst2F7F0VyHVje3cOTrWrl272YKh1USwj7tjOiocyjfYE99gT/pNjASguaGdijwD5acCrtVFjVgtCqEJ+i77pr+TQXWhkYBIb1tagXg9YQl6fALce8SPOuICqDWQPN221eXBvnfg4PvQWm+rrzgCKx4FqxmG3OXYvgohhANJgFUIIYQQogdSqVT4TJ6M98SJNK5bR80ri2jPzgbAaupA001zxf8UuqBgrv7lb8k/tJ/G2hp0gcF29cbqKnwCg9BqtNzb716uir2K53c/z/ay7QBsKt6Esd3IezPec0DvL59Abzcem5zEA+MT2HqymkHR9s+FlYfLefqzI8zpF86do+PoG9k1kCauHF6+biQMCiZhkG08mU0Wqgsb0WjtF78yd1ioKWpEUaCmuIma4iaObS4FwFPn2pnDNTRBT3C0T5f9hRPwj4dpf4KJv4Vjy2HPm1B+CFx9oO98+7ZtBnD1lkWxhBBXDAmwOjGTyYTJZHJ0Ny66b8/JGc9NCHGajHUhzp3HxIlEjh9P87p11L32Gv4PPIDZbL+aeHtWFm4pKQ7q4dld6DiP7N2vy34dra0s+/0v0QeFMOGO+wiMjiXUI5T/jP8Pa4vW8sL+F6htq+XxAY9fMa8rKmBcor/d+SqKwjvb8jBZFD47WMpnB0sZHO3L7SOjmZoajMtlWFFedH+BMbZF5L773Glt6iBlVCiV+Ubqypr5bqK5FmMHeQeryTtoyxE9/b40YvoGdNZ3dHR0OZ7oyVygz43Q50ZUpQegLgdF7Q7f+f/VfPU0qoKtWIc/iHXAQnDCPNjCnnx2F87ofJ7PkoPViSxatIhFixZhsVjIzs5m2bJleHrKG5kQQghxRbFabQuOfOdyXfeCQqJfe42W+Hhqp06hNT7egR28NGoO7qYh84jtjkqFPrk3/n0Hozm1MGmrtZUccw59Xfva7VdrqcVT5YmH2uNyd9khzFb4qljNjioVLWb7S7p9XRXGhVoZEazgJenzxA+wmqDDoKGjXkN7g4aOBg3Kd55PYZOb0Lie/prZXOKCMdcN9wAzbgEW3ALMaGTNYKelNTcz7djPcFFsgfV2Fx/ygqaTFzgZs4uXg3snhBDnrqWlhQULFpxTDlYJsDqhb5Pw1tTUOO0iV+np6UydOlWSZwvhxGSsC3HxlN57H627dnXe9xg+HP+HH8JjoGMXPrqY47zw6CE2v/dfGirKO8s89b6Mvvl2UkaPP2t+SLPVzG3f3EZNaw2/GPQLpsVMu2LySLZ2WFhxpJz3dhZysqrZrs5dq+aaAeE8ND6eML3kaRU/zmpVaKhooTLfiKG6lRHX2P+Is3nZCU7srDpdoILASG8ievkS0cuX0HgdLq5yKbnTqM9H8/VTqPPW2xUrrt5YB9+JddgD4B3ioM6JS0U+uwtnZDQaCQwMlEWurnRardapX9ic/fyEEDYy1oX4aRRFwW/etVjKy+koLASgdfduSnfvxmvUKAIffQRPBwdaL8Y4Txw0lNi+A9i/6nN2ffYR5o52WgwNpL/+bzI2pTP5rgcJiomz2+ejjI/IqrctEPb0jqdZWbCS3w3/HVG6qJ/Ul55Aq9Vyy8g4Fo6IZXtOLYu357PhRBWKAm0mK5/sL+Wxycny+ivOWUiMKyExvmetUxQVqBRQTv2A8Z08rofXlaBxUROaoCd1VBi9hodevk6LSyM4GW77DCqOwbZ/wfHPQLGi6mhCs/NlNHv+CwMXwqjHwD/ux48nehT57C6cyfk8lyXJkhBCCCGEE1OpVOjnziV+9SrC/vIXtNHRnXXNO3ZQePMCiu69j9YjRxzYy4vDRatl+LU3cOeLr5E0bFRneWlWBkuf+hkb3/0v7S2nZ2tOjp7MxKiJnfd3lO3g2hXX8t8j/8VkuTJyyKlUKsYkBfL2HUPZ+IsJ3DEqFi9XDVf1DiXc1z5twuHiBprazd9zJCG+34SFyYRPaWLGg70ZMCWKgEhvu3qL2UrpiXoMVS125Yqi0FDZglx02UOF9oHr34ZH98PgO+nMC2Fph33vwJKrbWlthBDCCcgMViGEEEKIK4DKxQXfa69BP3sWhhUrqXntNUwlJQA0b91K89atxH76KR59eju4pz+dLiiYub/4DQWH9rPh3TeoLy9DsVo5ujGdoXOvw83TlgMwzDuM/0z6D+uL1vOX3X+hsqWSdks7Lx98mdV5q/n9iN8zJHSIg8/m8okN9OLZub15YloyTW32gdQOs5V7luyjrcPCDUOjuH1kLNEBkutfnDu1C0Sl+RPf33ZpeIuxg9Lsekoy6yjOrKexro3IVH+7ferKm/nwj3vw9nMjMsWPyBR/IlP88NK7OeIUxIXyj4c5L8GEp2DnIltwtaMJRj4MapnzJYRwDhJgFUIIIYS4gqi0Wnyvm4d+7hwavviC2tdex1RWhsegQbj3TnN09y6q2AGDue0fizrTBoyavwBv/4Au7SZHT2ZE2AgWHVrEB5kfYFWs5BnyuPObO7km8Rp+MfgX+Lr7Xv4TcBCduxadu/0lcauPllHd2A7A29vyeWd7PlNSQ7hzdCwj4wOumNy14uLx1LmSNCSEpCEhKIqCsaYVb3/7nL8lmfUANNW3k7WzgqydFQD4h3sRleJPZKof4Um+uLrL19oewScUpj0HY5+AfYth4K329bW5sPJnMOZxSJhst1ijEEJ0d/JOJIQQQghxBVJptfjNn4/v1VfT8PkXuCUm2AXJFEWh+qV/o7tqOu6pqQ7s6U/zbdqA1LET8PK1nx3X1tzE3i8/Zdg18/Hy9OJXQ3/FnPg5/HHnHzlWewyAFbkruCnlpisqwHo2fSN8uWloFJ8fLKXdbEVRID2jkvSMSlJCfbhrdBxzB4TjrpWFisT5U6lU6IO6zoj29ncjKs2fspMNWEynLyWvK2umrqyZwxuKUatVRPcJYNZD/S5nl8VP4eFnC7KeaftLULDVtoX1hzE/h9S5oJbXFSFE9yfz8YUQQgghrmAqV1f8brwBz8GD7cpbdu6k9o03yL92HiWPPkbbiWwH9fDi0AUGo3Gxn1uw4+MP2PPlp7zz+P0c37weRVFIDUjl/Znv85vhv8Fb682ClAX0Duj5aRN+qsRgb/56XT92Pj2ZJ6f3IkR3+hLtrIpGfrX8CKP+uoHXN+c6sJfC2SQMDGbuYwO458WxXP3zgQy+KobgWJ3dxEarVUGj6TrTMWd/FbWlTZK/taewmKFk/+n75Yfhkztg0TA4sBTMHQ7rmhBCnAuZwerETCYTJpPzLdDw7Tk547kJIU6TsS6EY9V+sKzzdmN6Oo3p6XhNnYr/gw/glpR0UR7DkeO8ramRY5vWAdBiaODrV//FkXVfM/72ewmKieP6hOsZHzYeT62nXf9MVhOfnvyUeYnzcNNceXkgfVxV3DcmhjtHRvHN8Ure21XEoWIDAHXNHVQbW+V1W3RxMcZ6SLw3IfHeDJ4VTXuLibKTBkpPNFB6ooGwZL3dsc0dFtYtPo7FrODhoyWil++pzQ9vvytv3PYYd29AdWI1mh0voao4tfBibQ6seARl45+xjngI64BbwdXLsf0UZyWf3YUzOp/ns0qRn/ScxqJFi1i0aBEWi4Xs7GyWLVuGp6csPiCEEEKI86cymdDv2YP/xk24NDZ2lisqFY19+1I3ZQodIcEO7OFPZ2pupObALpqLC04XqlTok9Lw7zcYjWvXQMy2tm183fY1AeoA5njMIVGbePk63E0VNsLmCjVHalX8ZqAF/+/82ZpMkG1Q0d9fQSPXzolLRFHs03W21Wio2Xv270EuXlbcAsy4B1hwCzCj1p61mXAkRSGo8RhJlasIasq0q2rXeLM96Tc0ekQ6qHNCiCtJS0sLCxYswGAwoNPpfrCtBFidkNFoRK/XU1NT86NPgJ7IZDKRnp7O1KlT0WrlE5EQzkrGuhDdg7WtDeOnn1L/1ttYamtPV6hUeF91FQGPPoI2KuqCjt1dxnnhkYNsXvImDRXlnWUeOj1jbr6dlDETOnPTtphamPHFDBpNpwPOM2Jm8MSgJwjw6Lp41pXG2GpC52H///jqpjz+tT6HEJ0bC4dFceOQSPy9XB3UQ+Eol3usNze0k3ewhtITDZSdbMDcYT1rO5UKbnl+OB4+8pzsrlSl+1Bvfwn1ya8BUHxjMT+4C9RyMW53013e04W4mIxGI4GBgecUYJVXJSem1Wqd+oXN2c9PCGEjY10IB9NqCbrzTgJuuon6Dz+i9q23bIFWRaHpq68IuPUWtPHxP/EhHDvOEwcPI7bfQPav+pxdn32EuaOdVqOB9Df+Q+6+XVz7q/8DQK/Vs2TGEp7b9RwHqg4A8FXhV2wr38bjgx7n+uTrUauu3GmaAWf8H5osVj7YUwxApbGdF9flsGhTHtcOjODuMXEkhfg4opvCgS7XWPcN0jJomjeDpoHFbKWywEhJZh0lWfVU5BtRrLY5Rr4hnuj87S83P7allI5WM1Gp/gRGeqNSy0r2DhU70rZVZsD2l1DFjkHr5mHfZu9bED8RAhIc00dhx9Hv6UJcTOfzXJYAqxBCCCGE+FFqDw8C7rwDvxtvoP5//6P2rbdxT0vDc9Agu3aKyYSqB36xctFqGX7tDaSOmcCmJW9xcs8OAOL62y/+leiXyOKrFvNFzhe8uP9FDO0GGjsaeW7Xc6zIXcH/jfw/kv2SHXEK3Y6LWsW/bxrI4u35pGdWoijQbrby4d5iPtpXzJx+4Tw2OYnEYG9Hd1U4MY2LmvBEX8ITfRk2BzrazJSdbKAksx5PfdeZq0c3lVBX1szOz3Nx99IS0cuPqFQ/IlP80Qd5nOURxGURkgbz/tu1vCoLVv8CVGrofS2M+TmE9r38/RNCXPEkwCqEEEIIIc6Z2tOTgLvvxu+mm7AYDHZ1iqJQsGAhbklJBD74AK4XmDrAkXRBwcz9xW8oOLSfoxvT6Tf1Krt6U0c7Li5a5iXNY0LUBF7Y9wIrclcAcLj6MDesvIG7+tzFY4Mec0T3uxWVSsXIhABGJgRQVNvCezsL+HhvMY3tZhQFVhwuY9WRMq4ZEMFTM1II1rk7usviCuDq7kJs30Bi+wZ2qWs2tFNX1tx5v63ZRO6BKnIPVAGgC3Qnspcfkan+RKX64+7V835Mcjq7Ftn+VaxwbLltS5oGY56AmJGO7ZsQ4opy5V7DJIQQQgghLpjaywtteLhdWdP69bQdPYrhs8/InTGTst/9jo6SUgf18KeJHTCYOT9/CrVaY1e+cfEbfPjsU1QV5OHv7s/zY57n7WlvE6uLBcCiWDBZZQXlM0UHePL72Wns/M1kfjsztTMP/NwfsQABAABJREFUq1WBb45XoJUVsEQ34KlzZeEfRjDupmTiBwTh6mE/H8lY00bG9nLWvnWcqgKjg3op7Ex9Dib9Hjy/EzA/uRYWXwXvXAXZa22roAkhxCUmM1iFEEIIIcRFYTEYUet0WI1GMJsxfLocwxdf4jtvHoEP3N8lINvTlJ88wdGN6aAovP/U4wyYPotRNyxkWNgwls9dztvH3mZN3hoe7P+go7vabXm7uXDvuHgWDI/mvZ0F/HdLHreOiMHvjIWv2kwW3LWa7zmKEJeGSqXCN8QT3xBP+k6IxGqxUl3URHFWHSVZdZTnGrCaFdQuKsISfe32zdpVTtaOciJT/IlM9SM42ge1/HBw6Xn4wrhfwoiH4OD7sOM/YLDlfqZoJyybDyF9YdofIWGSQ7sqhHBuEmAVQgghhBAXhe918/CZOoW6JUupe+89rI2NYDbT8PHHNHz+Ob7XzSPw/vvRhoU5uqsXxGqx4BcaTn15KYpi5eDXKzmxcyvjFt5J2tiJPNj/Qe7pcw9ajf1lw+9nvI9KpeKmXjehUUvQEMDLzYWHJiRy64gYVCr7RYQaWjqY/MJmZvcL46GJiYRI6gDhIGqNmpA4HSFxOobMiMXUYaE8pwFjdStaN/uxXHisltLsBkqzG9i9AlzdNUT0suVujUr1wzfEs8tzXVxErp4w/D4Ycicc/QS2vQQ1J2x1lUehvdGh3RNCOD/5SU0IIYQQQlw0Gp2OoEceJnFdOoEPPYja69QK3SYTDR9+RO606dR/8oljO3mBIlLSuO0frzDmpttwcXMDoMXQwNev/qszbcCZwdXixmJeOvASf93zVxasWcDx2uOO6Hq35eOuxdvNfs7Hf7fkUdvcwXs7Cxn39408tyqD6sZ2B/VQiNO0rhqi0wLoMz6yS11jbZvd/Y42C/mHa9j6UTbLnt3Ne0/vYN27GZRk1V2u7l6ZNFoYsAAe2gU3vg/hgyAgCVLm2LdrKIb2Jsf0UQjhlCTAKoQQQgghLjqNXk/QY4+RuH4dAQ/cj9rTEwDFZMKjd28H9+7CuWi1DL/2Bu588TWSho/qLC87kcH7Tz3OhsVv0NZ8+kv79tLttFtswcGM2gwWrF7AX/f8laYO+WL/fTxdNXi62mYHtputvL0tn7F/38Bf1mRS19zh4N4JcXbX/3oIt/5pJBNvSSFxSDDu3vY/tjQ3tHNiVwU1JfZj32pV6Gg1X86uXhnUakidA/dugDu/st3/rtVPwL96w8Y/Q4sEvYUQP52kCHBiJpMJk8n5Fln49pyc8dyEEKfJWBfCSXh54ffww+gWLKD+vSVYKivRJCXZfU5pycvDPTS0MwjbE3jo/Zjx6JOkTTjE5vfepKGirDNtQGB0LKnjbLn+rku4jgRdAn/e82dyDDlYFSsfZH7A2oK1/Grwr5gUNUkuGz7D/WNjuX5QOG9uzeeDPcW0may0may8sSWP93cVctuIaO4aHYuvp6zg3lNcKe/pHnoXkoYHkTQ8CMWqUFvWTNmJBkpPNFCea8DcYSU0UWf3d6jMN7LipcMEx+iI6OVLRC9fgmN90LjIXKiLxs0XvvvcqziK9uRa2+3Nf0PZ8TLWgbdhHf4Q6Hp2rnBHulLGubiynM/zWaUosqSes1i0aBGLFi3CYrGQnZ3NsmXL8OxBX1SEEEIIcQWyWon+z8toWlqonj2Lpr59oYcFHBWLhYaso9QdO4irrz+R0+Z2CZpaFAvb27ezsW0jJk5/WO/l0ovZHrPx0/hd7m73CMYOWFeqZnulCrNy+m/qrlG4N8VCos6BnRPiPCgW6DBocPWz2L3EGXNcMZ50s2ur0ii4+VtwCzDjFmBB62PtaS+L3ZpnezW9Kj4nsm4naiyd5VaVhmL/MZwMnkmze8/MFS6EuLhaWlpYsGABBoMBne6HP3RIgNUJGY1G9Ho9NTU1P/oE6IlMJhPp6elMnToVrVZmLgjhrGSsC+H8TCYTe/7wR0K+/LKzzGP4cIKefgrXhAQH9uzCNNZUY+7owC88wq48c9sm4gcOxc3Li9KmUv62729sK9vWWe+ucefliS8zOHjw5e5yj1FuaOONLfl8vL8Ek0XB10PLhifG4uMuF+T1BPKe/v2ObS4jY1sZDRWt39vGw0dL4pBgRs6Lv4w9uwIYilHvehX1oaWozKdz6CqoUFLnYhn1Mwjt58AO9iwyzoUzMhqNBAYGnlOAVT6RODGtVuvUL2zOfn5CCBsZ60I4t5bkJDzHjKFlmy3g2Lp7N0XXz8f/1lsJfPghNN7eDu7hufMP63ppaUnGMdJf/zeeel/GLbyTtLETeXXKq6QXpvO3PX+jqrWKAI8A+of0R+sir3XfJzpQy/Pz+vHgxEQWbcwhPtAbfx8PuzY7cmsYEOWLp6t8xemu5D29q4FTYhg4JYam+nZKTtRRkllPcVYdLYbT+YZbG02Y2q1d/nbFmXUERfvg7iV/0wsSGA+z/wkTfg27X4M9b0G7ARUKqswvUTdVwN1rHd3LHkfGuXAm5/Nclk8fQgghhBDCYUyBgYS9uoj2bduofP7PmEpLwWymbvFijKtWEfyrJ9HNnt1j85RuWbYYgBZDA1+/+i+OrP+GyXc9wLTYaYwKH8XLB19mTMQYPFzsg4VmqxkXtXxUP1Oknyd/mdd1RlmVsY07F+/Fx92FB8YncMuIGNy1Ggf0UIgL4+3nRsqIMFJGhKEoCvXlLRRn1VGSVU9pdj1RKfZpRNqaTaz8zyEUIDjah8gUfyJT/QhL0OMiz/3z4x0Ek/8PRv8M9r0DO1+F5ioY83P7dopi285cMEsIIQB5ZRBCCCGEEA6lUqnwmTSJ+NWrCHzkEVRutnyE5upqyp78FYW33kp7bq6De3lhZj/+FMnDR3feLzuRwftPPc6GxW/gYoKnhz/N2MixdvsUGguZ+dlMVuWtQrJ5nZtXN+XSbrZS09TBn1ZnMu7vG3l3ez5tJsuP7yxEN6NSqfAP96L/pChmPdSPe14YS8LAYLs2ZdkNKAqgQFVhIwe+KWTFS4d464mtfPnSQfZ/XUBVoRGrVV5Dzpm73hZUffwIXPtfSJpuX5+/GV4fDUc+BovZMX0UQnRbEmAVQgghhBDdgtrdnaBHHiZ+9Sq8J0/uLG89cBClh65KrAsMYs4TT3Pdb5/DL8yWm1VRrBz8eiWLf/4AxzevR7FaO9srisKfdv2J8uZynt76NPel30ehsdBR3e8xbhkRzex+pxelqWps59mVGUz85ybe31VIh9n6A3sL0b2pNWo0Wvuv7vpgD/pPiSIgwj6NisVkpSSrnl1f5PHJX/ax+FfbMMsPDedH6wH9b+w6U3Xri1CVAZ/dCy8Pgr1vg6nt7McQQlxxJMAqhBBCCCG6FdfISKIWvULUf99AGxON34IFuKekOLpbP0lsv4Hc9o9XGHPz7bicmqH7bdqAD5/5NW1NTQC0mlvx1p4OmOwq38W8L+fx2uHX6LB0nPXYAhKDfXhlwSC+fnwsV/UO7SwvN7Txuy+OMfGfm/hobxEmiwRahXMIiPBmzPVJ3PT7Ydz5/+zdd3hT1R/H8XeSpnvTXUYpe++9ZYqCDEFEmcJPcSCo4B6oiOIARxVEQASKAwRBQCjI3nvIKKuFLrqb7qbJ/f0RLEZUVuE26ff1PDw2596bfG7NSZpvzj1nent6PFaPOu2Ccfd1strP08/lmikDTu5MJHpfEnkGeU25YYU5YMy7ejszFlY/B582hO0zocCgWjQhRNkgEzsJIYQQQogyyb1jR8Jbt4a/jV5VjEYSX3sdn+HDcKlXT6V0N89Br6dVv0HUad+ZLd99Q/SeHQBoHXQ4ubkB4Kp3ZUaXGWy5tIWpe6aSmJtIkbmILw9/yZrza3ijzRu0CGqh5mmUabWDPJk1rBnH47OYueEMG05eBiA+M58Xlx3DaFJ4tHUVlVMKUbpcPR2p0SKQGi0CURSFrJR84k5lEHcynYAw61WvFUVh76rz5GQUApZCbcU6PlSq7UtwdS8cnaVE8I+c3OGxKIjZDts/gXO/W9pzLsOGNy1tLcZC63Hg5qduViGEKuTVUwghhBBClFlaR0dwdLRqS/9uIVm//ELWypV4PzSYgAkT0Hl7qxPwFvw5bUDM0UNsXjCHrqOeuGYRr44VO9IiqAVfHfmKhScWYlJMxBhiGL1uNH2r9eX55s/j6+yr0hmUffVDvfhmRHOOXMpkxoZoNp9Owd/DiQebVVQ7mhB3lEajwTvAFe8AV+p3DL1me1ZyfklxFSAtPoe0+ByObLiEVqchKNyLirV9qFjbl4AwD3Q6uei1hEYDVTtY/iUcgu0z4MRKQIGCLNj2EeyKgGf2g5e81ghR3sirpRBCCCGEsBmKomBYt+7PG2R+/wPnet1Lxg8/ophsa57BsIZNGPFRBH6Vw6zaY44e4vs3JpMdl8jzzZ/nh/t/oKF/w5LtK8+t5OmNT8sCWDegUSVvvh3VkmXj2vJe/wY4/+1S6S83n2X10URZCEiUGx6+zvSd0JimPasQUMUD/vLdjtmkkHAmk72rLvDzhwdIjslWL2hZF9IEBn8HT++DJo+CVm9pr9hciqtClFMyglUIIYQQQtgMjUZD2KKFpC9cRGpEBOa8PEyZmSS9+SaZP/1E0Ouv4dKokdoxb9jfR64WG438Pm8WGYnxLH55Io163Eu7wcNYeO9ClkYvZeaBmWQbs3m6ydPXHCv+XbMqPte0XUrPY0ZUNEaTQu0gDyZ2r0mPuoHyexV2TafXUqm2L5Vq+wLVKMg1En86g7hTGVw6lU5Wcj4AemcdgWEeVsee2JFAfHQGlWr7UrG2D+4+ziqcQRnjVwMeiIDOL1tGr9bsab1dUWD9a1B/IIQ2VSejEOKukAKrHTMajRhtdMXd//LnOdnjuQkhrpK+LoT9u+V+rtHgOXwYrr16kvrxJ+SsWQNAwfHjxDw0BI/+/fGb8Cw6X9u7hD7zcmLJz4pi5vC61ZzetY12Dw2nX4cH6BDcgd8v/U4L/xZWv7eU/BQ89B44O0jB40atOHgJo8kycvVUUjaPLzxAvRAPnr2nOp1r+kmhtRTJe3rZpXOEyg18qNzABwgnO72A+NOZFOYVYzKbMJmvXhlw9sBlLp3IIHqPZW5j70AXQmt5E1rLh5AaXji6lOPygmsgdH3b8vNfnueas1E47PoCdn2BuWpnzG2fRanS3jLdgJ2Rfi7s0c08nzWKXFtkNyIiIoiIiMBkMhEdHU1kZCSurq5qxxJCCCGEuKNczp8n4JeVOCUllbQVu7lx4cXJKE5O/3Fk2aSYTGSePkb6sUMopuKSdme/APybt8PJ13oBFUVRmJc7j0xzJn1d+lJDX+NuR7ZJigKnsjSsuajlYq51saOKu0LvSmZqeSn2WAcR4qYpCiT+7oa56N9mGVRw9DbjVKEY1+Bi9B7mu5qvrGpx/lNCsg5YtaW7VuNM4P0keTUBjczaKERZlpeXx9ChQ8nKysLT0/M/95UCqx0yGAx4eXmRmpp63SeALTIajURFRdG9e3f0er3acYQQd4j0dSHsX2n2c6W4mKwffiD9iwjMOTl4jxqJ33PPlVJSdWSnpbJt8XzO7t1Z0qbRaGnQrSetHxyKs5s7AL+e/5U3dr9Rsk+Pyj14vtnz+Lv43/XMtkhRFDZFp/LpxrOcSLSec7J5FW9e6F7jH6cYEDdO3tPtg6nYTHJMNvGnM4k/nUlyrAHlH+qoHYZUp0674KvHGc2YzQp6J921O9u74gK0R79Hu+sLNJkxVpsU/9qY2oxHqdsfdLbfL6SfC3tkMBjw8/O7oQJrOR7Db//0er1dv7DZ+/kJISykrwth/0qln+v1+I8cic/995M6+2v8n3oa3V/u01xYiNlgwMHfdoqOvkHBPPD8K8QePczG+bPISIhDUcwcjVpLYU4O9094EYBGQY1oHtic/Zf3A7D+4np2Ju7k2abPMqjmIHTacljUuEk964fQo14w6/64zMwN0ZxKshRa98dmci4tn9bVA1ROaB/kPd226fVQuY4TletYRtEX5RcTfyaTuJPpXDqVQUZiLgBh9f2t/j/HnUxl7VdHqVDRneBq3gRV8yS4mjfuPk72PxWHXg+txkLzUXBiBWyfAZePA6BJOYXDyidh6/tw/wyo3k3drKVE+rmwJzfzXJYCqxBCCCGEsBsOfn4EvfrKNe3p8+aR9s1c/J55Gt9HHkFjQx/+qjRszIgPP+fA6l/Yvex7TMXFtB38aMn2cK9w5vWcxy/nfuHj/R+TWZhJjjGHqXumsvLcSt5o8wa1fWureAa2QaPR0Kt+ED3qBrLmeCIzN5yhwGhiULNKVvsZTWb0OrmsVwhHFweqNvSjakNLwTU3s5DEc1l4+rlY7Zd0LgtFgdRLOaReyuHYZku7m7cTQeFeBFfzIqiaF36V3NHZa9/SOUCDBy2LXZ1ZD9s+gUu7LdsyL4Kr338fL4Qo86TAKoQQQggh7JoxPp7U2V+jFBSQ/P4HZC1bRuBrr+PWqqXa0W6YzkFPywcepE77zsSfPoFvSKjV9pTYC/QK6kanfp345MAnrDi7AoBjqccY8usQHqnzCE81fgpXvczPfz1arYb7G4Zwb/1g4jPycXSwLvi8uPQoGXlFTOxek4YVvdUJKUQZ5ObtRPVm1472dvHQUyHUnbSEHPjLBIW5mYWcO5jMuYPJAARX92LAC83uVlx1aDRQs6flX+xOS6FVMUNIY+v9Eg6DZwi4y+h5IWyFFFiFEEIIIYRd07i64tWnD5lLl4KiUHjmLBdHjMCzd28CJk9CHxSkdsQb5lHBj9ptO1q1GYsKWfnxVIry8+n4yCje7jSFB6o9wDu73+F81nlMionvTnxH50qdaRHUQqXktken1VC5gnVB+mxyDisOx2NWYNPpFLrVCWRi9xrUC/FSKaUQZV/jbpVp3K0yhfnFXL6QReK5LJLOZXH5ggFjoalkv4DK185vuGLGITz9nEtGunoHutrPtAJV2lr+FRdat5vN8PP/IDMWmgyDts+ATxV1MgohbpgUWIUQQgghhF1z8PEh+J238R48iKR33qXg6FEADGvWkL15M37jnqDCiBFoHB1VTnpr9q/6mazkywCsm/UpRzf+RtfR41jaZynz/5jP10e/pkeVHlJcLQXJhgKCPJ1JyCoAYMPJy2w4eZl76wcxoVtNagV5qJxQiLLLycWBynUrULluBQDMZoW0+BySzmWRdD6LSvV8rfbPTi8g/nQG8afh5I5EAJzd9ASFexJUzVJw9a/iid7RxueZdnCyvn16DaSetvy8bw7snwcNBkH7iRAg070IUVZJgVUIIYQQQpQLLg0aEPb9EjKXLSPl408wZWai5OWR8vEnZC37mcDXX8O9XTu1Y960+p27k3rpItG7tgGQeOY0i195jobd72X4Q8O4N+xe3BzdrI4xK2bWXFhDr7BeOGjlI8GNalvdj02TOvPjvkt8sekslw2WkWdrjyfx2x9J3N8whGe71qB6gLvKSYUo+7RaDf6VPPCv5EGDzhWv2Z6RmItWp8FsujqvQEGukZhjacQcSyu5D7/KHtz7eAPcfZyuuQ+bFNoM2jwN++eDMRcUExz93vKv1n3Q4Tmo2FztlEKIv7HTGaSFEEIIIYS4lkarxWfQIKr9thafoQ+D1vLncFFMDPkHD6mc7tZ4VPCjz4QXefC1d/ENsRQpFMXMkfWrmTfhf2QdOIWPo7fVMSvOruDlbS/z8OqHOZZyTIXUtsvJQcewNmFsmdSFN/vUxc/dUtRRFFh1JIEeM7bw0brTKqcUwvZVrleBsTM60v/5prTuF05YQz+c3awXKDSbFdITcnDxtG6/cDSVY5vjSLmUjdlkvpuxb59nMPScChOPQ6eXwNn76rbTq+GbrrCgD5zfrFZCIcQ/kK+rhRBCCCFEuaPz9ibojTfwfvBBkt55l+LkZCqMHaN2rNtSpUFjhn/4OQfXrGTX0iUYCwvIzzaUTBvQc9wEKoRWIr84n5kHZgJwKv0Uj6x5hIdqPcT4puPxcJRL3G+Us17HqHZVGdKiMot2x/LVlnOk5xZhVpARrEKUEgdHHSE1vAmp4Q2AoihkXs4j6bxlHtfEc1m4ejmi01mPHTuxLb5klKveSUdg1SvTCoR7ERjuhZOLDZRCXH2hy8uWOVgPfAu7voBsy1QJXNgK7kEQ3lnNhEKIv7CBVxUhhBBCCCHuDOe6dakSuZjipCS0zs5W29IXLkJfMRSPLl1USnfzdA56WvQdSO12ndiycC6nr0wbkBJzAQe9ZYSXi4MLn3f9nLd3vU10RjQKCt+f/p6NFzcyueVkelbpaT+LyNwFLo46xnYMZ2iryizYFcOmU8n0aRRitc+l9Dx0Wg0h3i4qpRTCPmg0GnyC3PAJcqNOW0s/M/1thKqiKCSezyq5bSw0EXcqg7hTGVfuBCqEuBEU7kWt1sEEVyvji9Q5uUPbp6HlWDjyPeyYCennLXOy/pXJaPmvTn/NXQgh7jyZIkAIIYQQQpRrGo0GfXCwVVtRTAzJ06cTN+5JLj0xjqKLF1VKd2s8Kvhx/1+mDWjZfxBeAUEl2xv5N+L7+7/n+WbP4+JgKfql5Kcwacskxm0cx6XsS2pFt1luTg482bk6Pz7eBp3WukA9dfVJOn+4mTd/Oc5lQ4FKCYWwT38fvYoCfcc3pv2gGlRvFoCbt9M129Pic/ljWwIZSblWm4oKikk6n4XJWAanFXBwgmYj4On9MHINBNa13n44Ej5tDLtnQVGeKhGFKM9kBKsQQgghhBB/k7l8BYrRMhooZ/NmcnfupMKYx6gwdixaF9sZhfjntAGKYt1uLChgbcQn3Nt/MD0e6MG0PdPYHLcZgB3xO+j/S3+eaPQEj9V/TEaz3qS//76Ox2fx2x9JACzYFcv3+y7xSKsqjOtcDX8PO1mUR4gyRKPVEFDFk4AqnjTqWgmA7PQCy5QCV6YWSI3LQTErBIVbj15NPJvFr18cQeugIaCyJ8HVvAiq5kVQuBeuno5qnM61tDoI+9uCjGYT7PgUDHHw24uwdTq0HgctxoKLtyoxhShvpMAqhBBCCCHE3/hPeBanmjVI/mA6xcnJKEVFpH75FVkrfiHwlZdx79rVZgqPOodrLxfd/fP3nNm7kzP7dtGoe2+mD36PndX38t7e90jOS6bQVMhFw0WbOceyrKKPC092rsa3O2PIKzJRWGxm3o4LRO6NZUSbMB7vVA1ftzJSuBHCTnn4OuPh60yNFoGAZaRqcmw2PoGuVvslXZlawFysWOZ5PZ8FUZZtXv4uJcXW4OpeVAgpQ3Mt52dCheqQfs5yOy8Nfn8Xtn8KLUZD66fAI1DViELYOymw2jGj0YjxysgLe/LnOdnjuQkhrpK+LoT9K+v93LVHDyq3a0f67NlkLlwExcUYExKIe/oZXNu1xe+ll3AMC1M75k0zm0ycP7TfckNROLJ+Nad3baPdkGEsvfcnZh2fzbrYdTzT6Jky+//GlrjpNUzsWo3hrSsxZ9sFFu+9RIHRTIHRzOyt51m0O5bhrSszul0Y3q72OXdiWe/rovzR6CAw3J1iUzGYrrb7VXKjZssALl/IJisl3+qYrJR8slLyOb07iQoV3Rj4YlOr7WaTglan0pdSjp4weDFc/gPdrk/RnFiBRjFDUTbs+BRl9yzMjR7G3Ppp8Am7IxGknwt7dDPPZ42i/P2CIWGrIiIiiIiIwGQyER0dTWRkJK6urtc/UAghhBBC/Cd9cjIBv6zE7ezZkjZFpyNuzGPkh4ermOzWKCYTmaePk378IEpxcUm7U4UA/Fu0Q+PjgZPG+vL1w0WHUVBorG8sI1tvg6EINsRr2XFZQ7Fy9fcY7qHwbH3TfxwphLibTIUaijJ1FGZoKcrUUZSlA7Olz7pVLsKnXqHV/klb3NA4KDj6mHDyNuHoY0LnrKDGy6Vr4WWqX15D5fRt6JSrr/HFGkfW1f+UYge3ux9KCBuUl5fH0KFDycrKwtPT8z/3lQKrHTIYDHh5eZGamnrdJ4AtMhqNREVF0b17d/R6+/yWXwghfV2I8sDW+rmiKORu2EDq9A8pTkpCH1aFysuWoXG03cu7s9NS2b5kAWd2b7/aqNHQ4J6etBk0FGd3DwDS8tMY8OsAso3ZNA9szistXiHMM0yd0HYiyVDArC0X+PFAHEaTwpcPN6Z73QC1Y90RttbXhfgnJqOZ1LgcLp834F/Fg+DqV+dvzcsqYtFre645xs3bkcCqngSGexJY1RO/im5o/74o152UnYh27yy0B79FU5SLueFQTH0+uyMPJf1c2CODwYCfn98NFVhligA7ptfr7fqFzd7PTwhhIX1dCPtnS/3csXdvvDp3JvXrr3Ft3gJHN+tRQMUZGTj4+KiU7ub5BgXTd+JLxB47zO/zZpGeEAeKwrGNvxF79BCjZ85G5+DA72d/J9uYDcD+y/t5aM1DPNbgMcY0GIOTThZquhWVKuiZOqAhT95Tg6X747i3YYjVyOAjlzLZdT6N4W2q4OpoHx/bbKmvC/F3ej1UrOlExZoVrtlWlF9AhVB30hJy4C9D2HIzizh/KJXzh1IBcNBrefDl5ndv/lbfytDrPeg0CfbOQVuvP9q/9kFjPvw8FpqNhGpdKY3httLPhT25mefyXfzqRAghhBBCCNundXUlYMIE3Ntbr+JceOYMZ7vcw+X3P8CUk6NSultTpUFjhn/4OR0fGYXeyRmAxj16o3OwFPaG1hlKRNcIQt1DATCajcw6MouBKweyO3G3arntQai3C892q3HNtAsfrT/N+2tP0XH6Jr7Zdp4Co0wfIERZ5V/JgyGvt2TMJx3pM74RLe4Lo2JtH/ROOqv9FAW8/a2n8Tu1O5HfvzvJiR0JZCTlckcuMnbxgU6Twa+GdfuhRXByFSwaCF93gj+Wg1lea4S4FfbxVagQQgghhBAqUhSFpHenohQUkP7tt2St/pXASZPw7NPHZuYr1TnoadF3ILXbdeLA6hU07d3Xansr3+Ys6fodC85FsuCPBRQrxcQaYhm7fiz3hd/HpOaTqOBy7cgucfMupOay46xlxFtqThHvrj7J11vP82TnagxpWRlnve469yCEUIOTiwOV61agcl3La6HZrJAWn0PSuSySzmdhNivo9Nbj3C4cTuX84RRO7ky03IebA8HhXgRV8yK4mhf+VTzRO96hPn967dWfE4/ATyOhQnVoNwEaPgQOtjsFjhB3m4xgFUIIIYQQ4naZTLi2bIHGyXK5vCkllYTJLxI7bBgFp0+rHO7meFTwo/PwMegcrC+L2/nTYiJfmMA9mXX44b7vaezfuGTb6vOr6bOiD/uT9t/ltPapqp8b6yd25P6GwSVX7CZnF/LWqhN0+Wgzi3bHUlRsVjekEOK6tFoN/pU8aNC5It1H16PnmPrX7JMca7C6XZhbTMyxNHavOM/yjw/xzYSt/DRtH6f3JJV+wEd+gkELILjR1ba0s7DyafisMez6EopyS/9xhbBDUmAVQgghhBDiNmkcHPB/6inCV/+Ke9euJe35+w9wof8Akt6dislg+I97KNtSLsZw6LdVFGQbiPr6c/Z/8jXTa73Om23exNPRsuiDXqunhk+N69yTuFHVAzz4YmhT1j7bgV71gkraE7MKeG3Fcbp8tJkf9l3EbJY1i4WwZY9MaU3/55vSpn81whr64exm/eWW2ayQHJuNsaDYqr2ooJhjm+NIuZiN2XSLX7hodVCvH/xvCzz6M4R1uLrNEA/rXoYZ9eHcplu7fyHKEZkiQAghhBBCiFLiWLEilSK+IGfrVpKmTsUYexHMZjIWLcKwZg0Bzz+PV/9+aLS2Nc7Bxd2Dmq3bc3rnVgASz54m8tXnadStFz89EMnnp2bRPrQ9Xk5eVscpimIzUySUVbWDPJk1rBnH47OYueEMG05eBiA+M58ley8xuHkllRMKIW6Hg6OOkBrehNTwBiyvm5mX80g6n0XSuSwSz2WRkZRHUDXr19fLMQa2fh8NgN5JR2BVT8u0AuFeBIZ74eRyE+UejQaqd7X8u7QPtn8Cp9dYthXlQkCd0jhVIeyaFFiFEEIIIYQoZe4dOxLeujXp8+aTOmsWSkEBpvR00r/9Fq++fcDGCqzuvhW4/9nJNOzak43zZpEefwkUhSNRazm9ewcPPzyCBmHdrY5JzkvmyQ1PMr7peDpW7KhScvtRP9SLb0Y058ilTGZsiGbz6RSe71HzmgK22ayg1UpRWwhbpdFo8AlywyfIjTptQwAoyDXi+LeCadK5rJKfjYUm4k5lEHcq48qdQIUQN4LCvQiu7k2tVkHcsEot4OElcPkEbJ8Bzl7g8bfjo9dZFszyDb+lcxTCHtnWX3ZCCCGEEELYCK2jI35PPE61Navx6NkTgMDXX0Oj11/nyLKrcv1GDJ/+GR0fHY3eyRmgZNqAJa9P4vL5syX7Tt83ndMZp3lq41M8t/k5kvOS1YptVxpV8ubbUS1ZM74D7av7WW3beS6VnjO3svpookwdIIQdcXbTX/PFSfVmAbQfXIPqzQJw83ayPkCBtPhc/tiWwMF1sdfcX1ZKPibjdaYVCKwLA+dA7w+t2wtzYPnj8HkzWDoako7dyikJYXdkBKsQQgghhBB3kD4khIqfzqTgdDTOtWpabSs4eZL8I0fxHvQgGp1trAyvc9DTos8AarfryJaF86ymDYg/fZLA8OoUFBeQWZBZckxUbBQ7E3byTJNnGFJrCDqtbZxrWVY3xNPqtqIozIiK5kxyDk9FHqR2kAcTu9ekR91AmaZBCDv05yjXRvdYpgnJTi+wTClwZWqB1LgcFLNyzdQCAL9+cQRDWj4Bla9MK1DNi6BwL1w9Ha99oL+/fhxaCPlXRsoeX2b5V6MHmjbjS/sUhbApUmAVQgghhBDiLvh7cVUxm0ma8jb5hw+T+dNPBL3xOi6NGv3L0WWPh6/flWkDerFx3lfoHBxo3KM3AM4OzszpMYdfz//KR/s/Ir0gnVxjLu/vfZ9V51bxRps3qFuhrspnYF8MBcX8ddDqqaRsHl94gPqhnjzXvSZdagVIoVUIO+bh64yHrzM1WgQClkWwkmOzr1k0Kz+niMzLeQCWeV7PZ3E4yrLN09+lpNgaXM0L32A3NH+fcqTRw1CUA7u/grw0S9uZ9TicWU97t5po6npAjXvu6LkKURbJFAFCCCGEEEKoIP/AAfIPHwag4I8/iHloCAmvvkpxerq6wW5S5foNGT79c/pNfh3tX0bhajQaQk6ZmNfoUwbWGFjS/kfaHzy8+mHe3/s+OUU5akS2S14uepY+0YYFo1vSqOLVEWvH4w2M/nY//b/cydboFBRFpg4QojxwdHagYi0f/Cq6W7UXF5mp3ToIrwCXa44xpORzencSWyJP8/07e0k8l3ntHbt4Q8dJMOE43DsdvK4utFchNxqHxf0h8iFIOV3KZyRE2SYjWIUQQgghhFCBa4sWVFn4HUnvvEthtGUl6KxlP5MdtQH/8ePxGfIQGgfb+HNd5+CAp1+AVVvSuTNsXjgXgPbdenHvPV/x/rGPOZt5FrNiZvHJxRxOPsyS+5bIyMpSotFo6FTTn441/Pj9VDKfREXzR4IBgMOXMhk+by8twnyYP6ol7k628dwSQpQuD19nuo60XEGQn11E4rmskpGsyTHZmIotc7NqtRr8q1hPRXJscxwndiQQHO5FUHUvgqoPx6PZKDTHl6Js+wRN2hnLjtG/gU8Y3PvB3Tw1IVQl76pCCCGEEEKoxLVFC6r+vIyMyCWkfPYZ5pwczAYDl999l8ylSwl643VcmzZVO+YtObh2JVwZLXkkai3Ou3fwxpCxHKiawOyjsykwFTCy/kgprt4BGo2GrnUCuad2AOv+uMzMDdGcSsoGwNFBK8VVIQQALh6OhDf2J7yxPwAmo5mUS9kknssiP7sIvaP1fNkJZzJJvZRD6qUcjm2JB8DNy5Ggag0IqPkT2acX0a5oCQ7GHOg4+a6fjxBqkndWIYQQQgghVKRxcMB3+DA8e99L8kcfk7ViBQCFp04RO/QR/J8dj9+4ceqGvAU9n3iWgCpV2bl0CcaCfAqyDfw+50uCq9di7pCZ7DAdpWeVnlbHZBVm4ergil6n/5d7FTdDo9HQq34QPeoGsvZ4EjM2RPNc97/NBawonEg0UC/k2oVwhBDli06vJSjcMgfrPykqKAYN8JeZRnKzijh3MIVzB1OAdpx26kCTVg60cKtgffCBBeDgDA0GgVZmqxT2R57VQgghhBBClAEOfn6EvD+NKpGRONWtU9Lu1qaNiqlunc7BgeZ9BjB6xixqt+tU0p549jQbpn5ArUNaivLzrY55a+dbPLjqQfYn7b/bce2aVqvhvobBrJ/QkWZVfK22/X4qmfs+287I+Xs5GpepTkAhhE3o80xjxnzSkT7jG9HivjAq1vZB72Q9ytVYaMYxsIpVmykrhcNLt5Hy4zSUr7tAzPa7GVuIu0JGsAohhBBCCFGGuDZtQtWffiLzxx8pionFpXFjq+3mvDy0rq7qhLsF7r4VuG/8JBp27cnGebNIi7uIopg59NsqzuzZweC33scnKIQtl7aw4eIGAEatG0W/6v14rtlz+Dj7qHwG9kP7t9XAFUXhkyjL/L+bT6ew+XQK3eoEMrF7DRnRKoT4R04uDlSuW4HKdS0jVM1mhbT4HOKj0zm4/RTkuBJay/p1+/LWdezIGAoMxSk9m5CTWwmtuIWK9/bDt25dNFqZKkbYPimw2jGj0YjRaFQ7Rqn785zs8dyEEFdJXxfC/kk//2/uDz4IWP9+FJOJuEeHoa9cGb/nn8MhKEiteDctqGYdhrz7MYd/W8Wen7+nuKgIV29fXH18MRqN+Dn5Uc+3Hn+k/wHAirMr2HxpM882fpa+4X1lrtY7wGRWGNqiIhGbz5OQVQDAhpOX2XDyMj3rBjD+nmrUDPS47ceRvi6EffMOcsatgj+xOYfp1q09er3eqr9fVFoDFwEoVDy4UNiaC+eALy7j7HiR4Fp+hNQOIKSmNz5BtvMForB/N/O+pVEURbn+bsIWREREEBERgclkIjo6msjISFxtaHSDEEIIIYT4b1679xC4fDkAZkdH0rreQ0b79uBgW+MmjDnZpBzYiW+DZjj7+pW0mxUz+wr3sr4gikIKS9qr6KrQ17UvgbpANeLavWIz7E7WsD5eS1bR1UK2BoXGFRTurWQm0EXFgEIIm1acq6EgxYGCdB3FqQrFJsd/3M/BxURQ57y7nE6If5eXl8fQoUPJysrC09PzP/eVAqsdMhgMeHl5kZqaet0ngC0yGo1ERUXRvXt39HpZAEEIeyV9XQj7J/385mWvXk3KB9MxZ2SUtOnDquD/8su4tm2rYrLSkXj2NJvmzaLJ0If4LusX1sWuK9nmoHFgWJ1hjKk/BhcHqfbdCYVGE9/vj2PW1guk5hSVtGs1sHFiByr63NrvXfq6EPbvRvu5YlZIi00lceNvJJ5OJ6GwFkWKGwC1w1PoOHGA1f6/zfoDR1cHQmp4EVLDG08/5zt6HkL8lcFgwM/P74YKrLb1Vbe4KXq93q7/gLH38xNCWEhfF8L+ST+/cb79+uHVuTMpn31Oxvffg9mMMSaWhMefwKN7dwJfehF9aKjaMW+JqbiYzfNmkXoxhqgPptOza0/6dp7J+8c+5lL2JYqVYuafmE+zoGZ0qtTp+ncobpper2dMx+o80roqi3bHMmvLOdJyi7indiBVA25/4Ib0dSHs34308+CaIQTXHA3ZSZh/n0rqvp3Em1sScO+rVscW5Bq5eCIdFDi7LxkAD19nQmt5E1rLh9CaPnj4SsFV3Dk3854lBVYhhBBCCCFsiM7bm6A3Xsd70IMkvf0O+YcOAZAdFUXOtm34Pf4/fEePRuvkpHLSm5NnyESjvbIataJwdMNvuO7bzfuPPseWsJPM+2Me7UPa07FiR3WDlgMujjrGdgxnaKvKfLcrlk41/a22m80Kn248w0MtKhHiLaOJhRC3yCMI7QOfE9D6DwKy4qBmRavN6TvW4ODgSbHx6oXX2ekFnNqVxKldSQB4+rsQWtObVn3DcfOyrfc9YV+0agcQQgghhBBC3DznOnWoErmY4PenofOzzGOqFBSQ8vkXFJ0/r3K6m+fh68cj731C5+Fj0TtZRiTlZWUSFTGT4LXJLGg9i1dbv2q12JWiKGy8uBGT2aRWbLvm5uTAuM7VqBtiPXp19bFEPt14hs4fbuaNX45z2VCgUkIhhF0IrAc1e1q3GRII2fs4Y/yGMKD9HlrdG0RoLR90eusyliEln1O7ktA76azbU/PJMxQhxN0iI1iFEEIIIYSwURqNBu9+/fDo2pXUL74gfdFifB4ajHOdOmpHuyVanY5m9z1AjVZt2fTtbM7u2w1A7NFDxJ/8g9YDh+Dfpz86B8sle79f+p0JmyZQx7cOb7Z5k3p+9dSMX258s/0CAEUmM9/tiuWHfZd4pFUVxnWuhr+HjCATQpSCHZ9CcT46IPjs+wQ7fUXzji9Q/MRYLl8qIP50BvHRmSRdyMK/kgeOztblrT2rzhO95zI+wW5UrGmZUiCkpjcu7v+8wJYQt0sKrEIIIYQQQtg4nYcHgS+/jNfAgegDAqy2KUYjGUuW4D1oEFoX27ic29PPnwdeeI2z+/fw+7xZZKelUGwsYvv335Eef4l7n34eo8nIB3s/AOBk+kkeXv0wQ2oP4Zkmz+Dh6KHyGdi3BaNaMGfbeebviCGvyERhsZl5Oy4QuTeWEW3C+F/HcCq4S6FVCHEb7nkdnL1h52dgzIPCLIh6HYd93xDa7S1C7+8PGg3FRSZys6xHqiqKQvzpTAAyEnPJSMzl2JZ4ACqEuhFa08dScK3hjbObzAstSodMESCEEEIIIYSdcK5ZE523t1VbRmQkl9+bxvn77id7wwYURfnng8ug6s1bMfKTL2l2Xz80Wi0arZZm9/cHQK/TM73jdGr41ABAQWHJqSX0XdGX3y78ZlPnaWu8XR2Z1LM22yZ34fGO4ThfuWS3wGhm9tbzdJi+iQ/XnSIrz6hyUiGEzXJyhy4vwzMHoMmjwJXpYTJjYekomNsdLu7BwVGHl7/1l4fmYoVarQIJCPNEo9VYbUuLz+XopjjWzjrG3Be2cWJHwl06IWHvpMAqhBBCCCGEnTIXFJA6azYAxoQE4p5+hktj/0fhhQsqJ7txjs4udB4+hkenzeSeUU8QEBZesq1xQGPmd5jNxKYTcXGwfMBOzU9l0tZJjNswjkuGS2rFLhcquDvxcu86bJ3chVHtwnB0sHy8zCsy8eXmc1zOlrlZhRC3yTMEHoiAJ7ZBeOer7XH7YF4POPHLNYfo9Fra9K/OoJeaM+bjDtz3VEMad6+Mf2WPkjotAApUCHG3OjYtPoedP58l9o80igqK78w5CbskUwQIIYQQQghhp7TOzlRZvIjL775L7s5dAORu3875vg9QYeRI/J54HK2bm8opb0xAWLhVcRXAVGxk+btv4u7lzXdDZxERO5/NcZsB2JGwg/4r+zO2wVjGNhyLViNjS+6UAA9n3uxTj8c7VuPLzWdZsvci99YPpmagTNUghCglQQ1g2Ao4uwHWvwYpp8AzFGr0+M/DHF0cCGvgR1gDy2KQhXlGEs5kEn86k8sxBvwrWxdYY4+ncWj9RQ6tv4hWqyEgzIOQmj5UrOlDUHUv9I66f3oYIaTAKoQQQgghhD1zCg+n0ty5ZK+P4vL771OcmAhGI2lz5pC1ahWBL07Go1cvNBrN9e+sjNn/6wrS4i6SFneRhDdP8HC/QTzQvg8fHPqQpNwkCk2FnEw/KcXVuyTIy5m3H6jP452qXbNNUWD21gs83KqKzM8qhLg1Gg3U6A7hXeDQQnD1Bf3f5haP2Q4VW4DDP7/OOLnqqdrIn6qN/P9xe3x0RsnPZrNC0nkDSecNHPwtFq1OQ2BVT0Jr+lClfgWCwr1K7dSE7ZO/NIQQQgghhLBzGo0Gz549qLb6Vyo88TgavWVRj+KkJOInPselMWNRzGaVU968wLBwPP0DATAVF7Nr6RIufr6ML2tMZXjd4XjoPXip5Usqpyx/Qr1dCPW2LnpsTtTwUdQZ+n6xg2NxWSolE0LYBZ0DNB8FdR+wbk8/D9/1g4iW8Mdyyzc7N6nriLr0HFuf+h1D8QlytdpmNikkns1i/5oYjm2Ju+ZYs8n23kdF6ZECqxBCCCGEEOWE1tWVgAkTCP91FW6dOpa0O9WsiUZrex8Nwho3Y+THEbR84EG0OstlmxmJ8ax8723q79GyvMePBLkFWR2z8eJGlkUvw6zIB+G7Ja+omM2JludXfGY+A2ftZOmBa4sTQghxWzZMAbMRMmLgp5Ewrydc2ndTd+Hq6Uj1ZgF0GlqLoW+1ZuQH7ej+WF3qtg+xWkwrtKaP1XFFBcXMfX4bv8w8xP61MSSdz8IkBddyRaYIEEIIIYQQopxxrFKFSrNmkbNpM2nffIPfU09abVeKi0Gns4lpA/ROznQYOpI67TsTNSeChOiTAJzYtonzB/fR8dHR1O/cDY1WS3ZRNlN3TyUlP4UVZ1fwepvXqelTU+UzsH+ujg4818DE8uQKHLqURVGxmRd+OsKxuExeu78uep3tFfeFEGVQh+cgPx0ubLXcvrQH5naDev2h21vgE3bTd+nm5UTNFkHUbGH5si4no4D46Ewq1rYusCacyaSowETcqQziTmWwB9A76Qiu7kVoTR9Ca/ngX8kdrbze2S35PyuEEEIIIUQ5pNFo8LinC2GRi9G5/20V5bnziB02jILTp1VKd/P8KocxZMoHdP/f0zhdWbirIDeHDd9EkHk5EYBNlzaRkp8CwOGUwzy06iE+OfAJecY81XKXF16OsGh0Cx5pVbmkbcGuWB6Zs4eU7EIVkwkh7EZwIxi+Eob+CH61rrb/sRy+aAHrXoX8jH8//ga4+zhTq1UQbl7Wc7waC0y4+/ytrdDExT/S2bX8HEvf38/c57ex+sujMpWAnZICqxBCCCGEEKKEMTGR1FmzyN9/gAv9B5D07lRMBoPasW6IRqulYddejPpkFnXadwag+f398QkOBaBvtb7M6TGHKp5VAChWipl/fD79funH5kub1Qldjjg6aJnavwEfDGyA45VRXHtj0unz+XYOXby9oocQQgCWhbBq9oRxO+G+T8DVz9JuKoJdX8BnTSDxaKk/bI0WgQx/ry2PvtOaLo/WpkaLQFy9HK32KSowkZ1ecM0o1ksn00mNy0Yx3/ycsaLskCkChBBCCCGEECWKU9NwCPDHGHsRzGYyFi3CsGYNAc8/j1f/fjYxV6ubtw+9n3mB+l26E1yjltW25n7N+LTaW6w372POsTkYzUYScxN55vdn6Fq5Ky+1fOmaeVtF6XqoRWVqBnowbtFBkgwFJBkKeGj2br4e3ozOtQLUjieEsAc6B2jxGDQYBDtmwq4IKC4AFx/wr31HHlKj0eDl74qXvyt124egKAqZl/OIj84k/nQG8dEZVPzb3K0AmyNPY0jJx9lNT0hN7ytTCnjjG+xmE1P1CAspsAohhBBCCCFKuDSoT/iqVaTP/5bUWbNQ8vMxpaeT+OqrZP74I4Gvv45L/Xpqx7whles3uqZt38ql7PxxMbXadiSy/7d8dOoz9iTuASwLYO1M2MlHnT6iY8WO1xwrSk+Tyj6seqY9Ty0+yN6YdEK8nWlS+drCgxBC3BZnT+j6BjQbBb+/C3X6gIP1yFIyL4J35X8+/jZoNBp8gtzwCXKjfsdQFEWh2Gg9PUB2egGGlHwACnKNnD+UwvlDlqlsXDz0JfO3htb0xjvQVQquZVjZ//pZCCGEEEIIcVdpHR3xe/x/VFv9Kx69epW05x85QsygQSS++RbFGbZ3SbchJZk9y38E4PTOrWx4cypPK/2Z1m4avs6+AOg0Omr73pnRTcKav4cTi8e24n8dw5k9rDleLnq1Iwkh7JV3JRgwG+rcb91++QR82hiWjoaM2DsaQaPRoHfUWbXpHXW0H1SDsIZ+OLpYj4HMzzZy9kAyWyJPE/nWHpLOZd3RfOL2yAhWIYQQQgghxD/Sh4RQceYMcncNJundqRSdOweKQuYPP+AQ4I//U0+pHfGmePj5023MU2xZNI+CbAOFeblsnPcVwdVrMX/k50SmraSadzUCXK0vU1cURUYN3SF6nZZXete5pj3ZUMDF9Dyah/mqkEoIUW5EvQ6KCY4vg5OroNUT0OF5cPG+Kw/v7K6nUddKNOpaCbNZIfVSNvGnM4mPziDhbCbGAhMADnotAVU8rY49tTuRuFMZJVMKeFZwuSuZxT+TAqsQQgghhBDiP7m1aUP48p9JX7SY1C++QOvtRYXRo9WOddM0Gg31O3cjvGkLti6azx9bNgCQePY0K954jQ739aPtg/2tjskqzGL0utGMrj+a3lV7S6H1LigqNjNu8UGOXMrkjT51Gda6ivzehRClT1GgZi9IOAR5aZaFsHZ+BocWQeeXoPlo0N29kfVarYaAKp4EVPGkSY/KmE1mUi7mEB+dQWF+MTq99UXo5w+lcOFIKqd3JwHg6ef8lykFfHD3cbpr2YUUWIUQQgghhBA3QOPoSIXRo/C87z6KExPQuliPlMn+/XdcGjfGwbfsjzh09fSi15MTqNe5KxvmRJCeEIdiNrN/1c+c3rWNbo89SXjTFgB8fuhzojOieWnbS6w4u4LXWr9GFc8qKp+BfVu8J5YDsZYpKN745Q+OxmXxbr/6OOt11zlSCCFugkYDLcdCw8Gw7RPY/RWYCiE/HdZOhr1fQ/e3oVZvy753mVanJbCqJ4FVPa/ZpigKybHZVm2G1AIMqYmc3JkIgFeAC6G1fKjdKojg6t53I3K5JnOwCiGEEEIIIW6YPjAAl8aNrdqKLl4kfsJEzvW6l/RFi1GKi9UJd5Mq1W3AsOmf027wo+j0llFK2akpJJ49DYDJbCItP61k/92JuxnwywC+OvIVRaYiVTKXB8NaV+F/HcNLbi89EMfg2buIz8xXMZUQwm45e0H3KfDMfmgw+Gp72ln4fih8ez/kpKiX7x9oNBqGvdOG/s83pWWfqoTW9EbnYF3iy0rO58S2BNLic6zaTcVm8rPlPay0SYFVCCGEEEIIcVtSZn6KUlSE2WDg8rvvcuHBQeQdOKB2rBvioNfTeuAQRnz4BZUbNMY7KJhW/SwfsHVaHTO6zOCzLp8R7BYMQJG5iC8Pf8nAlQPZk7hHzeh2y+HKvKyfPdwElyujVo/GZdHn8+3sPJeqcjohhN3yrgwD58DY36Fy26vtBZngWvauztDptYTU8KbFfVXp91xTxszowAMTm9C8dxjB1b3Q6iyjbkNq+lgdl3A2k3mTtrPk7T1s/SGa84dSKMg1qnEKdkWmCBBCCCGEEELclsBXXkbj5ETW8uUAFJ46Rewjj+LZtw8BL7yAPiDgOvegPp/gUB589R3ysjJxcHS02uZ3zsichp+wzLCe7058h0kxEWOIYcz6Mdwffj/PN38ePxc/lZLbr76NQqgR4M7jCw9wMT2P9Nwihs3dyyu96zC6XZjMyyqEuDNCm8GoNXBqNUS9AT3eAe3fpigxFoDeWZ18/8JBr6NiLR8q1rIUVI2FJpIuZOET5Gq1X/xpyxQs6Qm5pCfkcmxTHGjAr6J7yRyuITW8cXKRkuHNkBGsQgghhBBCiNvi4OdHyLT3qBIZiVPdqyvCG1au4vy9vUmb/y2KseyPjtFoNLh5W4/0SU+IY+M3X7LsjVdoctydyO7f0di/ccn2X8//yqNrHqXYbBvTItiaOsGerHy6HR1r+gNgMiu88+sJJv5wGJNZUTmdEMJuaTRQ5354ai9Uu8d6W9wBmFkf9s4BU9l9b9M76ahU2/eaL6PcfZwJqOJhPa2sAqmXcjiy8RJrvjzK3Oe2su6b43c3sI2TcrQdMxqNGG3gD9mb9ec52eO5CSGukr4uhP2Tfm5/9A3qUzEyEsOyZaR9+hlmgwFzbi7JH3xA5tKlBH/+GfpKldSOeVP2rfwZ05U5ZQ+uXYnb7h28PHwsR8MS+PzI5xiKDIyoMwLFpGAswx+01XS7fd1Nr+HrRxrz6cazfLX1AgAezg6YTcWYTaUWUwhxG+z6Pd38l3NSFHTrXkGbmwJrXkDZMxvTPW+i1OipykJYt6JWmwBqtQmgKL+YxLNZJJzJIuFMJmnxuXDleytFAb2z9pr/n0c3xuEb4kZguCd6J/tfePBmns8aRVHkaz87ERERQUREBCaTiejoaCIjI3F1db3+gUIIIYQQQpQybW4ufr+tw2vfPjSKQlGFCsROnIByZTEpW6GYzWSeOkr6sYMopqvVPNfQyrg1bcwB/R90duqMVnP14sB8cz5ajRYnjZMake3akTQNu5M1jKllRifXYwoh7jKdqZDGF7+hYqb1HNwp7nX4I/RhslzD1AlWCsxFUJjhQGGajsJ0HR7VinANvnp1hqlQQ+Lv7pYbGgVHbxNOviY8qxehsdPX47y8PIYOHUpWVhaenp7/ua8UWO2QwWDAy8uL1NTU6z4BbJHRaCQqKoru3bujt7E/0IUQN076uhD2T/p5+VBw/Dgp772H7xNP4Naxo9pxbllW8mU2L/ia2CMHS9ocnJxoPfBhGve8H63u6kiet/e8za7EXUxuNpkulbqoEbdMKe2+rijKNZe8nknOobq/m8zLKoRKytt7uiZ+P9oNb6CN22vVbm4wGFPnV8EzVKVkd865gylsnH/Kqs2jgjMPv9VCpUR3nsFgwM/P74YKrDJFgB3T6/V2/cJm7+cnhLCQvi6E/ZN+bt/0TZrg/sMPaLTWw1uMiYmkzZ1HwHMT0drAVVd+oRUZ+PIUonfvYNO3s8nNzKC4sJDtkd9yescW+k16HU//AA5ePsiKcysAeH7b83Su1JmXW75MiHuIuidQBtypvn4iwcCAWbvpWjuQ6Q82xM1JPuYKoZZy854e1gYeWw8nV0LUm5Bhmb5Ee+xHtCdXQpunoNNL4OB4nTuyHWH1/OnxmI646AziT2eQlZxPxVo+dv3/+2bOzU4H8QohhBBCCCHKir8XVxWTiYRJk8lYtIgLAx+k4MQJlZLdHI1GQ6027Rk1YxaNe95XMt9esdGI65XFsfxd/Wkb0rbkmM2XNtPvl37MPz4fo9kO5yZUWVGxmXGLD1BgNLP6WCL9v9xBTGqu2rGEEOWBRgN1H7AshNVzGjh7W9qLC+DCVtDZV+HR2V1PjRaBdHmkNo++3YYR09rRvHeY2rHKDCmwCiGEEEIIIe6qwjNnyL9SVC26cIGYh4aQvmABtjJ7mZOrG11Hj2Poux/hHxZOt8eexOHKKJdKHpWY1W0WH3b8ED8XPwDyi/P55MAnPPTrQxxOPqxicvvj6KDl9fvq4nFl1Gr05Rz6frGdTaeSVU4mhCg3HByhzZMw/hC0eRp0jtBjqs0senWr3H2c8PRzUTtGmSEFViGEEEIIIcRd5Vy7NlWXLcW5bl0AFKORy9Pe59Ljj1OclqZyuhsXXL0Ww6bNpHL9hlbtaXEXyf9xD4vazuHh2g+jwfIh+0zGGYatHcaUXVPIKsxSI7Jd6lY3kF+ebkf1AMviK4aCYkYv2MfnG89gNttG0V4IYQdcfaHnVJh4Aiq3st52YSss6AuJR9XJJu44KbAKIYQQQggh7jqnqlUJ+34JvqNGlbTlbt3G+Qf6kbN9h4rJbs410x+YzWz4JoLzB/ex9KXJdE2ozqKe31HHt07JPkujl7Lp0qa7HdWuhfu7s+KpdvSqFwSAosDHUdE8segA2QUyNYMQ4i5y97e+bTbD+tfgwhaY3RFWPAmGBHWyiTtGCqxCCCGEEEIIVWgcHQl8cTKV5sxB52e5nN6UmsqlMWO4PP1DlKIilRPevOy0VLJSLJenFxuL2P79dxz88Gs+rPYaL7V8CTe9G00DmtK3Wl+Vk9ofdycHvnq0KZN61iq5Mnf9icv0i9jB2eQcdcMJIcovQxzkZ165ocDhxfBZU/h9KhTKa5O9kAKrEEIIIYQQQlXuHdoT/ssK3Dp0KGlLnzePvP37VUx1azz9Axj18Zc07f0AGo3l41Za3EWWTnkF/61p/Nh1Me+0ewetxvqj2IbYDRSaCtWIbFc0Gg1PdanO/JEt8HS2zMsam5ZHWo78boUQKvGuDE/vgx7vgrOXpa04H7ZOh8+awIFvwWxSNaK4fVJgFUIIIYQQQqjOoUIFKs2eRcBLL4Jej8/Qh3Fr21btWLfE0cWVLiPG8sh7nxAYXqOk/djv6/n1tTfIOXzWakGvvYl7mbh5IgN+GcDOhJ1qRLY7nWsFsOqZ9tQO8uC1++rQKryC2pGEEOWZgxO0fQbGH4ZW40Br+QKI3GRY9SzMag9nNqgaUdweKbAKIYQQQgghygSNVkuFkSOpuvQnAiZPttqmKArmvDyVkt2awPDqDJ36EV1GPo6ji2Wl5XxDFmsjPmHj3C8By3m9v+99AC5mX+TxqMeZvHUyqfmpquW2F1UquLHiqXaMaBtm1W42KzIvqxBCHa6+cO/78NReqPOXqWKST8Cuzy0TSAubJAVWIYQQQgghRJniXKsWWmdnq7asn3/mfJ++5B8+rE6oW6TV6mh6bx9GfvIVNVu1K2mv3a4TYLmkfXqH6TQNaFqybe2FtfRd3pfvT32PSS4bvS3Oeh2aPydkvSJi01n6fL6d00nZKqUSQpR7FarBQwth1G8Q2gzQWKYQ+NvrlbAdUmAVQgghhBBClGmF5y+Q9O5UjPHxxDzyKKmzZqOYbKvw6OHrR5/nXqb/i2/SesBDVKxTv2RbdZ/qzLlnNm+3fRsvJ8v8fNnGbKbumcqwtcM4mXZSrdh2Z9OpZD7ZEE1MWh79v9zB6qOJakcSQpRnVdrAmI0wZgMENbDediYKNk2Dolx1sombIgVWIYQQQgghRJmmdXLEuXZtyw2TiZSZM7k4ajTGy5fVDXYLwpu2oN1Dw6zaFLOZpe++hvP6C/x4z2IeqPZAybZjqccYsnoIH+z9AKNJLmu/XTUC3akb7AlAXpGJpyIP8v7aU5jMclmuEEIlGg1UbG7dZjLCby/Dlvfhs6Zw8DtZCKuMkwKrEEIIIYQQokzTh4ZSZeF3+D35JGgtH2Hy9u7lQt8HyN64UeV0t+/oxnUkRp/ixNbfWf7ySzxk7MjcHnMJ9woHwKyYOZ1xGoc/F0URt6yijyvLxrVlQNPQkrZZW84xcv5eMnKLVEwmhBB/EbcPMi5Yfs5JgpXPwOyOcO53dXOJfyUFViGEEEIIIUSZp3FwwH/8M1RZ8C0OQUEAmLKyiHvqaZLefhtzQYHKCW+dg6MjTm5uABTkZLN+1mdcmP0zXzedwbNNn8VD78FrrV+7Zi5RcWuc9To+HtSIt/rURae1/E63nUmlzxfb+SMhS+V0QggBVGkLT+6G2vdfbbt8HBb2h0UD4fIJ9bKJfyQFViGEEEIIIYTNcG3RgvAVy/Ho3q2kLSNyCTGDBlMUG6tisltXr1NXRn0yq2ThK4C4k8eJfOk5av/hxNoHVpeMZv3T7sTdfHPsG4xmmTbgVmg0Gka2q8riMa3wc3cEIC4jn4Ff7eSXw/EqpxNCCMCvBgxZDCNXQ0iTq+1nN8CsdrByPGTb3lQ59koKrEIIIYQQQgibovP2JvSzzwh66y00Tk4AmAwGtJ6eKie7dW7ePtw3fhIDX3kb78BgAMymYvYs/4GlL08m5sjBkn0LTYW8s+sdPj34KYNXDebg5YP/drfiOlqHV2DVM+1pVMkbgAKjmYhNZykqNqsbTAgh/hTWHsb8DgO+Aa9KljbFDAcXwLqX1c0mSkiBVQghhBBCCGFzNBoNPkMeourSn3CqXZuQDz7AwcdH7Vi3LaxRU4Z/9AWt+j+EVmeZczXzciLLpr1JeoJlZOXexL3E5cQBcDbzLCN+G8GbO98ksyBTrdg2LdjLhR/+15qHmlfCw9mB2cOa4+ggH5WFEGWIVgsNB8HT+6DbW+DkCVoH6PyK2snEFfKuIYQQQgghhLBZTjVqUPXnZbi1bmXVXpyaSu7evSqluj16RyfaDxnG8OmfE1q7HgANu/bEN8SyMFOHih2IvC+SehXqlRzz85mf6bOiDyvOrkBRFFVy2zJnvY73BzbgtwkdqernZrXNbJbfpxCijNC7QPuJMP4QDJwLftWtt5/ZAOc3qxKtvJMCqxBCCCGEEMKmabTWH2sUs5mEl17m4oiRJM+ciWK0zXlKK1SsxENvvU+vJyfS4eGRVtvq+NTm80Yf8EqrV3DXuwOQWZjJ6zteZ9S6UZzPPK9CYtum0WgI9Xaxaiswmhg0exc/7b+kUiohhPgHbn5Qr591m7EAfp0I3z0AiwdD8ilVopVXUmAVQgghhBBC2BXD2rXkbt8OikLarNnEDhtOUZxtLlyk0Wio16krzu7uVu1HotaycPIzhB4oYOm9P9IrrFfJtgOXDzBw1UBWnlt5t+PaFUVReGX5MQ7EZjBp6VFeX3Fc5mYVQpRdx36ErIuWn8+sg6/awKoJkJOsaqzyQgqsQgghhBBCCLvi2asX/s89Bw6WOUzzDx/mQr9+ZK1erXKy0pGTkc72Jd+hmM3sW7mMNa+/yTiPwXzV7SsqulcEQIuWRv6NVE5q28wKuDk6lNxeuDuWoXN2k5xdoGIqIYT4F40fhf6zwdMynQyKGQ7Mh8+awNYPoShP3Xx2TgqsQgghhBBCCLui0enw+99YwhYvQl/RUnA05+SQ8PwLJLz8CubcXJUT3h5ndw+a3dcP3ZUCsiElmeUfTCHjh20s6jiX/zX8H+Maj6OKZxWr48yKjL68GTqthnf61Wf6wIY46iwfnffHZtDn8+0cvJihcjohhPgbrRYaDYGn98M9r4PjlSsfinLg93fhi+Zw5Hswy3vBnSAFViGEEEIIIYRdcmnUiKorluPZp09JW9by5VwYMJD8P/5QMdntcdDraTtoKMM/jKBy/YYl7dF7dhA56VnaJYUxut4oq2Pyi/MZvGowi08uxmQ23e3INm1wi0r8+EQbgr2cAbhsKOSh2buI3HNR5WRCCPEPHF2h4wuWhbCajwbNldKfIR6WPw6b31M3n52SAqsQQgghhBDCbunc3Qn9cDohH7yP1tUVgKLYWGKGPEzhhQsqp7s9viGhPPjaVO596jlcPDwBKMrPZ9O3s4l89QUunz9bsu+co3M4nXGa9/e+z9A1Q/kjzXYLzGpoXMmbVc+0p1VVXwCMJsv8rC//fJTCYilYCyHKIPcAuH8GjNsFNXpa2hycodlIVWPZKymwCiGEEEIIIeye1wMPUHX5zzjXr2+5fd99OFWtqnKq26fRaKjb8R5GzZxNg3t6lLRfPn+GY5uiAMtiTYYiQ8m2E2knGLp6KNP2TCO7KPuuZ7ZVfu5OLBrTilHtwkraluy9xPd7L6kXSgghriegNjzyIwz/BXpNA6+K1tsv7obcVHWy2REpsAohhBBCCCHKBccqVQiLXIz/c88R+NprascpVS7uHvR4fDwPTfmAChUr4+btQ4eHhwOWIuxrrV9jQa8FVPeuDljmY408FckDKx5gXcw6FEVRM77N0Ou0vNmnHjMeaoSTg5YONfx4tHWV6x8ohBBqC+9smTLgrwpz4MfhloWwts8Aoyzid6scrr+LEEIIIYQQQtgHjaMjfv8be027Yc0a8o8ew/+5iWgdHVVIVjoq1q7HsA8+JTMpESdXN6ttXnHFzG0TwYrk3/jq8FcUmApIyU/hhS0v0D60Pa+0eoVKHpVUSm5b+jepSM1AD0K9XdBpNWrHEUKIW7PrC8i5bPl5w1uwby50fRPqD7QsmiVumPy2hBBCCCGEEOVaUVwciW+8Sfq33xJrB3Oz6hz0VKhY2aotOy2V1Z99yKIXxtMwxoef+yyjY8WOJdu3x2/n4dUPk2fMu9txbVa9EC+8Xa2L8YcvZfLSsqMUGGVeViGEDWg2CpqOuLoQVtYl+HkMfNMVYnaom83GSIFVCCGEEEIIUa4VHD+OUlho+fnECS4MfJDMZT/b1WXzO3+KxFiQj7GwgC0L57LpvY94tfJ4ZnaeSYBrAAAj643EVe+qclLblZJdyBMLD/D9vksMmrWL+Mx8tSMJIcR/8wiEvp/BEzugerer7QkH4dve8P0jkHr2348XJaTAKoQQQgghhCjXPHv1IuzHH3C8suiVkpdH4quvkvD885gMhuscbRs6DRtNox73gcZyOXtKzHmWvP4CbIzmpx7fM77JeEbUHWF1TJ4xj/SCdDXi2qToy9lk5RsBOBafRZ/Pt7PznCwcI4SwAYF14dFl8OjPEFDvavupX+HLVnD8Z/Wy2QgpsAohhBBCCCHKPec6dai6bCnegx4saTOsWcuFfv3JO3hIxWSlw9nNnW6PjWPoOx/hX8VSSEZROLxuNT+++Dwdc+vgoLVeomPWkVn0XdGXn8/8jFkxq5DatrSr7sfyp9pSpYJlFHB6bhHD5u7lm23n7Wo0tBDCjlXvCk9sg76fg3ugpc3BBcI6qJvLBkiBVQghhBBCCCEArasrwe+8Q+jMGWg9PAAwJiQQO2wYqV99hWKy/Xk1g2vU4tFpM+n06GgcnJwAyM1I59eZ77P8/bfIzcwA4HT6ab478R1ZhVm8ufNNRv02ijMZZ9SMbhNqB3my8qn2dK7lD4DJrPDu6pM8+/1h8ots//kjhCgHtDpoOhyeOQidX4YuL4O7v/U+aedAvjiyIgXWMuLgwYM899xzNGrUCE9PT/z9/enYsSMrVqxQO5oQQgghhBDlimevXoSvWI5L06aWBpOJ9IWLMGVmqpqrtGh1Opr3GcCoj78ivFnLkvaUizHorxRdK7hUoGdYz5JtB5MPMnjVYGYcmCELYV2Hl6ueuSNa8HSX6iVtK48k0P/LHVxMk9+dEMJGOLlD55egzVPW7XnpMOce+KYbxO5SJ1sZJAXWMmL69OksWLCAli1b8uGHH/Lqq69SWFhI//79eeONN9SOJ4QQQgghRLmiDw2lyncL8HvqKdDpCJn2Hg4VKqgdq1R5+gfQb9Lr9H3+Fdx9K3DPyMdxdLFc3u7n4scHHT9gdvfZVPaoDECxUsy84/Po/0t/tsZtVTN6mafTanihZy1mPdoMN0cdAKeSsnlw1k4ZySqEsG1bP4KCTIjfD/N7QfwBtROVCVJgLSOeeeYZ4uPjmTNnDo8//jgTJkxg586dtG7dmmnTppGeLpPLCyGEEEIIcTdpHBzwf+Zpqq37DfdOnay2mbKzMRcUqJSs9Gg0Gmq0bMvombOp3rKN1TZDSjL5vx0hsut3jGs0Dr1WD0BCbgJPbXyKiZsmkpSbpEZsm9GrfhC/PN2OcH83AJ7vUROXKwVXIYSwSdXvAf86lp+rdoSQpurmKSOkwFpGtGvXDmdnZ6s2nU7HgAEDKC4uJjo6WqVkQgghhBBClG+OFSta3VYUhcRXXiFm0GAK7OTvdL2TMxqNpuS2oihsnD+Lw+tWEznpWbrk1GVpn6W0CmpVss+GixtYF7NOjbg2pXqAByueasd7/RvwUIvKascRQojbU70bPLEd+nwKPabCX947yjMpsJZxCQkJAPj7+19nTyGEEEIIIcTdkPnjT2RHbaDwzBliBg0mY8kSu1sl3pCSTNyJYwDkZWWy5vOPOPDFPKY3nMK0DtPwdfalhk8NhtYZqnJS2+DprGdoq2uLq9/uuMCF1FwVEgkhxG3QOUCzkRDcUO0kZYZdFljz8vJYu3Yt7777LgMGDKBKlSpoNBo0Gg1vvfXWDd1HdnY2b731Fg0aNMDd3R0vLy9atGjBxx9/TFFR0Z09gSvi4+OZP38+rVq1olq1anflMYUQQgghhBD/zbVZU5xq1gRAKSwkacrbxD3zDMUZGSonKz1eAYGM/OQrarRqW9J28dhhvpv0NBUOZbP8vmV83OnjkmkD/rQhdgPZRdl3O65NWn00kbdWnaDvF9v5/dRlteMIIYS4DQ5qB7gT9u7dS+/evW/5+NjYWDp37kxMTAwArq6uFBYWsn//fvbv38/ixYvZuHEjPj4+1xyrKAqFhYU39DharRZHR8d/3JaXl0f//v0pLCzk66+/vuVzEUIIIYQQQpQup+rVCfvpR5Knf0jG4sUA5GzYyIVjxwmZPh23Vi1VTlg6PHz96PvcK5w7sJeN874iOzUFk9HIzh8Xc2r7FrqNfQq8ru5/Mu0kz295Hl9nXya3mEyvsF5W0w6Iq8xmhTnbzgOQXVDMYwv2M6FrTZ65pzparfzOhBDC1tjlCFYAHx8funbtyqRJk1iyZAlBQUE3dFxxcTF9+vQhJiaG4OBgoqKiyM3NJS8vj++//x4PDw8OHTrEo48++o/Hx8bG4uLickP/mjb954mAi4qKGDBgAAcPHmTx4sU0bChDroUQQgghhChLtE5OBL3+GhW//BKdtzcAxZcvc3HkSJJnzkQxGtUNWIqqNWvJqI+/onmfAWi0lo+Q6Qlx/DjlZbYtWVCy38f7P8asmEnNT2Xy1sk8seEJLhouqhW7TNNqNSwa04p761s+pyoKzNgQzf8WHsBQYD/PHSGEKC/scgRrhw4dSE9Pt2p76aWXbujYBQsWcOyYZa6hZcuW0aaNZSVNrVbLQw89hNlsZujQoaxZs4aNGzfStWtXq+P9/PyYP3/+DT3WP42ANRqNDB48mPXr1zN//nwGDBhwQ/clhBBCCCGEuPs87umC8y8rSHjxJfJ27wZFIW3WbPJ27abS3G/QuburHbFU6J2d6fToaOq078yGOREknj0NQEjN2iX7vNPuHd7f+z6/X/odgJ0JO+n/S38eq/cYgUqgKrnLMncnB758pClfbTnHh+tOoyiw4eRl+kXs4Othzage4KF2RCGEEDfILgusOp3ulo9dsMDyDWyXLl1Kiqt/NWTIEF599VUuXLjAd999d02B1d3dnZEjR97SY5tMJoYOHcovv/zCV199xYgRI27pfoQQQgghhBB3jz4wkMpzvyFt7jxSPv0UTCYcgoLQurmpHa3UBYSFM+Sd6RzdsI6kc9FUa9aqZFuwezAzu8xk86XNvLf3PZJykygyF/HVsa/w1/rTPLc5VbyrqBe+DNJoNDzZuTr1QrwYv+QQWflGzqfk8sAXO/jkocb0rHdjV2IKIYRQl91OEXAr8vLy2LFjBwD33nvvP+6j0Wjo1asXAOvXry+1xzabzYwYMYKlS5cyY8YMnnjiiVK7byGEEEIIIcSdpdHp8PvfWMIiF+PasiXBb0+x2/lHtVodjXv0pte4CVbtiqKw8uOpOO6OZ9m9PzGq3ih0GsvglxRzCuM2jiMlL0WFxGVfp5r+rHq6PbWDLKNWc4tMPL7wAIv3xKqcTAghxI2wyxGst+rkyZOYzWYA6tev/6/7/bktKSmJ9PR0fH19b/uxJ02axOLFi2nTpg1+fn4sWrTIanvbtm0JDw//x2MLCwutFtYyGAyAZboBox3N/fSnP8/JHs9NCHGV9HUh7J/0c2GPHOrWJWTuN5gB81+e23m7dqP19MS5Xl31wt1hZ/bs4Oy+3Zzdt5uT27fwwKjH6dmrJ5O3TeZizkUu5Vxi4qaJzOs+z26Lz7cj2FPPD2Nb8OqKE/x6LAkfVz3twn3kNVLYBHlPF/boZp7PUmD9i4SEhJKfQ0ND/3W/v25LSEgolQLrgQMHANi1axe7du26Zvv8+fP/tcA6bdo0pkyZck37+vXrcXV1ve1sZVVUVJTaEYQQd4H0dSHsn/RzYe90BgNhM2aiLSwktVdPMtq3B639XUyYcfIoaDSgKGRdTmTF+2/hHladBxv3ZZ5mEUUU0bagLWvXrlU7apnWzQ0cqmgIdTNxZOcmjqgdSIibIO/pwp7k5eXd8L5SYP2L7Ozskp//qzD5121/PeZ2bN68+ZaPffnll3nuuedKbhsMBipVqkSPHj3w9PQshXRli9FoJCoqiu7du6PX69WOI4S4Q6SvC2H/pJ+L8iLl/ffJuvIhzX/1GqpkZhLw7lQc/CqonKyU9e5NWtxFNs2bRUL0SQByYs7imJzI0Lqdaf/IYGr7177OnQiA+/6hLbewmN0X0ulaO+Cu5xHieuQ9XdijP68QvxFSYLUDTk5OODk5XdOu1+vt+oXN3s9PCGEhfV0I+yf9XNi74BdfxMHFhbRv5gKQt2Mnlx58kJD3p+HeoYPK6UpXUNVqDJnyAcc3b2DronkU5OZQlJdL0f5DnMORGuMm4uzuDljmbDUpJhy08rH0ehRF4ZUfj7LmWBKPdwxnUs9aOOjsbxS0sH3yni7syc08l+UV+S88PDxKfv6vYcB/3fbXY4QQQgghhBDi7zSOjgS88AKV5n6Dzt8PAFNaGpfG/o/L73+AuahI5YSlS6PV0uCeHoyaMYu6HbqUtJ/bv4eFL40nJyMds2Jm2t5pTN46mWJzsYppbcPvp5JZcywJgNlbzzNy/j4ycu3reSOEELZMCqx/ERISUvJzfHz8v+73121/PUYIIYQQQggh/o17u3aE//ILbp06lrSlf/stMUOGUHj+gorJ7gxXL2/uffp5+jz/ClpHyxV3AWHhuHn78N6e91hyaglRsVG8ufNNzIpZ5bRl2z21A5jStx4OWsviYNvPptLni+0cj89SOZkQQgiQAquVOnXqoL0y2fzx48f/db8/twUFBZXKAldCCCGEEEKI8sHB15dKs2YR+MrLaK5celh44iQXR49GsbORrH+q2qQFle4dQM3W7en5xAQ0Gg1dKnUpmRpg5bmVvL/3fRRFUTlp2aXRaBjRNozIsa3xc3cEIC4jn4Ff7WT5oTiV0wkhhJDJbv7C1dWVdu3asW3bNn777TcmTZp0zT6KorBu3ToAevTocbcj3hSj0YjRaFQ7Rqn785zs8dyEEFdJXxfC/kk/F+WZx8MP49ikCUmTX8R44QJ+k16gWKMBO+wPRqMRvZs7XR8fj06vx2g00jKgJdPaTWPGijco1phZcmoJzlpnnmn8jNpxy7QmFT1YPq41Ty85wpG4LAqLzUz84QiHL2bwYs+a6GVeVqESeU8X9uhmns9SYP2bESNGsG3bNjZt2sSePXto1aqV1faffvqJ8+fPAzB8+HA1Iv6riIgIIiIiMJlMAKxfvx5XV1eVU905UVFRakcQQtwF0teFsH/Sz0V5phk9Cvfjx4kuLoY1a65uUBTQaNQLdgf8va8X5+XS7XAIFBk5VCOT+cp84s7H0cm5k0oJbcfwUFhapGVXsqWgumDXRXb8EcvYWiac5VO+UJG8pwt78l/rM/2dRrHT6zAyMjJKCo0ATZs25dKlS0yaNInJkyeXtDs7O+N+ZRVLgOLiYpo2bcqxY8cIDQ1lwYIFdO3aFbPZzLJlyxgzZgwGg4F7772XNX/9A6gMMRgMeHl5kZqaiqenp9pxSp3RaCQqKoru3bvL6oRC2DHp60LYP+nnQvy7y6+9jr5iRXzGjkGj06kd57b8W1/fvmQBB1evKLkd75fPtkapjG/7AkNqDVEhqe35fl8cb68+idGk0K22PxEPN0arta/CvLAN8p4u7JHBYMDPz4+srKzr1tfs9rutJk2aEBsbe037hx9+yIcfflhye8SIEXz77bcltx0cHFi5ciVdunQhJiaGbt264erqitlspqCgoOS+Fy9efMfP4Xbp9Xq7fmGz9/MTQlhIXxfC/kk/F8Ja1sqVZP/yCwAFe/cS8uF09EFBKqe6fX/v650eHYWTiwu7ln0PikJoqgt9twfzXc6neDp78kD1B1RMaxuGta1K3VBvPl5/mhlDmuDkJK+lQl3yni7syc08l2WCln8QFhbG0aNHeeONN6hfvz4ajQa9Xk+zZs346KOP2L17Nz4+PmrHFEIIIYQQQtih4uRkuLL4bt6+fZx/oB8GO7zsVqvV0XbQIwx67V3cvC2fr1wLHeixJ5ANS76h0FigckLb0KyKD5FjW+PhbF0IOJucQ1GxWaVUQghRvthtgTUmJgZFUa7776+jV//Kw8ODKVOmcOzYMXJycjAYDOzfv5/nn38eR0fHu3syQgghhBBCiHKjwpgxVPluAQ7BwQCYs7KIf2Y8iW+9hbnA/oqOles3YtgHn1GlQWMAtGgIO67wy7S3yclIVzecjUrMymfI17sYOmc3yQb7e84IIURZY7cFViGEEEIIIYSwVa7NmxO+YjkePXqUtGV+/wMxgwZRcDpaxWR3hpu3DwNfeZt2Dz2K5sriXpf+OMoPb72I+S9ra4jrUxSFZ78/TGpOEftjM7j/8+0ciM1QO5YQQtg1KbAKIYQQQgghRBmk8/Ii9NOZBL09BY2zMwCFZ84SM2gQ6YsXY2/rFWu0WloPGMLgN6fh7lsBgPZDhlOkGDmXeU7ldLZDo9Hw2n11CPGyPGeSswsZ8vUuFu+JtbvnjBBClBV2u8iVsKziZzQa1Y5R6v48J3s8NyHEVdLXhbB/0s+FuDHu/fvj2KgRSZNfpOj0aZSiIrI3b8F90KCS0Z5l2c329cDqtXh46iec3buTwEYNGLdhHNEZ0czpNoca3jXuZFS7USfQjZ+faMWzPx5lz4UMjCaFV5cf5/DFDN68rzZOep3aEYWdkfd0YY9u5vmsUeQrLLsRERFBREQEJpOJ6OhoIiMjcXV1VTuWEEIIIYQQohRojEb81q7F4+gxYic8i8ndXe1Id9zqvNXsKtoFgLvGnUfOtsI3qBpuoZVVTmYbTAqsjNWyOfHqxatV3BVG1zTh7aRiMCGEsAF5eXkMHTqUrKwsPD09/3NfKbDaIYPBgJeXF6mpqdd9Atgio9FIVFQU3bt3R6/XX/8AIYRNkr4uhP2Tfi7ErTFlGdB5Wf+dXxQTgz40FE0Z7Eu309dzjbmM+30cx9OOEx7vRscjfgA07f0AbQY/is5BLsq8ESuPJPLqL39QYDQDUMHNkc+GNKRlmK/KyYS9kPd0YY8MBgN+fn43VGCVdyM7ptfr7fqFzd7PTwhhIX1dCPsn/VyIm6P3q2B1uzg9nYTRj+EQEkzoRx/hWKmSSsn+2630dW+9N7O6z2LUulEEHU4vaT+45hcSo09x37OT8QoILO2odmdg88rUDvHi8YUHiMvIJy23iNTcYnntFaVO3tOFPbmZ57IsciWEEEIIIYQQNizpzbcoTkmh4MhRLvTrT9aqX9WOVKq8nLz4uvvXXOzgzp466Zg0loswE8+eZuFL4zmzb5fKCW1DvRAvVj3dnvbV/RjboSoPNA5VO5IQQtgNKbAKIYQQQgghhA2r8L+x6Ctb5iQ15+aSMGkSCS++hCknV+VkpcfPxY85PeaQWd+dNW2SyHaxLDxSmJvLyo+m8vu3symWxXWuy8fNkQWjW/Jir9rXbMvKk9+fEELcKimwCiGEEEIIIYQNc2nQgKo//4zXA31L2rJ++YULAweQf+y4islKV7B7MHN6zIEgD1a2TyQm6GoB+dDaVXz/xiQykxJVTGgbdFoNDjrrUsCyA3F0+XgzO8+mqpRKCCFsmxRYhRBCCCGEEMLG6dzdCPngA0Kmf4DW1RUAY+xFYoYOJW3uXBSzWeWEpaOKZxW+7vE1Lm4ebG6SSlJbT3RX5si7fP4sWxfPVzmh7Tken8Ury4+RnlvEo3P3MGfreWQtbCGEuDlSYBVCCCGEEEIIO+HVty9VVyzHuWFDS4PRSPKHHxH/7ARVc5Wmmj41mdVtFvdXu593n57P0Hc/xic4BFcvb7o+Nk7teDanko8rbatZFk4zKzB1zUnGf3+YvKJilZMJIYTtcFA7gLhzjEYjRjuch+jPc7LHcxNCXCV9XQj7J/1ciDtDExxM6LfzSYv4ksx580BRcOnUUbW+dif6em3v2rzd+m0wg09oJR56+yOykpNwdHO3ehyz2YRWqyu1x7VHrnqYNbQxn206R8Tm8wCsOpLAmSQDEUMbU9nXVeWEwhbIe7qwRzfzfNYoMvbfbkRERBAREYHJZCI6OprIyEhcXeXNUAghhBBCiPLK5exZ3E+eJOX++0GjUTvOHWUwGzhcdJgOTh3QaDSYCguIi1qFb/0meIRVVzueTTiarmHRWS2FJstzxVWnMLymmTreUjYQQpQ/eXl5DB06lKysLDw9Pf9zXymw2iGDwYCXlxepqanXfQLYIqPRSFRUFN27d0d/Zb4lIYT9kb4uhP2Tfi6EejLmzcOtcxccw6ve8ce6G309Pieecb+PIy4njsfqPcaTDZ9k1SfvEXNoPwB1O3Wj0/Ax6J2c7sjj25NzKbk8GXmI86l5gKUu/1zX6jzesSoaOy/Si1sn7+nCHhkMBvz8/G6owCpTBNgxvV5v1y9s9n5+QggL6etC2D/p50LcXYb160mbMZP0WbMJevUVvAYOvCuFszvZ109knCAuJw6AuX/MxV3nSkVP76vbt2zg8rlo+kx8iQoVK9+RDPaidog3vzzdnud+PELUicsoCizcc4lH2lTF101eq8V/k/d0YU9u5rksi1wJIYQQQgghRDmhKArpC76z/JyfT+JrrxM/8TlMBoPKyW5P7/DevNTypZLbnx79nMx7guj15EQcroxaTYu7yKJXJnJ88wa1YtoMD2c9sx9txnPda+LooOWrR5vi6+aodiwhhCizpMAqhBBCCCGEEOWERqOh8jdz8B48uKQt+7ffON+vH3kHD6qY7PY9UucRxjcZX3J76p6pnK+Yx6PTZuJXOQyA4sJC1n01k7URn1BUkK9SUtug1WoY37UGWyZ1plkVX6ttZrPMNCiEEH8lBVYhhBBCCCGEKEe0Li4Evz2F0M8+RevlBUBxQiKxjw4jJSICxWRSOeGtG9twLI/Vf6zk9ms7XuNQ8WmGTv2Yhl17lbSf2Po7i1+eSErsBTVi2pRgLxer22azwtjv9jNzQ7QUWoUQ4gopsAohhBBCCCFEOeTZowfhK5bj0ryZpcFsJvXzL4gdMQJjQoK64W7Ds02f5aFaDwFgVsxM2jqJvSn76f6/p7lv/CT0zpaCYXpCHIlno9WMapNmbohm46lkZm44w/8W7sdQYFQ7khBCqE4KrEIIIYQQQghRTumDg6myYAF+zzwNWsvHw/z9B0h67z2Vk906jUbDK61eoW+1vgAUm4uZsGkC57POU7tdJ4a9P5OAsGrUbteJBvf0UDmt7XFzckB7ZU20DSeT6ffFDs5czlY3lBBCqEwKrEIIIYQQQghRjml0OvyfeooqixbiEBKMzsuLoNdfVzvWbdFqtExpO4VulbsB8GDNBwnzDAPAJziUh9/5kO7/exqNRmN1XHZ66t2OanMe71SNb0e1xMvFsrr2+dRc+kXs4LfjiSonE0II9UiBVQghhBBCCCEErk2bEr58OZVmz0IfGGi1TTHa3mXgDloHPuj4AVPbT2Vyi8loNVc//jo4OuLobD236Nl9u5k7fiwH165CUWRu0f/SsaY/q55uT51gTwByi0w8seggH647hUnmZRVClEMOagcQd47RaMRog38IXc+f52SP5yaEuEr6uhD2T/q5EGWQqysO9epZ9UuTwUDcI4/iNWQIXkMfvmbU5/Wo2dc1aLi38r0UFxdbtSuKYnUe2Wmp/PblDExGI5u+nc3F44fpOvZpnN3c73ZkmxHsqeeHMS149Zc/WHU0CYCITec4FpfJJ4MaloxwFeWDvKcLe3Qzz2eNIl/N2Y2IiAgiIiIwmUxER0cTGRmJq6ur2rGEEEIIIYQQtkpRCI5cgsfRowDk1KnN5QcfxORuu4XHuOI4Vuav5BG3R/DSegGgmEykHdlH5qljJfs5uLkT1K4rzn4BakW1CYoCmxM1rIzVYsZStO4cbKZ/mFnlZEIIcXvy8vIYOnQoWVlZeHp6/ue+UmC1QwaDAS8vL1JTU6/7BLBFRqORqKgounfvjl4v34oKYa+krwth/6SfC1H2KSYTqR99TNaiRSVtOn9/At+bimvr1jd0H2Wprx9JOcJTm54irziPMM8wvun2Db7OviXbzx/cR9TszyjMzQFAq9PR9qFhNLm3702P3C1vdp9PZ/wPRwj0dObHsS1xcdSpHUncRWWpnwtRWgwGA35+fjdUYJUpAuyYXq+36xc2ez8/IYSF9HUh7J/0cyHKML2ekNdexbNDexJefgVTejqmlBQS/vc4FcaMwX/8M2husP+Whb5exbsKfi5+XMy+SIwhhqc2PcXcnnPxcrKMZK3Vqi3B1aqz+tMPSYg+idlkYnvktySc+oNeT07ExcP+BrCUlg61Avl1fAfMZgVPN2erbUXFZhwdZAmY8qAs9HMhSsvNPJflFU4IIYQQQgghxH9y79SJqiuW49a2raVBUUibM4eYRx6l6OJFdcPdBH9Xf+b0mEOgq2URr9MZp3ly45PkGfNK9vH0C2Dwm9No+cCDJW3nD+7juxfHc/n82bue2ZaEertQydd6mrozl7PpMP13Vh1JUCmVEELceWW2wGo2y3wtQgghhBBCCFFW6AMCqPTNHAImTYIro3oKjh7lQv8BZP26WuV0Ny7EPYRvelydGuBoylHG/z6eQlNhyT46Bwc6DB3JgJenlIxaLS4owMUOp2C7k4wmM8/9eITLhkKeWXKIpyMPkpFbpHYsIYQodWWuwGoymZg3bx61a9dWO4oQQgghhBBCiL/QaLVUeGw0YZGR6KtUBsCcm4vJkKVyspsT5hXG192/xsPRA4A9SXt4YfMLGM3WK0ZXbdyM4dM/p1LdBvR8ciKesuDVTSkwmqhc4eqI1l+PJtJj5lY2nUpWMZUQQpS+MlNgNRqNzJ49mxo1ajB27FjOnTundiQhhBBCCCGEEP/ApUF9qi77Ga9+/XDv2hWfhx9WO9JNq+Vbi1ndZuHi4ALA5rjNvLrtVUxmk9V+7r4VGPTGe1Rv3sqqvTAvj7iTx+9aXlvk4awnYmhTPnu4CV4ullHPKdmFjPp2Hy8tO0pOYbHKCYUQonSoXmAtLCzk888/Jzw8nCeffJLY2FgURVE7lhBCCCGEEEKI/6BzdyPk/WlUnPEJGo3Galvunr0oNjDtW0P/hnxxzxc4ah0BWBuzln2X912z39/PT1EUouZ8wY9TXmHnT5GY/1aUFdb6Ngph/cSOdK7lX9L2/b5L9Jq5lV3n0lRMJoQQpaNUCqzr1q3jxRdfZPDgwfTs2ZMHH3yQt956i6NHj/7rMcXFxXz++edUrVqVCRMmEB8fD1BSXG3atGlpRBNCCCGEEEIIcQdpHB2tbuds28bFESO4NGYsxuSyfyl4y+CWzOgyA0etI2+3fZvWwa2ve8z5g3s5vXMrimJm19JIlk19nZyM9LuQ1nYFejozf2QLpg1ogJujDoC4jHwenrObWVvkClYhhG27rQLrgQMHaNiwIb179+ajjz5i2bJlbNiwgeXLl/POO+/QpEkThg0bRn5+vtVx69ato1atWkyYMIGkpKSSdkVRaNWqFb/++iv79l37raEQQgghhBBCiLLLXFBA4iuvApC7cycX+vUnd+s2lVNdX8eKHVk7cC39a/S/of2rNmlOu8GPotFYPlJfPH6UhS+OJ+booTsZ0+ZpNBoeblmZ3yZ0pGVV3ytt0LSyj8rJhBDi9txygXX//v106dKFP/74418v6VcUhcjISPr3v/omNXnyZHr37k1MTIzVfh07dmT9+vXs2rWL3r1732osIYQQQgghhBAq0To7EzL9Axz8LZeCm9LTSXzqKfxXrUIpLtvzbQa4XruA1SXDpX/cV6vV0XrgEAa9MRV3H0uhMC8rk2XvvcH27xdiNsmUAf+lkq8r349tzWv31eHpLtVLiq1CCGGrHG7lILPZzPDhw8nJySmZi0ZRFNzc3PD29iYjI4O8vLyS9qioKBYvXszJkyf56KOP0Gg0JUXZbt268frrr9OhQ4dSOiXxJ6PRiNFovP6ONubPc7LHcxNCXCV9XQj7J/1cCPvk2Lw5lZYt5fIbb5C3eQsAPtt3kDhpEsHTp6PR61VOeGN+vfArU3ZP4aUWLzGw+sB/3CeoRm2GTP2EqFmfEnv0ECgKe5b/wKUTx+j55EQ8Kvjd5dS2ZUTrSoD1+4DZrPD26lMMbVmRmoEeakUTN0ne04U9upnns0a5hRWlVq9eTZ8+fUoKpX369GHKlCk0bty4ZJ99+/bx5ptv8ttvv6HRaKhRowYXLlwoCdewYUM+/fRTOnXqdLMPL/5FREQEERERmEwmoqOjiYyMxNXVVe1YQgghhBBCiPJIUfDetQu/X1ejvTKiM6deXRKGDgWHWxrrc9fEF8fzVc5XAGjQ8KDrgzRybPSv+yuKQubJo6Qd2QdXPmJrnZyo1LMfenfPu5LZXmxK0LAiVodOo3BfJTNdQhS0musfJ4QQpS0vL4+hQ4eSlZWFp+d/v5bfUoF17NixzJ07F41Gw8CBA/nxxx//dd8BAwawYsWKkmKsRqNhzJgxRERE4FDG31RtlcFgwMvLi9TU1Os+AWyR0WgkKiqK7t27o7eRb7+FEDdP+roQ9k/6uRDlg2HzFpImTkR7ZYoA144dCfrkY7ROTion+3eKovDp4U/57uR3AOg0Oj7s8CGdK3b+z+MSo0+xNuJjctJSqdqkBfc/93LJVZ/i+kxmhUFf7+FYvKGkrVllbz4YUJ8qFWTwUFkm7+nCHhkMBvz8/G6owHpLFc4DBw6U/Dx9+vT/3Pejjz5ixYoVgGVC6/bt2zN79uxbeVhxk/R6vV2/sNn7+QkhLKSvC2H/pJ8LYd88O3fi4MgRVFq0GKWggPx9+1AuXUJfp47a0f7TCy1eoMBUwI/RP2JSTLy4/UUiukbQJqTNvx5TuV4Dhn/wGduWLKDDwyNwdHS8i4ltnx746Ym2zIiK5utt51EUOHAxkz4Ru3jlvjo82qqyFKzLOHlPF/bkZp7Lt7TIVVxcHBqNhurVqxMWFvaf+4aHh1OjRo2SOVdfeOGFW3lIIYQQQgghhBA2Kq9GDYK/jEDn60ulWbNwLuPFVbAMEHq19avcH34/AEazkWc3Pcuh5EP/eZyLhyc9/vcMLh7Wo50uHj/C2X2771hee+Gs1/Fy7zr8+HgbKvtaRq3mG028vuI4w+ftJTErX+WEQghxrVsqsGZlZQFQsWLFG9r/r/v9dZ5WIYQQQgghhBDlg2uLFlTfEIVbq5ZqR7lhWo2Wd9q9Q9fKXQHIL87nyQ1PciLtxE3dT25mBqs/+5BfPnqXTQvmYCqWhYCup0WYL2uf7cAjrSqXtG07k0qPGVtZfihOxWRCCHGtWyqw/rlQldMNzpnz18siQkNDb+UhhRBCCCGEEELYOO3fFuFVFIWM73/AlJOjUqLrc9A6ML3jdNqGtAUgx5jDE1FPcC7z3A3fx/FNUeRlZQJwcM0vLHl9MpmXk+5EXLvi5uTA1P4NWDC6JYGelvpDdkEx+2MyVE4mhBDWbqnAelsPqL3rDymEEEIIIYQQogxK/fxzkt56i0uPjcFkMFz/AJU46hyZ0XkGTQOaAlBgKiC9IP2Gj2/ZbxBdR49Dd2Wh58vnz7DwxfFE795+R/Lam041/Vk/oRP9GodQ2deVV3qX/SkmhBDli1Q7hRBCCCGEEELcdcUpKWRELgEg/8gRLo5+DFNmprqh/oOr3pUvun5B6+DWfN39a1oEtbjhYzUaDY173sfD736Md1AwAEX5eaya8T4b5n5FcVHRnYptN7xc9cwc0oSVT7fDzcl6ve5d59LIypdpF4QQ6pECqxBCCCGEEEKIu87B35/KC75F5+sLQMHx48SOGk1xRtm9/NvD0YOvu39N44DGt3R8YNVqPDrtU2q17VjSdmT9aiJff4GMxPhSSmnfvF0drW5fSs9jzIJ99Jq5le1nUlVKJYQo7xyuv8u/27t3L/fcc8919zt69GjJzzeyP1i+4du4ceMtZxNCCCGEEEIIUbY516pFle8WEDtyFKbUVApPnuTiiJFUnj8PhwoV1I73jzQajdVtRVH4KfonelXthaej53WPd3J15b7xk6hcvyGb5n9NsbGIlJjz/DDlZcZ89g0Ojo7XvQ9x1Vsr/yC3yERukYlH5+5heJsqvHRvbVwdb6vcIYQQN+W2XnEyMjLYsmXLDe3755vQjeyvKMo1b1pCCCGEEEIIIeyPU/XqVPluARdHjKQ4JYXC6Ghih4+g8vx56AMC1I73nxRF4eP9H7PgxAJ+Pf8rs7rNwlXvet3jNBoNDbv2IrhGbX6d8T7pCXF0enS0FFdvwdv96lOw9Ag7zqYB8N2uWLZGp/Dx4MY0q+KjcjohRHlxy1MEKIpyx/4JIYQQQgghhCg/nMLDqbLwOxyCggAoOneOi8NHYLx8WeVk/y0lP4VV51cBcCj5EM9uepZCU+ENH+9fOYxHps2g57gJ1Gnf+Q6ltG+h3i4sHN2KKX3r4ay3lDhi0vIYNGsnH/x2isJik8oJhRDlwS2NYB0xYkRp5xBCCCGEEEIIUY45hoVRZeF3xI4YQXFCIkUxMSS8+BJVvp2vdrR/FeAawKxus3hs3WNkG7PZnbibSVsm8XHnj9Fr9Td0H47OLtTv3O2a9k0L5uBfpeo/bhPWtFoNI9qG0aGGH8//dIRDFzMxK/DV5nNsOpXMJ4MbUzfk+tM3CCHErbqlAuv8+WX3DU4IIYQQQgghhG1yrFSJsIULiR0xEsVoJPjtKWpHuq46FerwZbcv+V/U/8gvzmfTpU28tv01pnWYhlZzaxeNntqxhYNrfgHg0h9H6frYOBydXUoztl0K93fnp8fbMHvreWZuiMZoUjiVlM3ji/az6fnOOOhknW8hxJ0hsz7bMaPRiNFoVDtGqfvznOzx3IQQV0lfF8L+ST8Xony46b4eEEDo/HmYCwrQBAfbxGtEPZ96fNLxE57d/CxF5iLWXFiDi86FV1q8ckvri8RHnyr5+cTW30k8e5p7n5mEX6UqpRnbbv2vfRU6Vvdl0tJjnLqcw5Q+dVDMJoxmmS7gTpH3dGGPbub5rFFk0lO7ERERQUREBCaTiejoaCIjI3F1vf4E60IIIYQQQghR5hUX45CdTbFP2V246JTxFJG5kZgxA9DOqR29nHvdUpE1O+YsyXu3oxRbPuBrdDr8mrXFs1otWRT6BhWb4WSmhga+1mWPgmJw1IFWfo1CiP+Ql5fH0KFDycrKwtPzv6cZkQKrHTIYDHh5eZGamnrdJ4AtMhqNREVF0b17d/T6G5vXSAhhe6SvC2H/pJ8LUT6URl9XjEaSJk2m4MgRQud+g2N4eCmnLD2/xfzGqztfRcHyUfu5Js/xaJ1Hb+m+MhLjWfvFx6TGXihpq9m6PV1Gj8NJBtPcEkVRGLPwIEXFZt4fUJ9Qb5l6oTTIe7qwRwaDAT8/vxsqsMoUAXZMr9fb9QubvZ+fEMJC+roQ9k/6uRDlw+309eTPvyB340YA4kc/RuX583CuWbM045WaPjX6UKgUMmXXFELdQ+lWtdstn3dA5TAeefdjNi+cy5H1qwGI3r2d5Jhz3D/hJQKrVivN6OVC5J6LbD2TBsD9X+zi9fvrMLh5JRkVXErkPV3Yk5t5Lt/SDM9vv/02b7/9NpGRkbdy+H96+umnadq0Kc2aNSv1+xZCCCGEEEIIYXt8R43EqW4dAExpaVwcPoKCU6euc5R6Hqz5IG+3fZtve31LJY9Kt3VfDo6OdHtsHH0mvoSji2XUamZSIju+/640opY7YX6uJaNWcwqLeXHZMcYs2E+yoUDlZEIIW3ZLBda33nqLKVOmsGjRouvuq9Pp0Ol03HfffTd03+fOnePw4cMcPnz4VqIJIYQQQgghhLAzDj4+VJk/H+cGDQAwZWYSO2Ik+cf/UDnZv+tfoz9BbkGldn81W7dn2AefERheA1cvb3o88Wyp3Xd50raaH2sndGBQs4olbRtPJdNj5lZ+PZqgYjIhhC27pQLrzfhzileZ6lUIIYQQQgghxK3SeXlRed5cXBo3BsCclcXFUaPIP3pU3WA3qMhUxKQtk9h0cdMt34d3YBAPvzOdwW+8h7uPr9W2Ylm9/YZ5Ouv5cFAjvhneHD93JwAy84w8HXmIZ5YcIiO3SOWEQghbc8cLrEIIIYQQQgghRGnQeXhQ6ZtvcLkypZw5O5uLo0aTd/CQysn+W0FxAeN/H89vMb/xwpYX2J24+5bvS+egp0LFylZt+TnZLHjhSQ6sXiGDm25Ct7qBrJ/Ykd4Nro40XnUkgZ4zt5KaU6hiMiGErZECqxBCCCGEEEIIm6Fzd6PynK9xbdkSAHNuLpfGjCH/2DGVk/07vVaPl5MXAEXmIsb/Pp7DyYdL5b4VRWHdVzPJTEpk83ffsOLDd8jPyS6V+y4PfN0ciRjalE+HNMbLxbKgTbvqfiUjW4UQ4kZIgVUIIYQQQgghhE3RurpSafYs3Nq2AcCxRnUcq1ZVOdW/02l1vNv+XTpX6gxAfnE+T254klPpt79Ql2I24xtydT7R8wf2snDyeOJPn7zt+y4vNBoNDzQOZf3EjgxsWpG3+tRTO5IQwsZIgVUIIYQQQgghhM3RurhQ8auv8B05kspz5qBzd1c70n/Sa/V81OkjWgW3AiDbmM3jUY9zPuv8bd2vVqej4yOj6P/Smzh7eFruOy2FH956kb2/LEUxm287e3kR6OnMx4Mb4eWqt2r/5XA87/x6ggKjSaVkQoiyTgqsQgghhBBCCCFsktbJicCXXkTn6WnVXlaLik46Jz7r8hmN/RsDkF6Qztj1Y4nLjrvt+w5v0oLhH3xGaG3L6EvFbGZb5Lf8/MEU8gxZt33/5VVSVgGvrzjO3O0XuP/z7RyNy1Q7khCiDJICqxBCCCGEEEIIu2HKzCRmyMNkb9yodpR/5Kp3JaJbBLV9awOQnJfM2PVjSc5Lvu379qjgx+A33qP1gIdAowEg5vABFk5+hksnyu4ctWXZkbhMCootBfuzyTn0/3Inn0RFYzSVzSK+EEIdUmAVQgghhBBCCGEXTDk5XBz9GAVHjxL37AQM69arHekfeTp6Mrv7bKp6WeaNjcuJY8aBGaVy31qdjnYPDWPgK2/j6uUNQE5GOunxl0rl/subnvWCWP1MexqEWhYpM5kVPtt4hn4ROzidJIuJCSEspMAqhBBCCCGEEMIuaJ2dcaxWzXKjuJj4557DsGaNuqH+ha+zL193/5pQ91CaBTbj1Vavlur9hzVswrAPPqNy/YbUbNOBht3uLdX7L09qBHrw85NtmdCtBg5ay8jgPxIM9Pl8O7O3nMNkVlROKIRQm4PaAYQQQgghhBBCiNKgcXAg5P1paBwcyFq+HEwm4l+YhFJcjFffvmrHu0aQWxDze87H29kbFweXUr9/dx9fBr76DiajEc2VKQP+lJmUiHdQcKk/pr3S67RM6FaTrrUDee7Hw5xJzqHIZGba2lNsOHmZmUOaEOpd+v8PhRC2QUawCiGEEEIIIYSwGxqdjuCp7+I96EFLg9lMwosvkfnzcnWD/Ytg9+BriqtZhVkUmYpK5f61Wh16J2ertnMH9jJvwuPs+HExZrOpVB6nvGhQ0YtVz7Tnfx3D/5zmlvMpuTg5SHlFiPLstkawbtmyhfDw8FLd9/Lly7cTSQghhBBCCCFEOafRagmaMgUcHMhc8j0oComvvIJSbMRn8GC14/2n1PxUHo96nEoelfio00c4aEv3wtPczAx++3IGimJm97IlxJ08xn3PTMLdt0KpPo49c9breKV3HbrVCeSFn47wSu/a+Lk7qR1LCKGi23qlLigoICYm5j/3+fMyhBvZ96/7CyGEEEIIIYQQt0qj1RL0xhto9HoyvlsIQNIbb6IUF+M7dKjK6f6ZWTHz5IYnic6IJjojmtd3vM7U9lPRakpvdKSrpxfN+wxgxw8LUcxm4k4c57vJz3Dv089TtXGzUnuc8qBlVV/WT+yIs15n1Z6WU8iOc2n0aRgsNQ4hyolbLrAqyo1N4nyj+93q/uLfGY1GjEaj2jFK3Z/nZI/nJoS4Svq6EPZP+rkQ5YPafd33hRdQtFoyv10AQN7BQ7g/+GCZLXyNbzye8ZvHYzQb+fX8r7joXHip+Uulmrfpff0IqlGL3774mJz0NPKzDfw87U2a9RlA64EPo3OQ5VpulA4wGs0ltxVF4ZWfj7LuRDKrj8QzpW9dKrg5qhfwLlG7nwtxJ9zM81mj3EJFc8qUKTd7yC15880378rj2IuIiAgiIiIwmUxER0cTGRmJq6ur2rGEEEIIIYQQQl2KQoV163BMSydxyEOg013/GBWdNJ5kSe4SzFgKdx2cOtDDuUepF4VNhQVc3rWFvISLJW3OfoEEtrsHvZt7qT5WeXHOAJ/9cbVA7a5XGBJupoGvDCYTwtbk5eUxdOhQsrKy8PT0/M99b6nAKso2g8GAl5cXqamp130C2CKj0UhUVBTdu3dHr9erHUcIcYdIXxfC/kk/F6J8KCt9XVEUMJvRlPHi6p/WXFjD67teR8Hykf3Jhk8ypv6YUn8cRVE4tHYlO39YiNlkWfDK2d2d3uNfpGLd+qX+eOXB2uNJvLnqJBl5V0e/9W8Swuu9a+HhbJ/vd2WlnwtRmgwGA35+fjdUYJVx/3ZMr9fb9QubvZ+fEMJC+roQ9k/6uRDlQ1ns64XnzpGzeQu+o0eVuSkDHqj5AEUU8fautwH48uiXeDp78kidR0r9sVo98CCV6zbg108/wJCSjKIo+AaHlLn/X7aib5NKtK7uzys/H2PD/9m77/CmqjeA49+kTfcAWqCUFsree4PsIaCAIEv2lr1kKfoDBBRlb5U9BAQVRVkW2UOWyF6FAqVltKV0jzS5vz9qYwvdI2nL+3keHnLXOW9u7kmTN+eec/M5ALsv+XH2/gvmd6tGo9LOJo4w++TEdi5ERqXnWs66kbKFEEIIIYQQQohcIvq+Nw8HDOD5/Pn4L12aI+cD6Va2G5NqTzIszzs3j913d2dLXUXKlKPvvGWUqduQt0eMx7FQ4Wyp501RyN6KNf1qM79rVewt4/q2+QVH0XvtWWb8eo3IGJ2JIxRCZKUcl2C1sbHBzMwMcxlUWwghhBBCCCFENom4cB6dfwAAgd98y/MFC3JkkrV/pf4MrzbcsHzh2YVsi9PKzo4OEz+mTJ0GidbHREVy7+K5bKkzL1OpVHSr7c6BCU1oWMrJsH7TmYf8eeuZCSMTQmS1HJfFVBQlR/5RE0IIIYQQQgiRd+Tv3h1Fq+XZ7DkAvFi3HmJjKTRtWo4bLmBktZGExYQRrYvm0/qfZmt8r5atKAqH1q7i5okjVH/7HZr2GYy5hUW21Z8XFc1nzdbB9dh85gHzDtyiSZmCvFOliKnDEkJkoRyXYBVCCCGEEEIIIYyhQO/eqMw1PJ0xA4AXmzajaLUU/vRTVOqcc8OnSqViSp0phsfG5HP9CjdPHAHgn4N78bt9i3cnTCW/i6tR48jt1GoVAxqVoEnZgjhYa157Hf1eRuKaz9pE0QkhMivn/MUQQgghhBBCCCGMLH+P7hSZOxf+TXgFbdvO0xkzUfR6E0eWmEqlei0pd//lfa76X83Wet0rVaX10NGYa+J6rT5/cI+t08Zx69SxbK03rypZ0A5nO8tE6/64/pSm84+w8ogXsbqcdd0JIdJGEqxCCCGEEEIIId5o+d7vgutX8+DfXqsvd+3iyfRPUXQ5dyKim4E3GXBgAMMPDef2i9vZVo9KpaJqq7b0mruQ/K5uAMRERrJ32Xw8v1uBNiY62+p+E7wIj+GT3VfR6hTmH7xNt2/PcN8/zNRhCSHSSRKsQgghhBBCCCHeeI4dO+I6/2swMwMgePduwo4fN3FUyfv2yrcERQcREhPCMM9hPAh+kK31FSxegj5fLqZi4+aGdVf+PMC26R8R6OuTrXXnZbaWZnSv7Y76387Jlx69pP2yE2w85Y1eL/PTCJFbSIJVCCGEEEIIIYQAHN95h6KLFoG5OQXHj8O+efPUDzKRL976gqoFqwLwIuoFQz2H4hfml611WlhZ0270R7w9YjzmlnG3uQc8esDWj8cT4PMwW+vOqyzNzZjStjy7hjfEw8kGgCitnpm/3aDPurP4vow0cYRCiLSQBKsQQgghhBBCCPEvh7fbUHLPrzgPH27qUFJko7FhVctVlMtfDoCn4U8Z+sdQ/CP8s73uys1a0eeLxTi5FQPAvWIVnIq6Z3u9eVmt4vnZN64x/RsUN6w7fS+QtouPs+uCD4oivVmFyMkkwSqEEEIIIYQQQiRgWbLka+sir1xBHxNjgmiS52jpyLetv8XDwQOAR6GPGOY5jJdRL7O9bie3YvT+YhG1O3Sh7cgJqNSSXsgsGwtzZnWqzNbB9XB1tAIgNDqWyT9eYflhLxNHJ4RIibwDCiGEEEIIIYQQKQg7dYqHffryeMwY9NE5a1InJ2sn1rRZg6utKwBeL70YcWgEYTHZP1GSxtKKpn0GYePgmGi9z/UrXD38h/S6zKC3yjhzYEIT3q8ZN6mYvZU53Wq7mTgqIURKJMEqhBBCCCGEEEIkQx8ejt+kySgxMYQfO87jESPRR+ascTFdbF1Y02YNztbOAFwLvMbow6PR6rVGjyUi+CV7ly/gj2+X8fuSr4gMCzV6DHmBg5WGhd2r8V3fWszrUpUijtaJtkvyWoicRRKsQgghhBBCCCFEMtS2trgtW4rKJm4CovDTp/EZPgJ9RISJI0usmEMx1rReg6NlXG/Spm5N0ag1Ro/jztnThAe9iHv810k2TxrFgyuXjB5HXtGmkgvvVC2SaF1IlJYuq09z5PZzE0UlhHiVeUYOGjRoUFbHYaDVGv8XNiGEEEIIIYQQIjk2depQbO1afIYORR8eTsTZszwaNgz3b77FzM7W1OEZlM5fmm9bfcvNFzfpWrarSWKo3qY9No6OeH63gqiwUMKCXvDT3M+o0a4DjXsNQGNhaZK48pLPf7vBpUcvGbjhPB/UdWf6OxWxs8xQekcIkUUy1AI3btyISqXK6liEEEIIIYQQQogcyaZmDYqtX8ejIUPRh4YSeeEiPkOG4L7mO8zs7U0dnkEl50pUcq5k0hjK1muEa5nyHFi9hIf/9l69tP83Hl29TPsxkyjk8fokYiJtomN1PA/9bxzg7ed8OOkVwIKu1ahX0smEkQnxZsvUEAGKomTLPyGEEEIIIYQQIqexrlaNYhs2oHaMuw0/8p9/eDR4CLqQEBNHlrLjj4+z4PwCo37ftivgxPsfz6L5gA8x11gAEPj4Ed9/MpFzv/6IXq8zWix5iaW5GZsG1mHOe5WxsTADwOdFJD3X/MWc328QpZXzKoQpZKgHa5MmTaQHqxBCCCGEEEKIN4515UoU37SRRwMGonv5kqgrV/AZOozi27ehUue8aU4OPjjItOPTiFVi0ZhpGFdznNHqVqnV1GzXgeJVqrF3+QL8H9xHr4vl7O4fKN+oKQ7OBY0WS16iUqnoU784jcs4M2nXZc4/CEJRYO1Jb47e8WdR92pUdctn6jCFeKNkKMF69OjRLA5DCCGEEEIIIYTIHazKl6fY5k08GjgI3cuXFBgyOEcmVwGiddHEKrEArL26FluNLUOqDDFqDE5uxeg9dyGnd37PuT0/0WLgcEmuZoHiTrbsGNaAdSfvs+DgHWJ0eryeh9F51WkmtSnHiGalTB2iEG8MGQVZCCGEEEIIIYRIJ6uyZSm+eRPR9+/j0Lq1qcNJVsdSHYnQRjD37FwAlv69FBtzG3pV6GXUOMzMNTTuNYAKbzXDyb14om0xUZHoYmOxtss5Y9nmFmZqFcOalKJZuUJM3PkP13xD0OkV7Kwk3SOEMeXMn9iEEEIIIYQQQogczrJUqSSTq/rISBNEk7ye5XsyvuZ4w/KX577kV69fTRKLczGP14YcPLppDZsnjeLBvxNiifQrW9ie3SMbMbZlGVqUL0SfesVMHZIQbxRJsAohhBBCCCGEEFnkxeYteHfugvbpU1OHksjgKoMZWmWoYfl/p/+H50NPE0YUx+vCWa4e/oOwoBf8NPczDm/8Fm1MtKnDypU0Zmomti7L2n61X0tibzr9gEeBESaKTIi8zyQJ1oiICI4fP84PP/zAnj17uHz5sinCEEIIIYQQQgghsszLX37h2RdfEPPgAQ/79kPr62vqkBIZU2MMvSv0BkCv6JlyfAonHp8waUwuJUtTvGoNw/Kl/b/x/ccTeOZ9z4RR5W5qdeLk6sm7AczYc522S4/z/dmHKIpiosiEyLuyJMEaFBSEp6cnO3bs4Pfff+fBgwdJ7vfy5UuGDx9OwYIFad68Ob169aJz587UrFkTNzc3Vq1aJQ1dCCGEEEIIIUSuZFu3Lppicbdma318eNi3HzGPH5s4qv+oVCqm1JnCe6XfAyBWH8vU41MJiQkxWUx2BZx4/+NZNB/wIeYaCwACHz9i2/SPOPvLLvR6ncliyyuWH74LQESMjum7rzFgw3meBkeZOCoh8pZMjXrs5+fH+PHj+eWXX9DpEr/pNWjQgJUrV1KtWjUAHj16RIsWLfD29k4yiern58eYMWM4duwY27Ztw8zMLDOhCUCr1aLVak0dRpaLf0558bkJIf4jbV2IvE/auRBvhjeqrRcsSNH16/AdMgTtg4do/fx42KcvruvWYlEs54yJOb32dMJjwjnue5wvGn2Btcra5K9PlVZtca1QiYOrFhPw0Bu9LpaT2zdx/+/ztBk+DoeChUwaX272Te/qzDtwhx8uxCX7j93xp83iY8x4twIdqrq8NpxARrxR7Vy8MdJzPauUDHYZffDgAU2bNuXx48eGhKlKpUqUPLWzs+PUqVNUrlyZ+vXrc/78+df2MQTy73qVSsXMmTP57LPPMhLWG23lypWsXLkSnU7HnTt32LZtGzY2NqYOSwghhBBCCCHeKGYhIbitWYvl8+cAxDo44DNsKNqCBU0c2X9ilVie657jau5q6lASUXQ6Aq9e5OWN/4YSVJlrcH+7ExaO+U0YWe53I0jF9ntqQrT/JVSrF9DTraQeO40JAxMih4qIiKBXr14EBwfj4OCQ4r4ZTrA2b96cY8eOJZswjdeiRQtGjhxJ165dUalU2NnZMXDgQN566y3y5ctHUFAQx48fZ+PGjURERKAoCpaWlvj4+ODs7JyR0N54ISEhODo6EhAQkOoFkBtptVo8PT1p3bo1Go38FRAir5K2LkTeJ+1ciDfDm9rWYwMD8RsylBgvLwDMnJ0punYNFqVKmTiylIVrw7HV2Jo6DHxv3eCPb5YQGuCPe6WqvDd1Biq1zNOdWS8jtMz6/Sa/X/1vEjZnOwvmdKpIy/IZ7yX8prZzkbeFhITg7OycpgRrhoYIOHbsWKLkarNmzRg/fjzly5cnJiaG06dP89VXX+Ht7c2RI0cMwwe4ublx7NgxPDw8EpXXrVs3xo0bR/PmzfH19SUmJoZt27YxduzYjIQn/qXRaPL0G1tef35CiDjS1oXI+6SdC/FmeNPausbFheJbNvNo0GCib95EFxCA76DBFNuwAatyZU0dXpLWX1vPD7d+YGPbjRSxK2LSWDyqVKP//BWc2LaRep17YGFpadJ48oqCjhpW9K5FuytP+PSXqwRFaAkIi2HdqYe8Xdk108MFvGntXORt6bmWM/Tzzw8//GB4/N577/Hnn3/SsWNHypYtS+XKlRk2bBhnz57F3d0dgOPHj6NSqVi0aNFrydV4pUuXZuHChYblo0ePZiQ0IYQQQgghhBAiRzDPn5/iG9ZjValS3AqdDhS9aYNKxvZb21l8cTF+4X4M9RxKQGSAqUPC0saWVkNGYe+U+O7WxzeucXjjt2hjok0UWe73TtUiHJzQhJblC2FjYcaCbtWyZCxWId5UGUqwnj9/3vB4/vz5STZCZ2dnpk6diqIoKIqCjY0N7733XorldunSxTBm6JUrVzISmhBCCCGEEEIIkWOY5ctHsQ3rsW3cGPf167AqX97UISWpdfHWFHcoDsDDkIcM8xxGcHSwiaN6XXREOPtWLuTS/t/4/uMJPPO+Z+qQcq1C9las7V+b38a8RXGnxMNCeAeEExmjS+ZIIcSrMpRgffjwIQAeHh6USmH8mDZt2gBxE1iVL18eMzOzFMs1NzenYsWKKIpCQIDpfy0TQgghhBBCCCEyy8zBgWJrvsM6vidrDuRs7cya1mtwsXUB4G7QXUYcGkG4NtzEkSX2xOsOkcFxid/Ax4/YNv0jzv6yC71ekoEZoVKpKFXQLtG6iJhYBm44xzvLTvD3oyATRSZE7pKhBGtwcDAqlcowBEByEm4vUKBAmsrOly8fAGFhYRkJTQghhBBCCCGEyPEUvZ6nX3xBxN9/mzoUgyJ2RVjbZi1OVk4AXA24ypjDY4iKjTJxZP/xqFqDPvOWUsgjrrOXXhfLye2b2DnrE4KfPzNxdHnD/IO3eRAYwf2AcLquPs38g7eIic2ZQ1sIkVNkKMGq1WoBDLfzJ8cywSDUqfVefXU/RVEyEpoQQgghhBBCCJGjKYrC09mzCdq8hUdDhhJ+7pypQzIo7lCc79p8h4NF3IzZ55+eZ+LRiWh1WhNH9h8nN3d6zV1A3fe6wb9DFvreus7mKaO5fuxPySdkUp/6xanmng8AvQIrj9yj44qT3HwSYtrAhMjBMpRgFUIIIYQQQgghRMYoWi3aRz5xjyMi8Bn2IeFnzpg4qv+UzV+Wb1p9g415XKeqE74nmHZiGrH6WBNH9h8zcw2NP+hPjxlf4lCwEAAxkZEcWLWY3xZ/SWSoJAMzqlRBO34a3oBJbcqiMYtLYN96GkrHFSdZecSLWJ30ZhXiVZJgFUIIIYQQQgghjEhtYYHbqpXYNW0KgBIVhc/wEYSdOGniyP5TpWAVVrRcgaVZ3J2pF55d4Gn4UxNH9Tq3CpXp9/UKKjVtaVjnfekiESE5b4Ku3MTcTM3oFmX4ZVQjyrvYA6DVKcw/eJtu357hvr8M6yhEQpJgFUIIIYQQQgghjExtaUnR5cuwaxmXGFSio3k8ciShR46YOLL/1HGpw+Jmi3Gzc2Nj24242buZOqQkWdrY0HbkBDpM/BgrO3ua9hmEU9GU54wRaVPJ1ZFfRzdiRLNSqOM6s3Lp0Us6rzpNaFTOGTZCCFOTBKsQQgghhBBCCGECagsL3JYsxv7tt4G4oQMejx1H6KFDJo7sP43dGrPnvT2UcCxh6lBSVbZeIwYu/oZqbdonWq+Nicb/obeJosr9LM3NmNq2PLuGN8DDKW7YiJHNSmFvpTFxZELkHOaZOfjcuXO0aNEiS/e9cuVKZkISQgghhBBCCCFyDZVGQ9GFC/AzMyNk3z7Qank8fgJFFyzAoe3bpg4PAI1Z4kSaTq/jryd/0ahoIxNFlDwbB8fX1p3ctol//thHw+69qdOxC2p12ibhFonVKl6AfeMas+3sIwY2Spxw1+kVZG4x8SbLVII1KCiIY8eOpbiP6t8Z/dKyrxBCCCGEEEII8aZRmZvj+vVXqDTmBP+6B2JjCVi1CvtWLVGZZ+pre5bT6rVMPzGd/Q/282m9T+lRvoepQ0rR45vX+Hv/HgBObt+E96ULtBs1EcdChU0cWe5kY2HOkMYlX1u/6NBdztxWU79pNC75pWerePNkeIgARVGy7Z8QQgghhBBCCPEmUZmbU+SLL3B8vwsWHh64r12T45KrAIcfHWb/g/0AzDk7h9/u/WbiiFJWpEw56r7XDf7t/OV76zqbp4zm+rE/Jf+QRS4+fMHakw+4GqSmx5rzPA+JMnVIQhhdht6t+/fvn9VxCCGEEEIIIYQQbzSVmRlFZs9GFxyMef78pg4nSW97vM3NwJusu7YOgM9OfYaNuQ0ti7c0cWRJMzPX0PiD/pSoUZv9KxYR4v+MmMhIDqxazL2LZ2k9dDTW9g6mDjNXC4vWkc9Gw4twLQ9fRNBr7Vl2DKuPs52lqUMTwmgylGDdsGFDVschhBBCCCGEEEK88VRq9WvJVX1EBOGnT2PfqpWJokpsXM1xhGvD2XF7BzpFx6Tjk1jRYkWOHJM1nlv5SvT7ejlHNn7H9WNxk4jdPXsavzu3aDt8HB7Va5k4wtyradmC/DKiAe8tP8aLaBVez8Po82+SNZ+NhanDE8IoMjxEgBBCCCGEEEIIIbKXPjISnxEjeTx6DC+2fm/qcIC4uVY+rvcxHUt1BCBWH8v4I+O5+OyiiSNLmaWNDW1HjqfDxI+xsrMHIDzoBT99OQPvf3J27DldEUcrRlfU4eIQ12v11tNQ+q0/R0iU1sSRCWEckmAVQgghhBBCCCFyqJB9+4g4exaAZ3PmELhxo2kD+pdapWZWw1m0KhbXqzZKF8WoP0dxPeC6iSNLXdl6jeg/fwUe1WoCUKRseYpXqW7aoPIAJyvYPLA2Be3jkqxXHgczYP05wqJjTRyZENlPEqxCCCGEEEIIIUQO5dilC04jhhuWn8/7isC1a00Y0X/M1eZ81eQrw9AA4dpwhh8azr2X90wcWersCjjR5eNZtBg0nPajPkJtZpZou0yAlTElnG3ZNqQeBWzjhgb4+9FLBm88T2SMzsSRCZG9JMEqhBBCCCGEEELkUCqVikLjxuE8ZrRh3fMFCwlYvdqEUf3HwsyCxc0WU7NQXG9QF1sX8lnmM21QaaRSqajx9rvkcymSaP0Tr9vsmj2d4OdPTRRZ7lamsD1bB9fD0VoDwI0nITwIDDdxVEJkL0mwCiGEEEIIIYQQOVzBUaMoOGGCYdl/6TL8ly3PET0trc2tWdlyJd3KdmPd2+twsnYydUgZpo2KYv+Khfhcv8LmKWO4fuzPHHGOc5uKrg5sHlQX9wLWbBtSnwpFHEwdkhDZShKsQgghhBBCCCFELuD84TAKTZliWA5YtQr/xUtyRALQzsKO/zX4Hw4WuTuRFhLojy427nb2mMhIDqxazG+LvyQyNMTEkeU+1dzzcfijZlRxczR1KEJkO0mwCiGEEEIIIYQQuYTToIEU/uQTw3Lgd98R/PPPJowoeRHaCL6/+X2OSACnlVNRd/p9vZxKTVsZ1t09e5pNk0fz4J+LJowsd9KYJU47KYrCL5d80elzzzUhRFpIglUIIYQQQgghhMhFCvTri8uM/wFg27AhDu++a+KIXucX5ke//f2Yd24e3135ztThpIuljQ1tR46nw8SPsbKzByA86AU/fTmDP9d/gzY6ysQR5k56vcJnv15j/A//8PHPV9BLklXkIeamDkAIIYQQQgghhBDpk/+DDzAv7IJtg/qoLS1NHc5rvF56cSfoDgAr/llB6XylaVm8pYmjSp+y9RrhWqY8B79ZyoPLfwPwz8HfeXT1H94ZN4VCHiVNHGHucuNJCDvO+QCw88JjLMzVzO5UGZVKZeLIhMg86cEqhBBCCCGEEELkQvYtmqO2tk60LvbFCxS93kQR/aeJWxPG1hxrWP745MeGhGtuYlfAiS4fz6LFwA8x11gAEPz8qYmjyp0qF3Vk2Qc1UP+bT9361yPm7L2Zq4aQECI5kmAVQgghhBBCCCHyAO2zZzzo+QFPPpmOotOZOhwGVx5MuxLtAIiMjWTs4bG8iHph4qjST6VSUaNtB/rMW0qhEqVo1KOv9F7NoPZVirCoe3XiO62uO+nNgj9umzYoIbKAJFiFEEIIIYQQQohcTomNxWfIELSPHhH8yy/4TZ2GEhtr0phUKhWfN/ycik4VAfAN82Xi0YlodVqTxpVRTm7u9JqzgFrvvpdovS5Wy91zp6UnZhq9V6MoX3WpalheeeQey/+8a8KIhMg8SbAKIYQQQgghhBC5nMrcHOexY0GjASDk99/xnTQZRWvaZKaVuRXLmi/D2doZgIvPLjLv3DyTxpQZZuYa1GqzROvO/LidPQu/4LfFXxIZGmKiyHKX7nXcmd2pkmF5oecdvjt+z4QRCZE5kmAVQgghhBBCCCHyAIfWrXFbuhTVv0nW0AMH8J04ESUmxqRxFbYtzNLmS7FQx41huvPOTn649YNJY8oqQU98OffLjwDcPXuaTZNH8+CfiyaOKnfo28CDT9+pYFj+Yt8tdl7wMWFEQmScJFiFEEIIIYQQQog8wr5Fc9xWrkBlEZfMDPU8xOOx49CbOMlatWBVZjScYVj+68lfeeKW+vxFitJhwjSs7B0ACA96wU9fzuDP9d+gjY4ycXQ535DGJZn8djkASjjb8lZpZxNHJETGmJs6ACGEEEIIIYQQQmQduyZNcP9mNT4jR6FERRF29CiPR43Gbfky1FZWJourY6mOeAV5Ya42Z3SN0ajiZzrK5crUa0iRsuU5+M1SQ+/Vfw7+zqOr/9B+zCQKlyxt4ghztlHNS2NnaU67yi4UcjDd9SlEZkgPViGEEEIIIYQQIo+xbdgQ92+/RWVtDUD4iRM8HjnS5D1ZJ9SawNiaY1Gr8lY6wi5/AbpMm0mLQcMx18T1Hn7h95htn37E2d070et1Jo4wZ+vf0OO15Gpe6OEs3hx56x0tF3v06BF9+vShfPnyODg4YGdnR+XKlfn8888JDQ01dXhCCCGEEEIIIXIZ23p1KbbmO9Q2NgBYlCptGJ/VVJLqtRoQGYBe0ZsgmqylUqmo8fa79PlqKYVKlAJAr9Nxcsdmrhw6aOLocpcorY6hmy/wx/Wnpg5FiDSRBGsO8ezZMx4/fkznzp2ZN28eCxcupEGDBsyZM4fmzZujNfHMj0IIIYQQQgghch+b2rVxX7eWAoMGUfiTj3Pcbfn/PP+Hrnu6suLSClOHkmWcirrTa84C6nXujkqlppBHKaq0aG3qsHKNyBgdAzec59DN54za9jdHbj83dUhCpErGYM0h6tSpw9GjRxOt+/DDDylbtixTpkzB09OT9u3bmyY4IYQQQgghhBC5lk2NGtjUqGHqMF4TGBnIMM9hRMZGsubqGkrnK037knnje6+ZuYa3evajRPXaWNnZY2aeuOewXqdDbWZmouhyNgtzNYUdLAHQ6hSGb7nIhgF1aCgTYIkcTHqw5nAlSpQAICgoyMSRCCGEEEIIIYTIKyKvXOHRoEHogoNNFoOTtRNjaowxLP/v9P+4HnjdZPFkh6LlK+Lk5p5onf9Db9aNG2aYEEskZqZWsaBbNdpXcQEgOlbP4E0XOP/ghYkjEyJ5kmDNYaKioggICMDHx4e9e/fyySefYGVlRZMmTUwdmhBCCCGEEEKIPCDy+nUeDRlK+OkzPBw4kFgTdujpU6EP75V+D4BoXTRjD48lIDLAZPFkt9iYGPYtX0CI/zN++nIGf67/Bm10lKnDynHMzdQs7VmDVhUKARCpjRs24B+fl6YNTIhk5MkEa0REBPv372fOnDl06dKF4sWLo1KpUKlUzJw5M01lhIaGMnPmTKpUqYKdnR2Ojo7UqVOHhQsXEpONsy6uXbuWggULUqxYMd59913UajW//vor7u7uqR8shBBCCCGEEEKkQqXRGCa7ir5xk0f9BxD7wjS9A1UqFZ/V/4zqBasD8DziOeOOjCNGl33fu01JGx2FndN/t7r/c/B3tk4bz7P7XiaMKmfSmKlZ2bsmTcoWBCAsOpZ+685yzdd0va6FSE6eTLCeO3eO9u3b89lnn7F7924ePXqUruMfPnxI1apVmTVrFteuXUNRFKKjo7lw4QKTJk2ifv36yd6yrygKUVFRafqXVKL2vffew9PTkx9//JGJEydiZ2dHSEhIhs6DEEIIIYQQQgjxKquyZSm+ZTPmBeMSV9F37vCwXz9i/f1NEo+FmQWLmy+msE1hAK74X2HWmVkoimKSeLKTtb0DXabNpMWg4ZhrLAB44feYbZ9+xNndO9HrdSaOMGexNDfj2z61qF+yAAAhUbH0XXeW209DTRyZEInlyQQrQP78+WnZsiWTJ09m+/btuLi4pOm42NhYOnTowIMHDyhSpAienp6Eh4cTERHBjh07sLe359KlS/Tp0yfJ4x8+fIi1tXWa/tWsWfO1493c3GjVqhXvv/8+CxcuZOrUqXTr1o1Dhw5l6nwIIYQQQgghhBDxLEuWjEuy/vtdOcbrHg/79Uf7zDQztjtbO7OsxTKszKwA2HNvD5tvbDZJLNlNpVJR4+136fPVUgqXLA3ETXp1csdmfpj5McHPn5o4wpzF2sKMdf3rUKt4fgCCIrQs+/OuiaMSIjFzUweQHRo3bsyLV25vmDZtWpqO3bRpE1evXgXgp59+okGDBgCo1Wp69OiBXq+nV69e7Nu3jz///JOWLVsmOt7Z2ZkNGzakqa78+fOnuk+XLl2wsrJiw4YNtGrVKk3lCiGEEEIIIYQQqbHw8KD4ls087N+fWL8nxHh787BfX4pv3IimSBGjx1PRqSKz35rN5GOTAVh0cRFN3JpQwrGE0WMxBqei7nwwez5nftzBuV92oSh6/G7fYNPkMbQbOYEy9RqaOsQcw9bSnA0D69Bn7VkcrTUs6FbN1CEJkUieTLCamZll+NhNmzYB0Lx5c0NyNaGePXsyffp0vL292bx582sJVjs7OwYMGJDh+l8VGxuLVqtNdkgCIYQQQgghhBAioyzc3Sm+eQuPBgxA+/gx2oePeNi3H8U3bURTtKjR42nr0RavIC82XNvAzIYz82xyNZ6ZuYa3evalRPVa7F+5kODnz4iNicbOycnUoeU4DlYatgyqh6VGjZUm43kfIbJDnh0iICMiIiI4deoUAO3atUtyH5VKRdu2bQH4448/sqzuZ8+eJbn+22+/RafTUa9evSyrSwghhBBCCCGEiGfhVpTiWzajKV4MAO3jx0T8fclk8YysPpJdHXbRoVQHk8VgbEXLV6TvV8up1KwV9bv0pEjpcqYOKUdytNG8llz1D43mWUiUiSISIk6e7MGaUTdv3kSv1wNQuXLlZPeL3/b06VNevHhBgQIFMl331KlTuXHjBq1bt6Z48eKEhYVx7NgxfvvtN8qVK8e4ceOSPTY6Opro6GjDcvykWFqtFq1Wm+nYcpr455QXn5sQ4j/S1oXI+6SdC/FmkLaeSzg7U3T9enyHDCVfn97YtH3bpK+Zu637G3fNqDUaWg4ZhaIoiZ67Xqfjn4O/U6VlWzSWliaMMHmmaudPQ6Lov+ECoGLb4No42eXM8yNyp/Rcz5JgTcDPz8/wuGgKt0Ik3Obn55clCdYuXboQGBjIpk2b8Pf3x9zcnNKlS/Ppp58yadIkHBwckj32yy+/ZNasWa+t/+OPP7Cxscl0bDmVp6enqUMQQhiBtHUh8j5p50K8GaSt5w6qwYNQNBrYt8/UoSTyT8w/PNc9p411G1OHYnQvrl3ixZULnP39Fwo3bI5VAWdTh5QsY7fzlTfU3A+Ouzm78/KjjKmow1Zj1BBEHhYREZHmfSXBmkBoaKjhcUqJyYTbEh6TGR07dqRjx44ZOvbjjz9m4sSJhuWQkBDc3d1p06ZNionZ3Eqr1eLp6Unr1q3RaOSdU4i8Stq6EHmftHMh3gzS1nO/sD/+QFOyJJalS5uk/pWXV/Lj9R8BaFGjBR1LZuy7c24UHR7Oht3fA6ANeYmf5x7qdelJrQ6dUatzzjikpmrnVRpE0GvteZ6GRPMkQsU2vwJsGlALB2t5rxGZF3+HeFpIgjUPsLS0xDKJ2wQ0Gk2e/gCT15+fECKOtHUh8j5p50K8GaSt504hBw7wdPIUzBwdKbZhPVblyxs9Bld7V8PjuefmUip/KaoXqm70OExBky8fveYuZP+KhTy774Vep+PMru95eOUS7UdPxLGQi6lDTMTY7bxkIUe2D2tA92/P4B8azTW/EIZuvcTmwfWws5SUl8ic9FzLMslVAvb29obHKXUDTrgt4TFCCCGEEEIIIUReoej1vNi0GfR6dEFBPOo/gMjr140eR/dy3elRrgcAWr2W8UfG8zT8qdHjMBWnou58MHsB9Tr3QKWKS+P43b7BpsljuHbEE0VRTByhaZVwtmXbkHoUsLUA4O9HLxm08TyRMToTRybeJJJgTcDV9b9fxXx9fZPdL+G2hMcIIYQQQgghhBB5hUqtxv27b7GuVg0AXXAwjwYOIvLqVaPHMrXuVOq61AUgMCqQsYfHEhkbafQ4TMXM3Jy3evalx8x5OBYqDIA2KpKD3yxlz8IviAgJNnGEplWmsD1bB9fD8d+hAc55v2DYlgtEaSXJKoxDEqwJVKhQAbU67pRcu3Yt2f3it7m4uGTJBFdCCCGEEEIIIUROZGZvj/u6tVjXqgWAPiSERwMHEXHpklHj0Kg1LGy6kKJ2cZNO33xxk/+d+t8b13uzaPmK9Pt6OZWatTKs8zp/hr/3/WrCqHKGiq4ObBlcF/t/hwY4cTeAkd//TUys3sSRiTeBDEiRgI2NDY0aNeLEiRMcOHCAyZMnv7aPoigcPHgQgDZtcvbshVqtFq1Wa+owslz8c8qLz00I8R9p60LkfdLOhXgzSFvPAywtKbJqJU9GjyHy/Hn0YWE8GjwE11UrDYlXY7A1s2Vxk8UM+GMAEbERHHhwgJIOJRlSeYjRYsgJVOYaWg4ZRfFqNTm8bjU2jvmo1aGLSdtYTmnnFQrbsrZfTQZuukhEjI5C9hYouli0isqkcYncKT3Xs0p5Q37u8fDw4OHDh8yYMYOZM2cmu9+6desYMmQIKpWKM2fOUK9evUTbd+7cSY8ecWO/HDp0iJYtW2Zn2OmycuVKVq5ciU6n486dO2zbtg0bGxtThyWEEEIIIYQQIg9QxcTgumkztl5eAOg1GnwHDiCyVCmjxnFTe5Nt4dtQiEtn9LbtTQVNBaPGkFPERkagj4nGwjF/ovW6mGjMLF6fDPtN4RUM11+q6VhMj0pyqyKDIiIi6NWrF8HBwTg4OKS4b55NsAYFBaHT/TfWRs2aNfHx8WHy5MlMmTLFsN7Kygo7OzvDcmxsLDVr1uTq1asULVqUTZs20bJlS/R6PT/99BNDhgwhJCSEdu3asW/fPqM+p7QKCQnB0dGRgICAVC+A3Eir1eLp6Unr1q1lFlIh8jBp60LkfdLOhXgzSFvPW/RRUTwdP4GIU6cAUOfPj8eB/aiN3Lln/fX1rLi8ggJWBVjQeAHVC1Y3av05WZCfLz/MnEKtd96jVocuqNVm2V6ntHORF4WEhODs7JymBGueHSKgRo0aPHz48LX18+fPZ/78+Ybl/v37s3HjRsOyubk5e/bsoXnz5jx48IBWrVphY2ODXq8nKirKUPb333+f7c8hszQaTZ5+Y8vrz08IEUfauhB5n7RzId4M0tbzCI0G99Wr8B03nvDTp3FbvBhLR0ejhzGs2jBiieX9Mu/jYuti9PpzKl1sLH98s5SYiAjO7NrGw8uXaDf6I/IVNs45ysnt/JpvML9fecLUtuVQSbdWkQbpuZZlkqskeHh4cOXKFf73v/9RuXJlVCoVGo2GWrVqsWDBAv766y/y58+fekFCCCGEEEIIIUQeo7awwG3pEop//z229eulfkA2UKlUjKo+SpKrSShRvSYqVVy6x+/OTTZPGcO1I55v3IRgCV16FESvNX/xzbF7zP795ht9LkT2yLM9WB88eJCp4+3t7Zk1axazZs3KmoCEEEIIIYQQQog8QmVhgXXlSonWKYpCjJcXlmXKmCQmvaLnmM8xmhdrbpL6cwIzc3Ma9eiLR7Va7F+5kODnz9BGRXLwm6Xcu3iO1sNGY+Ng/B7HpuYTFElodCwA6095Y6VRM/lt6ckqso70YBVCCCGEEEIIIUSmBSxfwf33OhOyf7/R6w7XhjP+yHjGHhnLD7d+MHr9OU3R8hXp9/VyKjdvbVjndf4MmyePxvvSBRNGZhodq7ny1ftVDcurjt5j+WEvE0Yk8hpJsAohhBBCCCGEECJTQg8fJmDVKtDp8P1oEsG//W7U+k/7neaIzxEA5p2bx/mn541af05kYW3D28PH0fGjT7Cyj5ugJ/xlED/Pm8mZH7ebODrj617bndnvVTYsL/K8w7fH7pkwIpGX5NkhAkTcLH5ardbUYWS5+OeUF5+bEOI/0taFyPuknQvxZpC2/mawbNQIhy6dCfl5N+j1+E2Zgg4F+7ZtjVJ/M9dm9C3fly23thCrxDLx6EQ2v70ZNzs3o9Sfk3nUqEPvLxZzaM0KHl65BCoVLmXLZ2mbzC3tvGctVyKjtXyx/zYAX+6/hbka+tUvZuLIRE6UnutZpcjIvnnGypUrWblyJTqdjjt37rBt2zZsbGxMHZYQQgghhBBCiDeBXk+hX38l319n4xbNzXn84TCiihkneaVX9GwJ38Ld2LsAFFYXZpj9MCxVlkapP6dTFIWQuzeJjYrEqWotU4djUp6+Kn5/ZGZY7lFSR8PCkh4TiUVERNCrVy+Cg4NxcHBIcV9JsOZBISEhODo6EhAQkOoFkBtptVo8PT1p3bo1Go3G1OEIIbKJtHUh8j5p50K8GaStv1kUReH5jJmE7t4NgJmTE27bt6EpUsQo9YfGhNLvYD8ehj4EoJlbMxY0XoBaJSMkJkfR6zm1YzNVWrXFsZBLhsrIje18yZ9erDx6HwCVCvaNbkjpQnYmjkrkJCEhITg7O6cpwSpDBORhGo0m17yxZURef35CiDjS1oXI+6SdC/FmkLb+5ig6ayaPHj8m4vx5dIGBPB07Do/vt6K2tc32ugtoCrC85XJ67+1NqDaUo4+P8t317xhTY0y2151bXdz7C3/v+5Wrh/+gxYBhVGrWCpVKlaGyclM7n/R2eWL18O3x+3zesRIViuY3dUgih0nPtSw/4QghhBBCCCGEECLLqCwsKLpsKZricUMDRN+6he/kKSg6nVHqL+FYgvlN5xt6rX535TsOeB8wSt25jV6n4+rhPwDQRkVy8Jul7Fn4BREhwSaOLPupVCqmtSvPTyMa0reBh6nDEbmcJFiFEEIIIYQQQgiRpczz58d99WrU9vYARF6+jNbPz2j1NyraiI9qfWRY3nJzC3pFb7T6cwu1mRm95iygcvPWhnVe58+wefJovC9dMGFkxqFSqahV/PWeqy8jYkwQjcjNJMEqhBBCCCGEEEKILGdZsiRuS5dgWbECJXb+gIW7u1Hr71uxL51KdaJVsVasab1GxmFNhoW1DW8PH0fHSdOxso8bZzL8ZRA/z5vJoXWr0UZHmThC49p39QlvfXWEU14Bpg5F5CLy7iKEEEIIIYQQQohsYduwISV+/BFN0aJGr1ulUjGjwQwWNluIjcbG6PXnNmXqNKD//BWUqF7LsO7yH3vZMm08T+/dNWFkxnP2fiCjt/1NWHQsQzZd4Jz3C1OHJHIJSbAKIYQQQgghhBAi26jUiVMPil5PbGCgUerWmGle67karYtGURSj1J/b2OUvQOdpM2k5aATmFpYABPk95sqfb8YYtjWL56dlhcIARGp1DNxwjkuPgkwclcgNzE0dgMg+Wq0WrVZr6jCyXPxzyovPTQjxH2nrQuR90s6FeDNIWxcJ6SMiePbJdGLu38dt6xbMHByMWr9fuB8Tj03k3ZLv0qd8H6PWnZtUatEG1/KVOLh6CVHhoTTq2T/FNpyX2vniblUYuS2W43cDCY/R0W/9ObYMrE0lV+Neq8L00nM9qxT52SbPWLlyJStXrkSn03Hnzh22bduGjY3cBiGEEEIIIYQQImdw2b4dh38uAxBepgy+AweAmZlR6o7QR7A0dCnhSjgqVPSz7UcZTRmj1J1bKXo9sRFhaOwSJxe1oSGY29mjUqlMFFn2itHBd7fU3A2J6/1sa64wupIOV0mxvFEiIiLo1asXwcHBOKTyY5AkWPOgkJAQHB0dCQgISPUCyI20Wi2enp60bt0ajUZj6nCEENlE2roQeZ+0cyHeDNLWRUJaHx98evVG//IlAI49e1Jw+idGq3/V5VWsvb4WADuNHZvf3oyHg4fR6s8LQvyf8/3H43GvVJUWg0dg4+CYJ9t5REwsgzf/zYWHLwFwsrXg+8F1KFXQ1rSBCaMJCQnB2dk5TQlWGSIgD9NoNHnmjS0pef35CSHiSFsXIu+Tdi7Em0HaugDQlCyJ+4rlPBw4CLRagnfswKpMaQr07m2U+sfUGsO9kHsc8TlCmDaMiccn8v073+Ngkfc6J2UHRVHw/HYZ2qhI7l88y1Ov27w9YhzulasDeaudO2o0bBhYlz7rznHZ5yWB4TH033iBnR82oLiTJFnfBOm5lmWSKyGEEEIIIYQQQhiNTe3aFJk1y7D87IsvCTt5yih1q1Vqvmz8JaXzlQbgQcgDphybgk6vM0r9uZ1KpaLWu+9hbR+XkI4IfsnuebM4svFb9LGxJo4u69lbadg8sC4Vi8Q935cRWnxeRJo4KpETSYJVCCGEEEIIIYQQRpWvS2echgyOW9Dp8J0wgeh794xSt63GluUtlpPPMh8Ap/xOsfjiYqPUnReUqdOA/gtWUqJ6LcO6q4cO4LP/Z57cvWXCyLKHo42GLYPrUt09HxsG1uGtMs6mDknkQJJgFUIIIYQQQgghhNEVnDgRu5YtAdCHhuIzYiSxQUFGqdvN3o1FzRZhroobOXHTjU386vWrUerOC2zz5afztJm0HDwScwtLALShweya9TEHVi0m/KVxXkdjcbKzZPfIhjQsJclVkTRJsAohhBBCCCGEEMLoVGo1Rb/+Csvy5QHQPnqE/5KlRqu/jksdPq73sWF51plZeAV5Ga3+3E6lUlG9TXv6frWUQiVKG9ZfP/Ynv86fY8LIsodKpXpt3Y5zjwgKjzFBNCKnkQSrEEIIIYQQQgghTEJta4v7qpWYOTtj16IFhadMNmr93ct1p0e5HgAMqDSAkvlKGrX+vKCAqxvdZ87DuVZDLG3iJn9q2KOPiaPKXoqisMjzDtN+vkrf9WcJjtSaOiRhYuamDkAIIYQQQgghhBBvLo2rKx47tqMpUgSVmZnR659adyotirWgoWtDo9edV6jNzMhXrhKdh3zI/Qtn8ahaI9H2F36+qNQq8ru4mijCrBUUoWXHuUcAXPMNYcCGc2wZXA87S0mzvamkB6sQQgghhBBCCCFMysLN7bXkqqIoRqlbo9ZIcjWLWNs7UL1N+0TrFEXBc81yNn00kpM7NhMTFWmi6LJOAVsLtg2th5OtBQCXHr1k0MbzRMboTByZMBVJredhWq0WrTZz3dT1ej2xsbHo9fosiirzYmNjMTc3JywsDHNzuYSFyKukrQuR90k7FyL9zMzMMDc3T3IswJwq/jtJZr+biDeL9skTnk2dRsHpn2BZrpzR67/sf5mbL27Ss1xPo9edG6XUzu+eO83jG9cAOLt7J9eOHuKtD/pTtkHjXPVe9qri+a3YOKAWfddf4GWklnPeLxi86Rzf9a6Bpcb4PbFF1kvP3y2VYqyfhES2W7lyJStXrkSn03Hnzh22bduGjY1NhsqytLTE2toaa2tr1Gp1rn7TE0IIIYQQIq9QFAWtVktERATh4eFG6+EnhDFZPHuO23ffYR4WhtbRkUdjRqOztzda/X9H/82vkb+iQ0dv295U0FQwWt15kV6r5cX1S7y8dRUSdN6yKliYgrUaYlnA2YTRZZ5PGKy4YUaULi5vUjGfnsHl9JjLPeO5XkREBL169SI4OBgHB4cU95UEax4UEhKCo6MjAQEBqV4Ar1IUBX9/f4KDg7GxscHW1hYrK6sclWRVFIXw8HBsbW1zTExCiKwnbV2IvE/auRDpoygKOp2OsLAwQkJCsLCwoGjRoqjVOftbvFarxdPTk9atW6PRaEwdjsgF9FFR+A4aRPTVuF6PllWrUHTdOtRWVkapf+21tay6sgoAG3MbNrbZSOl8pY1Sd26Vlnb+8qkfx7/fwINLF/5bqVJRuXlrGnTrjbV9+vIXOcmlRy8ZsOkiEf8OEdC6QiGW9qiKxixnvz+LlIWEhODs7CwJ1jdVfII1LRfAq168eMGzZ89wcXEhf/782RRh5uj1ekJCQnBwcMjxHyaFEBknbV2IvE/auRAZFxkZyaNHj8iXLx+FCxc2dTgp0mq17Nu3j/bt20uCVaSZ9vlzHnTvQezTpwA4vPMOrgvmG+UHOUVRmHJ8CgceHACgqF1Rtr+znfxWOfM7ck6QnnZ+/9J5jm5aQ9ATP8M6S1tbWg8dQ7kGb2V3qNnmr/uBDNhwjihtXC/dfg2K83mnyiaOSmRGevJr8klWGCiKwsuXL7G3t8+xyVUhhBBCCCEEWFtb4+DgQGhoqAwTIPIkTaFCuK9eherfYe9C9u4lYNUqo9StUqn4vNHnVCgQNzSAb5gvHx37CK1exhHOCiVr1KH/gpU06T0QjZU1ANHh4djm8jxE/ZJOrOlXGwtzNYUdLOnXwMPUIQkjkgSrMIiNjSU6OhpHR0dThyKEEEIIIYRIhb29fZZMbCtETmVVoQJF538N//ZaDVi+gpD9+41St7W5NctaLMPJygmA80/P89W5r4xS95vAzFxDnY7vM2jJt1Rs0oIKbzXDrXylRPvkxh+PGpcpyJp+tdn5YQNKF7IzdTjCiCTBKgx0urixQmQWXyGEEEIIIXI+M7O4War1CSaNESKvsW/ZkkIfTTQs+037mMirV41St4utC0uaL0Gjjrvl/YfbP7Dz9k6j1P2msMtfgHajJtJ21IRE6xW9nl2zp3Pmp+1oY6JNFF3GNC1bkOJOtonW6fRKrkwYi7STBKt4jUwyIYQQQgghRM4nn9vFm6LA4ME4dukCgBIdjc/IkcQGBBil7uqFqjOjwQzD8pdnv+T80/NGqftNolabJVq+fvwwPtevcHrn92ycOJK7507n2gSlVqdnwg//8PXB27n2OYjUSYJVCCGEEEIIIYQQOZZKpaLIzBlY164FQL7OXTArUMBo9Xcq3Yl+FfsBoFapCYwMNFrdb6qwwABU/06AGeL/jD0Lv+DHuZ8R+PiRiSNLH0VRGLfjEnsu+7H66D2W/ell6pBENpF7wYUQQgghhBBCCJGjqSwscFu+nIi//sKhXTuj1z+h1gSCo4PpUa4HVQpWMXr9b5r67/ekTL2GHN7wLY+uXQbg0dV/2DR5NDXadqBB1w+wss35Y5yqVCoalnJm39WnACw+dAcLczUjmpUycWQiq0kPViGEEEIIIYQQQuR45vnzmyS5CmCuNmfOW3MkuWpETm7F6PrpHDp+9AkOBQsDcWOz/r3vV9aP/5Crh/9AyQVjUPepX5zP3q1oWP7qwC3Wn/Q2YUQiO0iCVQghhBBCCCGEELlSxN+XCN6712T1335x22R1vwlUKhVl6jZkwKJVNOzeG3MLSwAiQ4L547vlBPg8NHGEaTP4rRJMaVvOsPz57zf4/mzuiF2kjQwRkIdptVq0Wm269lcUBb1en6NnIo0fFDo+ViFE3iRtXYi8T9q5EJmj1+tRFAWtVouZmVnqB5hI/HeS9Hw3ESItQn/fy7P//Q8UBZWzM9Y1axqtbp1ex/LLy9l8czPzGs2jTfE2Rqs7J8r2dq5SU7tjV8o2bMLJbZvwOneaik1aks/VLde8twxtVJyIKC0rjt4HYPrua5irFLrUKGriyERy0nNtqRSZwizPWLlyJStXrkSn03Hnzh22bduGjY1Nmo83NzfHxcUFd3d3LCwssjFSIYQwneHDh/PDDz8wZMgQ5s+fb+pwcg1fX19q1qyJoiicPn2a0qVLmzokIYR448XExODj48PTp0+JjY01dThCGF2hX34h35m/AIi1tcVn1Ei0Tk5Gqft6zHW2R2wHQIOGoXZDcTV3NUrdAiKe+WHhmB9zK2vDOkWvJ+T+HRxKlEGVQ390UhTY80jNYb+4G8pVKPQro6ems6TmcqKIiAh69epFcHAwDg4OKe4rCdY8KCQkBEdHRwICAlK9ABKKiorCx8cHDw8PrKyssjHCzFEUhdDQUOzt7VGpVKYOJ1PS2tOgadOmHD58OE37PnnyhJ07d3Lo0CFu3bqFv78/UVFRODo64u7uTvXq1WnTpg3vvvtuuhLwr9q4cSODBw8GYN26dQwYMCBdx5csWZKHD1+/JcLW1hZHR0ecnZ2pWrUqderUoXPnzhQtmvqverNmzeLzzz9/bb1KpcLOzo6iRYtSr149BgwYQJMmTdIU582bN9m1axeHDx/G29ubgIAAzMzMcHJyokqVKjRv3pwPPvgAFxeXNJWX1pgB1Go19vb2lCxZkqZNmzJs2DDKlSuX5L6Q+DV5la2tLS4uLtSqVYvevXvz7rvvZijeeBcuXGD//v2cPHmSmzdv4u/vj0ajwdXVlYYNGzJo0CDeeuutNJe3f/9+1qxZw4ULF/D396dgwYLUrl2b3r1706VLlyxr6+fOnaNhw4ZoNBru3r2Lm5tbou1Hjx6lZcuWyR5va2uLq6srdevWZcCAAbRo0SLZfR88eECpUnGD1/fr148NGzakK9aBAweyefPmdB1z8eJFqlevnmhdcu8zGo2G/PnzU758eVq3bs2QIUMoVKhQiuWPGDGC7777jnfffZdff/01XbEJkZS89DddCFOIioriwYMHuLu75+jP71qtFk9PT1q3bo1GozF1OCIPUbRa/EaOIvKvuCSrRalSuG3dgtou+yc/UhSFmWdn8tv93wAobFOYrW9vxcnaOAnenCYntPMrnvs5uuk78rm40qTvYDyqGa9Hc3ooisLsfbfZ8tcjAKoWdWDnsHqYqeWzUE4TEhKCs7NzmhKsKCLPCQ4OVgAlODg4XcdFRkYqN27cUCIjI7Mpsqyh0+mUoKAgRafTmTqUTAPS9K9p06aplhUVFaVMmjRJsba2TlOZ9vb2ymeffaaEh4dnKPYNGzYYytqwYUO6jy9evHian7+ZmZnSuXNnxdvbO8UyZ8yYkeYyBwwYoMTGxiZbVmBgoNK3b19FrVanWpa5ubkyfPhwJTAwMN3nIT0xazQaZcWKFcmWlfA1Se1f27ZtldDQ0HTHqyiK0rhx4zTV0a9fPyU6OjrFsnQ6nTJ48OAUyxk8eHCWtffWrVsrgDJ8+PAktx85ciTN5zD+OSZ3HXl7exv269+/f7pj7d+/f7piAZRLly69Vk5ajy1QoIBy8ODBFGN68OCBotFoFEA5e/Zsup+TEK/KS3/ThTCF3PL5PSYmRvnll1+UmJgYU4ci8qDY4GDFq2075Ua58sqNcuWVh0OHKnqt1ih1R8dGK7339lYqb6ysVN5YWem9t7cSHZvy59+8ytTtPCY6Slk5+ANlQfd3DP9+/mqW8uKJr0niSY1er1em/XRZ6bjipPIyXN4bc6r05NdkDFYhcoARI0YwcuTIZLfb2tqmeHxAQAAdO3bkzJkzhv27d+9OixYtKFasGPb29gQGBuLl5YWnpyf79+8nNDSU2bNn0759e+rXr5+lzyc9XF1dOXjwoGFZq9USFBTEw4cPOX36NLt27SI4OJjdu3fz559/snXrVjp06JBquevXr6dOnTpA3C+EgYGB/PnnnyxZsoSwsDA2btxIwYIF+frrr1879v79+7Rr1447d+4AUKhQIXr16kWTJk0oUqQIKpUKPz8/jh49yk8//YSvry/ffPMNb7/9Nu+9916Gz0XCmAF0Oh1Pnjxh//79rF69Gq1Wy+jRoylXrhytWrVKsaw5c+bQqVMnw/LLly85deoUCxcuxN/fnwMHDjBo0CB27tyZ7jj9/PyAuNeuW7duNG7cmGLFiqHT6Thz5gwLFy7E19eXzZs3o9Vq2bZtW7JlTZ8+nXXr1gFQo0YNpkyZQqlSpbh37x5ff/01ly5dYt26dRQqVIgvvvgi3bEmdO7cOTw9PQH46KOPUt3/1XapKAovXrzgzJkzLF68mOfPn7N582bc3d2ZM2dOpmJLzcGDB3F1Tf2Ws5Ru269du3aiXrShoaF4eXmxatUq/vrrL168eEGXLl24evUqJUqUSLKM4sWL8/7777Njxw7mzJnDnj170v9khBBCCCGykJmDA+7frOZB9x7ogoMJP36CZ19/jcsnn2R73RZmFixpvoSev/fkWcQzLvtf5vMznzO70Wy5M8PINBaWvD99Noc3fIvf7RsA3L94joeX/6bWu52p17k7FgmGEzA1lUrF3PeqEKnVYWspqbk8IdvTvcLopAdr7sG/PcdmzJiR4TJiY2OVZs2aGcrq2LGj8uzZsxSPefr0qfLRRx8pZmZmypkzZzJUb1b1YC1evHiK+4WGhioTJ0401GVtba2cP38+yX0T9gY9cuRIkvucP39esbCwUADFwsJC8ff3T7Q9PDxcqVixYqLekyEhIcnGFx0drSxdulSxs7NTdu/eneJzyWjMiqIo69evN+zXpk2bJPdJy2vi7e2tFChQwLDf5cuX0x3zO++8o/zwww/J9tz09/dXypYta6jj2LFjSe53+/ZtxdzcXAGU2rVrKxEREYm2h4aGKjVq1DD0Er579266Y02oV69eCqDUq1cv2X0S9mBNqV1ev35dsbKyUiCuN3hSPXWzsgdrar23UxJfRnI94fV6vdK1a1fDfqNGjUqxvD179iiAolarlXv37mU4LiEUJW/9TRfCFHLL53dT92wTb4awv84qNypVNvRkfbF9h9HqvhZwTam9pbahJ+vm65uNVndOkVPauV6vV26cOKJ882HfRL1Zv/mwr3LjxBFFr9ebNL7UBEfGKP88CjJ1GOJf6cmvqbMtcyuEMIrFixdz9OhRAN599112796d6jiKhQsXZsGCBZw4cYKCBQsaIcqMs7OzY+HChcybNw+AyMhIhgwZkuHyateuTY8ePYC4iSGOHDmSaPvHH3/MjRtxv3gOGTKEtWvXYm9vn2x5FhYWjB07lr/++gt3d/cMx5WagQMH4uzsDMD58+czXI6HhwejRo0yLCfsPZxWv//+O927d092bE9nZ2cWLlxoWP7xxx+T3G/JkiWGCTmWL1+OtXXiX5RtbGz46quvAIiNjWXx4sXpjjVecHAwP/30EwC9e/fOcDnxKlasyDvvvAPE9QS9detWpss0FZVKZWhfAIcOHUpx/7Zt2+Lk5IRer0/3uLJCCCGEENnFtl5disycYVh+Ons24f/e4ZfdKjlVYnaj2YblBRcWcNr3tFHqFompVCoqvNWMgUu+pW6nrpiZx/UODQt6wb7lC9j1+SfodToTR5m0lxEx9Fl7ll5r/uLvR0GmDkekkyRYhcjFoqOjDYksGxsb1q1bh1qd9mbdoEEDwyQ8Od2UKVOoV68eAJcvX2bfvn0ZLqtu3bqGxwkn2vL392fNmjUAFClShCVLlqS5zEqVKlGrVq0Mx5QWHh4eQNzrnhnJPf+s1Lx5c8Pje/fuvbZdURTDJEnly5dPdpiKOnXqGCb2+vXXX1EyOC/jr7/+ajhv77//fobKeFXC2+gz+5qYWsmSJQ1Dkfj4+KS4r0ajMQzTsWPHjmyPTQghhBAirfJ17UqBgQMBUKnVxL54YbS625Zoy9AqQwHQK3rmnZ+HTp8zE3lvAgsraxr3GkD/BSspWfO/Ydici3mgTuNk08a27E8vrjwOJjxGR//157jmG2zqkEQ6SIJViFzs4MGDPH36FICePXum2nM1N1OpVIwbN86w/Msvv2S4rISzWuoS/Hq5Y8cOIiMjgbjeq6mNfWts8cnQYsWKZaqc5J5/VkqYcEyqp6u3t7dhLNemTZumWFaTJk0A8PX15cGDBxmKJ76nsru7e5rGMk2LhMnpzL4mpqZSqTD/99f9tMz6Gp8Q9/Ly4u7du9kamxBCCCFEehSa9BGOXbpQbNMmHP+948hYRtcYTXP35lQoUIHvWn+HmTpnJvLeJPmLFKXz1Bl0njYD17IVaNgt8d1sutjYHNOjdUrbcjQs5QRAaFQsfdad5dbTEBNHJdJKEqxC5AC7du2iYsWK2NjYYG9vT5kyZejfv/9rt6+/6tixY4bHbdu2ze4wTS7hxE4nTpzIcDlXr141PE6YbEt4Pt8x8oex1GzatAl/f3+ARJNXZURyzz8rJTyXFSpUeG17/DAMENeDNSUJt9+8eTND8cRfLwknEcuMW7du8fvvvwNxycbChQtnSbmm8uTJE4KD434hj+8pnZKEvaATvtZCCCGEEKamMjPD9Yu52NSsYfS61So18xrPY1O7TbjYuhi9fpG8kjXq8MHs+VjZ2SVa//f+PWyZNg6f61dMFNl/rDRmrO1fmzoe+QF4GaGl95qzeD0PM3FkIi1kqjIhcoCEySaI6xXm5eXF5s2bee+999i4cSOOjo6vHXflyn9/BGrWrJntcZpawYIFcXNz4/Hjx3h5eWWoDF9fX77//nsgrtdefO9IiBt6AECtVlO9evVMx5te3t7ehnFWIa536bNnz9i/fz+rVq0C4oYimDRpUobrCA0NNZQF0KxZswyXlRy9Xp9oTM/u3bu/ts/jx48Nj93c3FIsL+H21G5fT8rz588NwxTUqJH2D9rPnz/n2rVrhmVFUXj58iVnzpxh8eLFREZG4ujomKmxYdPqzp07hIWl/MHK1tY20bAF6fHll18aHnft2jXV/atUqYJGo0Gr1XLx4sVMjYsshBBCCGEMEX9fwrpGdVQqVbbWY6OxeW2doijZXq9Iv/CXQfz103ZiIiPZ+fknlG3QmKZ9BuLgbLo7Q20szFk/oA59153jH5+XBIbH0GvNX+z8sAEezjnrDkuRmCRYRYatPXGftSe8U92vclEH1vZP3GtsyKbzXPNNvav7kMYlGNK4pGE5LDqWVguPolcU1CoVkPwfqTX9alPF7b+k5J83nzF997Vk949nY2nG4Y+apbpfVrCxsaFjx460bNmS8uXLY2dnh7+/P8eOHeObb74hMDCQX375hU6dOuHp6fnarbsBAQGGxylNVhUYGMiTJ0+S3JaZpIwpODk58fjxY2JjYwkJCcHBwSHVYxRF4cWLF/z5559MnTqVly9fAtCjRw+KFy9u2C8wMBAAR0dHLC0tsyX+lAwaNCjZbY6OjsyYMYORI0eSL1++dJcdEhLCyZMn+eSTT/D2jmu39evXp3HjxhkNN1mLFy/m3LlzAHTp0iXJsWlDQ0MNj+1e+RX5VQmHakgtyZiUhMnc9AyjsXr1alavXp3kNrVazfDhw5kwYQJly5ZNd0zp9fbbb6e6T9OmTQ0T3qVFaGgod+/eZfny5WzatAmAMmXKJJoELTnm5uYUKFCAZ8+ecf/+/TTXKYQQQghhbIpej//iJQSuWUOhaVNxGjDAqPWHa8OZdmIarYq1olPpzN2JJrJWVHgY+Yu48ex+3JBXd86c4P7Fc9R9ryu1O3RBY2H874QA9lYaNg2sywdr/uLGkxCeh0bTe+1ZfviwPm75X0/gi5xBEqwiw0KjYnkaEpXqfkXyWb22LjA8Jk3HhkbFJlpWFIWnIWmbTCZGp0+0HKXVp6lOO0vjNQtfX98kk2WtW7dmzJgxtGvXjkuXLnHs2DFWr17N2LFjE+2XMEmV0nihW7ZsYcKECUluS29SxtQSJuNCQ0OTTbAmnGQpKXXq1OGbb75JtC7+fOa0sVcBgoODWbt2LU5OTnz44Yep7j9w4EAG/jvAf1JKlSrFzp07szJEIO528WnTpgFxyczkEpRRUf+1RQsLixTLTJjsjh8jNz3ih1YAyJ8/f7qPT4per2fHjh1YWVkxb948kyTk0+vYsWPJ9pxQqVR06tSJVatWpfkcxSdY48eBFkIIIYTIiSLOXyDw34lsn3/1NRYeHthnw11cSQmLCaPv/r54vfTilO8pPBw9qFawmlHqFqlzKupO77kLuXb0ECe2byIyJJjYmGhO7/yea0cO0azfYErXaWCS3seONhq2DqlHz+/OcOdZGL4vI+m15iw/jWhIQfuc/93jTSRjsIoMs7cyx8XBKtV/TravJ0+cbC3SdKy9VeJkp0qlwsXBkkL2Frg4WKZ4rIVZ4svbSqNOU52FHIz3ZpVST8TChQvz448/GnqtLl++/LV97O3tDY/Dw8OzPL6cKGFSOS29VxMyMzOjZs2aLF26lFOnTr027EL8+TTVuTxy5AiKohj+6fV6Xr58ybFjx+jcuTPe3t4MHz6ciRMnZqh8lUpFhQoVmD17Nv/88w/u7u6Gbb6+vly7di3Jf76+vmkq//r163Tu3JnY2FisrKzYtWtXsj1Graz+++ElJiYmxXITTphlbW2dplgSepFg9tj0JFhnzJiR6PVQFIWIiAiuXLnC5MmTCQsLY8mSJbRq1YqIiIh0x5Ue3t7er8Xy6r/M/FDi6urK+PHj0zUmb/y5fFPee4QQQgiRO9nWq4vTiOFxC4qC38SPiLp9xyh121nYUbNQ3FBuWr2W8UfG8zRcfpzOSVRqNVVatGHQkm+p2b4TKnVcHiHE/xl7Fn7Bj3M/I/DxI5PEVsDWgu+H1Kfkv0MDVC7qgKN16hPSCtOQHqwiw4Y0Lpno9v30eHXIgLSyszTn9LQWhlvD1eq0/0bQskJhWlbIXRPRlCxZktatW7Nv3z68vLzw8/NLlABxcnIyPPb390824Th+/HjGjx+faF1uHQMoflgEc3PzRAnmV61fv94woZFKpcLW1pbChQunmKBzcnIiKCiI4OBgoqOjTd4rUaVS4ejoSJMmTWjSpAm9e/dm27ZtLF68mPbt2yea9OtVc+bMMUyGpVKpsLGxoWDBgsnejj99+nTDbeKv6t+/Pxs3bkwxVm9vb9q0aUNQUBBmZmbs2LEj0fi2r0r42qV223/CBF5qwwkkJWEyNyM9YBOytramSpUqfP3115QpU4Zhw4Zx8uRJvvjiC+bMmZOpsrNb7dq12bBhA/Dv3QBPn3LixAmWLVuGr68vbdu2xdPTM83DRsSfy1eHLhFCCCGEyGkKjhlDzH1vQg8eRB8RweMRI/DYtRPzBN+nssu0utO4H3yfC88uEBAZwLgj49jUdhNW5q/f6SlMx8rWjub9h1K15dsc3vAtj67Fzc/x6Oo/XD9+mCa9BpgkroL2lmwbWp8Np72Z3KYc5mbSTzKnkgRrHqbVatFqtenaP77XnF6vT/0AE1EUxfB/To4zq1SoUIF9+/YBcRP8uLj8Nxtl1apV+fPPPwG4ePFihsdSzch5THhMZq+ZtB77/Plz/Pz8AChXrtxrx8VfGwDFixenYsWK6aqratWqeHl5odfr+fvvv6lXr16a4sqMhDGndh4/+ugjtm3bBsC6deto0aJFou0Jjy1SpEi6nn/COJLallJcfn5+tGrVCj8/P1QqFWvXrqVDhw4pHpPwhwIfH58k942PKeHEVkWLFk33tZbwh4iAgIAUj0+4LbXnPXDgQKZNm8aLFy9Yv349n3/+eYbLSkp6ro20sLW1TXRNVKpUiZYtW9K1a1feeustQkND6d27N1euXElT7/D4nsH58uV7I96LRfZ40/6mC5HV9Ho9iqKg1WoxMzMzdTjJiv9Okp7vJkJktYKzPyfm8WOir19H6+eHz8hRuK5bi9oInSq+avQVfQ/2xS/cjxuBN/js5GfMbTg313Z6SUpeaecOhYvQaeoM7l84y4ltG9BptdR6t4tJn5eTjRmTWpVG0evQ6nUmi+NNlJ7XXRKsecjKlStZuXIlOl1cg/vjjz+wsUn7AMjm5ua4uLgQFhaW6i27OUHCW8XzsoSvRXh4OCEh/00OVrt2bcPj33//nTZt2qS7/PjJotIr4RiaUVFR6S4j/ou0Xq9P87F79uwxPK5Tp85rxyW8lTwiIiLdMdWrV4+ff/4ZgN27d1OhQoV0HZ8R6Yk5YVLy8uXLr+2bmddk6dKlLF26NNntyZUVGBjIu+++a5jo6KuvvuK9995Lte5ixYoZHl+5ciXF/a9evWp47O7unu7XNWGv5adPn6Z4fMJb/aOjo1Otq2TJkrx48YInT57w4MEDChQoYNiWsGeuVqtNd9wJ/5iHhYVlqJ0mlFxbL1asGJ9++ilTp07Fx8eHuXPnMn369FTLi0+wFilSJNOxCfGm/E0XIqvFxMQQGRnJ8ePHiY2NTf0AE/P09DR1COINZ/ZeJ4r5+KAJCSHq8mX+GTqUpz16gBESnV3UXfiO74ghhgMPD6B7pqOpVdNsr9fY8lI7d27+DtrQYA4dOZJofcj9O1g4OGLlbLo7ZJ9Hwt5Haj4orccq5/6+luulZyg4SbDmIaNGjWLUqFGEhITg6OhImzZt0jVGZVRUFD4+PtjZ2SW6pTanURSF0NBQ7O3t89Qvfsm5d++e4XGZMmUSvaadO3emcOHCPHv2jJ9//pn58+dTsGDBdJVvbm6e7rFMIfFt11ZWVukuI354B7VanaZjFUVh3bp1huXu3bu/dlzCW/ptbGzSHVP//v2ZOXMmkZGRbN26lc8++yzbJ7xKT8wJh8RQFOW1fTP7mqRXcHAw3bt359atWwB8+eWXfPTRR2k6tkqVKri6uuLn58dff/2VZKzxbf3s2bNAXO/VypUrp7vdV61aFRsbGyIiInj06FGK5yXhj1KWlpapnsOEvUytra0T7Z9wOAONRpPu1yPhrfd2dnaZfj1Tauvjxo3jm2++wdvbm9WrVzN58mScnZ2TLevZs2eGhFi1atWy/VoTedeb9jddiKwWFRWFtbU1TZo0ydGf37VaLZ6enrRu3VqGlhEmF1W1Kr4DBqBERuFw6R883nqLAsOGGaXuEj4l+OhE3OflQ1GHaF+3PU3d8kaS9U1p56EB/mz5cROxMTFUaNycht37YJu/QOoHZqG7z8OYs+EC/mExmD8rwLp+NbGxkPRedkhPRxJ5BfIwjUaTrjc2nU6HSqVCrVana2xTY4vv+Rgfa17m7e3NoUOHgLhZ3xNOSgRxCZ2JEycydepUwsPD+fDDD/n555/TfV4ych4THpPZayYtx86bN49z584BULNmTdq1a/faPgm/nGckpsKFCzN06FCWLVvGkydPmDhxImv+nXE0NTdu3CAyMpJatWqlq870xPz3338bHru7u7+2b1a+JqmJiIigQ4cOhpimT5/OtGnT0lVGp06dWL16Nbdu3eLcuXPUr18/0Xa9Xs/58+cNCdxOnTpl6PZHS0tL6tevz+HDh7lw4UKK5yXhttTeYyIiIrhx4wYQ1xYLFSr02muQ1rKSktnrOSnJlWFpacm0adP48MMPCQ8PZ+nSpcydOzfZci5evGh4XL9+/Tz/Xiyyz5v0N12I7KBWq1GpVOn+3G8quSVOkbdpqlXD9auv8B07DoDgHTtw7tcPsxTmd8gqbUq2YXToaFb8swIFhemnp7P9ne2UzJexuU1yorzezq8c2k/sv3eZ3jxxhHsX/qJ+l57UbN8RM3PjPG+1mRlafVxHjwsPXzJy+2XW9a+DlUa6sma19FzL8klWCBP57bffUryV69mzZ7z//vuGIQJGjhyZ5H4TJ040TCb066+/0rVrV8NEUMkJCgrKYNTGFxYWxqRJk/j444+BuB6Ga9euzbb6vvjiC8PQAGvXrmXo0KEpTsKk1WpZsWIF9erVSzRWaFaLjo7ms88+Myy/88472VZXamJiYujcuTOnTp0C4no/ZmSCp/HjxxsSpmPGjHltAqrIyEimTp0KxPW+fHWitvSIn7jpypUriYZlyIz43s4Ab7/9do4e+y4tBgwYQNGiRYG4IWeCg4OT3Tf+xw4rK6sUJzMTQgghhMiJHNq0oeCECViWL0+JH34wSnI13rCqw2jr0RaARkUb4WLrksoRIidp0msALQZ+iOW/dzrGREZy/PsNbJo0Gu9LF4wSQ3kXB7YMqoe9VVyfyVNegYzYepHoWBmf1ZSkB6sQJjJmzBi0Wi3vv/8+DRo0wMPDA2trawICAjh69CjffvutIVH61ltvMWrUqCTLMTc3Z9euXXTo0IFz586xe/duPD096dmzJ82bN6dYsWLY29sTHh7O/fv3OX78OD/88IPh+PSM05uckydPpmm/jh07JhqjEuISlNeuXUu0/PLlSx48eMDp06f58ccfefnyJQCOjo58//331KhRI9MxJ8fW1pbff/+ddu3acefOHdauXcuePXvo3bs3TZs2pUiRIiiKwpMnTzh+/Dg//fQTjx49ypK6vb29X7stOyQkhH/++YdVq1Zx/fp1AEqXLs0wI93GlJQPPviAP/74A4AWLVowePDgRK/hqywsLChbtuxr68uWLcvkyZOZN28eFy5coFGjRkydOpVSpUpx7949vvrqKy5dugTA5MmTKVOmTIZj7tSpE7NmzSImJoYTJ07QqlWrVI95/vz5a88rKiqKu3fvsnnzZg4cOADEJRlfneDqVV5eXmzcuDHVOuvWrZvk5GR37txJMdEfr0iRIokm9UoPCwsLJk2axIQJEwgODmbZsmWJkvoJxU+u9/bbbyca41YIIYQQIrdwGjaUAv37oTby8BoqlYrPG31O7cK16VauG2qV9HvLTdRmZtRo24FyDZtw6octXPnzICgKQU98+XneTErWrEOz/kPJ7+KaemGZUMXNkY0D69Jv3VnCY3Qcue3PmG2XWNm7JhozuaZMQhF5TnBwsAIowcHB6TouMjJSuXHjhhIZGZlNkWUNnU6nBAUFKTqdztShZErx4sUVINV/77//vhIUFJRqeZGRkcqECRMUKyurNJXr4OCgfPLJJ0pYWFiG4t+wYUOa6kn479KlS+l+/oBibm6udOnSRXnw4EGKMc2YMcNwzJEjRzL0vOIFBgYqffv2VdRqdarxaTQaZezYscrLly/TXU/CmNPyr1q1aoq3t3eSZSV8TTZs2JCp55+S9L7uxYsXT7YsnU6nDBo0KMXjBw0alCXtvVatWgqgDBw4MNl9jhw5kq7nVrBgQeXgwYNJluXt7Z3uc7V48WLD8f3798/U8fHitzVt2jTVcxQeHq44OzsrgOLk5KSEhoYm+bxUKpUCKLt27Uq1TCFSklf+pgthKrnl83tMTIzyyy+/KDExMaYORYgU6WNjFV1UlKnDyJXe1Hb+9L6Xsu2zycqC7u8Y/i3u1Ul5cOWSUer/616AUu7TfUrxqb8rxaf+roz6/qISq9Mbpe43QXrya5LWFsJENm3axKxZs2jbti1ly5alQIECmJubky9fPqpUqcKHH35o6MGZL1++VMuzsrJi0aJF3L9/n4ULF9K+fXs8PDyws7NDo9FQsGBBqlWrxqBBg9i6dStPnjxh7ty52T6JU3rZ2NhQpEgRqlSpQp8+fVi2bBkPHz7kp59+onjx4kaLo0CBAmzevJlr164xY8YM3nrrLYoWLYqlpSU2NjYUK1aMDh06sHjxYh4/fszSpUtxdHTM8jhsbGzw8PCgS5cubNu2jQsXLuDh4ZHl9ZiKWq1m3bp17N27l06dOuHq6oqFhQWurq507NiRnTt3smbNmiwZmzG+1+/PP/+c4WECLCwscHFxoWXLlixcuJDbt2/Tpk2bTMeWU9jY2DBhwgQAAgMDWb169Wv7bN++HUVRcHV1pVOnTsYOUQghhBAiW+jDw3k8Zix+k6eg/DtGuDH5hvly8MFBo9crMqdwiVL0nPUV7cdMwu7fya5s8xfAtVwFo9Rfr6QTa/vVwcI87vvS71eeMPnHy+j/HaNVGI9KURQ563lMSEgIjo6OBAcHp2tm56ioKLy9vSlRokSOnoVUr9cTEhKCg4ODTIghRB6W1W09LCyMkiVL4u/vz9atW+ndu3cWRPlm0ev1VKhQgTt37vDll1+me2IzIV4lf9OFyJzc8vldq9Wyb98+2rdvn6cnvxG5l6IoPOrbj4gLcWNoOg3/kEKZGP8/vS4+u8iEIxMIjQllTZs11HapbbS6s4q0c4iJiuTs7p24lC5LmToNEm2LCH6JjWO+bKv78K1nfLjlIlpdXIrvu761aFNJxvfNrPTk1+STrBBCiDeCnZ2dYUzRL774wjB7uUi7H374gTt37uDs7Mzo0aNNHY4QQgghRJZQqVQ4DRsK//7YF/jNtwTv2WO0+o/6HCUoOohYJZaJRyfiG+ZrtLpF1rGwsqbxB/1fS64GPfFlzahBeK5ZQURI8hPJZkaL8oVZ/kENzNQqJrYuK8lVE5AEqxBCiDfG8OHDKVmyJDdu3ODHH380dTi5iqIozJ07F4BZs2ZhZ2dn4oiEEEIIIbKOXZMmFJ421bD8ZPqnRPx9ySh1j6s5jkaujQAIig5izOExRGgjjFK3yH5HN68lVhvDlUMHWD9+GJcO/IZep8vyetpWLsLB8U0Y2zLjkwOLjJMEqxBCiDeGRqNh48aNzJgxg9jYWFOHk6s8efKErl27MnfuXD788ENThyOEEEIIkeXy9+1Lvu7dAVC0Wh6PHk3M4+zvTWquNufrpl/j4eABwN2gu3xy8hP0itxxldspej1uFaugsbIGIDo8nMMbvmXL1LE8unYly+srXej1ThCPgyRZbwySYBVCCPFGady4MTNnzqRXr16mDiVXcXV1ZebMmXzyySeYmZmZOhwhhBBCiCynUqlw+exTbOrXB0D34gWPR4xAFxaW7XU7WDiwrMUy7DX2APz56E9W/bMq2+sV2UulVlOnQxcGLfmWSk1bGtYH+Dxk1+xP+G3xPEICnmdb/SfvBtBq0TFWHfXKtjpEHEmwCiGEEEIIIYQQQgAqjQa3JYuxKF4cgOi7d/H7aBJKNtzS/aoSjiX4qslXqFVxqZpvr3zLgQcHsr1ekf3s8heg7cgJfDB7AYVL/ncL/52/TrJhwgjO/LidWK02S+t8HBTBoE3nidLq+frAbdad9M7S8kVikmAVQgghhBBCCCGE+JdZvny4fbMataMjAGHHjvFi02aj1N3YrTETa000LH928jNuBt40St0i+7mWLU/vuQtpM3ws1g5x11dsTDQ3Tx5Fpcrautzy2zChVVnD8uzfb7D1r4dZW4kwkASrEEIIIYQQQgghRAKWJUrgtnQJmJtj37oV+Xv2MFrd/Sr2o2OpjgBE6aI4//S80eoW2U+lVlOleRsGLfmWWu90Qm1mRvP+QzEz12R5XSOalWJcgkmvPv3lGrsu+GR5PQLMTR2AEEIIIYQQQgghRE5jW78+Ht9vxapKFVRq4/VPU6lU/K/B/3ga/pRuZbvRtkRbo9UtjMfK1o5m/YZSs10nHAoWSrQt4NEDrh7+gwbdemFl+/rEVekxvlUZomP1fHPsHgBTf7qChbmaTtWLZqpckZgkWIUQQgghhBBCCCGSYF2tmknqtTSzZG2btaiy+r5xkeO8mlxVFIUjm77j0bUr3Dx1jLd69qNy81ao1RmbaFalUjG1bTmitDo2nn6AXoGJOy9jaa6mbeUiWfEUBDJEgBBCCCGEEEIIIUSaaH19eTxmLLrg4GyvK6nkamBkYLbXK0wr6IkvfnduAxAZEoznd8vZNv0j/O5kfCxelUrFjA4V6VWvGAA6vcKY7Zc4fOtZlsQspAdrnqbVatGmYxY6rVaLoijo9Xr0en02RpY5iqIY/s/JcQohMkfauhB5n7RzITJHr9ejKAparRYzs4z1bDKG+O8k6fluIkROFHX9Ok9GjkL34gWxIcG4rl6NSpP142Ym59d7v/LVha9Y2GQhDYo0MFq9aSHtPOvYFyxM36+Xc3L7Ju6ePQXAs/tebP9sMuXfakajHn2xzV8gQ2XPaF+OqJhYfr7kh16B4PBoec1SkJ5zo1LiP9mKXG/lypWsXLkSnU7HnTt32LZtGzY2Nmk+3tzcHBcXF9zd3bGwsMjGSIUQQgghhBCZFRMTg4+PD0+fPiU2NtbU4QiR55m/eEGxFSsxDw8H4GW9ejzv/B5ZPv17Em5rb7MlfAsAViorhtsNx9nMOdvrFaYV8cyPgItniHn5wrBOZa6hQOUa5CtXGVUGflzTK7Dtnpoq+RWqOUlKMCURERH06tWL4OBgHBwcUtxXEqx5UEhICI6OjgQEBKR6ASQUFRWFj48PHh4eWFlZZWOEmaMoCqGhodjb28t4NELkYdLWhcj7pJ0LkTlRUVE8ePAAd3f3HP35XavV4unpSevWrdEYsbefENkh8tIlfAcPgX97tjlPm0q+3r2zvV69omfSiUkcfXwUAA8HDza12YS9hX22150W0s6zj16n4+rhg/z143aiw8MM60vVqc8746aaMLK8LyQkBGdn5zQlWGWIgDxMo9Gk641Np9OhUqlQq9WojThDYnrF30IYH6sQIm+Sti5E3iftXIjMUavVqFSqdH/uN5XcEqcQKdHUrYsydw5+U+ISWwFfz8e6ZEnsmjTJ9rrnNZlHn3198HrpxYOQB0w/M50VLVZglsHJj7KDtPNsoNFQu30nKr7VjNM7t3L50AFQFGq165il53rnBR8quDhQxc0xy8rM7dJzfuWTrBBCCCGEEEIIIUQaOXbsiNOHH8Yt6PX4TphI9N272V6vrcaWZS2W4WgZlwA76XuSpX8vzfZ6Rc5g4+BIqyGj6PPlEhp174N7paqJtgc/f0ZMVGSGyt50+gFTfrxC3/VnufkkJCvCfeNIglUIIYQQQgghhBAiHQqOG4t9mzYA6MPD8RkxktgXL1I5KvPc7d1Z1HQRZqq4Xqsbrm/gt3u/ZXu9IucoXKIU9d/vmWidoijsXT6fDeM/5ObJo6RnNNBYnZ69V58A8DJCS5+1Z/F6HpqlMb8JJMEqhBBCCCGEEEIIkQ4qtRrXeV9iVbEiANrHj3k8egz6mJhsr7tukbpMqzvNsDzz9Eyu+F/J9npFznXz5FGe3LlFWNAL9i1fwI4ZU3nmfS9Nx5qbqVk/oA41iuUDIDA8hl5rzvIgIDwbI857JMEqhBBCCCGEEEIIkU5qGxvcVq/CvFAhAGzq1UVlbpypbnqU60G3st0AiNHHMOX4FLR6rVHqFjlPkTLlKFmrrmHZ7/YNtn48Hs81K4gICU71eDtLczYOrEvlonETOT0Pjab32rM8DorItpjzGkmwCiGEEEIIIYQQQmSApnBh3FatwnX+1xQaNw6VkSZtVKlUfFz3Y2oVrkUh60IsaLoAjVoml3pT5XdxpfOU/9Fl2kzyFykat1JRuHLoAOvHD+PSgd/Q63QpluForWHLoHqUK2wPgO/LSHqtOcvT4KjsDj9PkASrECLX8vDwQKVSMWDAAFOH8kZZv349KpWKKlWqpGtsnzedoihUqVIFlUrFhg0bTB2OEEIIIYTIItaVK+HYoYPR69WYaVjUbBHb391OZefKRq9f5DwlatSm/4IVNOkzCI2VNQDR4eEc3vAtW6aO5cnd2yken9/Wgq1D6lGyoC0Aj15E0GvNXzwPlSRraiTBKoQJqVSqNP1r1qxZmst88uQJS5Ys4d1336VUqVI4ODhgYWFBwYIFqVmzJoMHD2bnzp1ERGSuq//GjRuTjdfOzo7SpUvTs2dPfv/990zVk1fduXOHyZMnU7duXQoUKIBGo8HW1hYPDw9atmzJlClT2Lt3b5pfpyNHjjB+/Hhq1qyJi4sLFhYW5MuXj/Lly9O7d2+2bt2a6dccICwsjE8++QSA//3vf6hUqtf2Selatra2xt3dnQ4dOrB582ZiY2NTrK9q1aqYmZnh4eGR7lhTukaT+7dkyZLXymnWrFmS+5qZmVGgQAFq1arFuHHjuH79eorxqFQqpk+fDsD06dMJD5cxjYQQQggh8qqIixeJunEj2+spYFWAQjaFsr0ekXuYmWuo06ELg5Z8S6WmLQ3rA3wepun4gvaWbBtSn2IFbAC4HxDOiK1/S+eaVEiCVYg8Ijo6msmTJ1OqVCkmTJjA3r17uX//PqGhoWi1WgICArh06RLr16+nR48euLi48L///S9Lkm6vCg8P5969e/zwww906NCBdu3aERYWluX15FazZs2iUqVKLFiwgPPnzxMUFERsbCwRERE8fPiQw4cPM3/+fN59910+//zzFMu6cuUKjRs3pkWLFixdupRLly7x7NkztFotwcHB3L59m23bttG3b1/c3NxYuHAher0+w7EvW7aMZ8+eUbFiRbp27Zru46Oionj8+DG///47/fv3p169ejx79izD8ZiSXq8nKCiIv//+m2XLllGtWjXmzZuX4jHdu3enXLlyPHnyhJUrVxopUiGEEEIIYUwvf/mFRwMG4jNiJNpnz41at06vY9GFRRz1OWrUekXOY5e/AG1HTuCD2QsoXLIMlZq2okiZcmk61sXRim1D61E0nzWO1hpmdKiYZOca8R/jjL4shEjRiBEjGDlyZLLbbW1tUzw+ICCAjh07cubMGcP+3bt3p0WLFhQrVgx7e3sCAwPx8vLC09OT/fv3ExoayuzZs2nfvj3169fPVPxz5syhU6dOhuWXL19y6tQpFi5ciL+/PwcOHGDQoEHs3LkzU/XkBfPmzWPmzJkAODo68uGHH9K0aVOKFClCTEwMjx8/5uzZs/z222/cunUrxbL2799P9+7dDcnrSpUq0aNHD+rUqUPBggUJDw/n4cOHHDhwgD179hAUFMSkSZMYPHgw+fLlS3fskZGRLFq0CIAJEyak+ge2du3ar90KHxYWxrVr11ixYgWXL1/m77//pmvXrpw4cSLd8aTHq9docooUKZLi9qtXrxoex8TEcP/+fX755Re+//57dDodH3/8MaVKlaJbt25JHq9Wq5kwYQLDhw9nwYIFjB07Fisrq/Q9GSGEEEIIkWMpOh0vd/yAotUS++wZj0eNoviWzaitrbO97qjYKCYcncBJ35P8cPsHvm//PaXzl872ekXO5lq2PL3nLkQbE51ovV6v47dF86jYuDml6zZ47fudW34btg2tR3i0joquDsYMOVeSBKsQOUChQoWoXDljY+bodDq6detmSK527NiRNWvWUKjQ67eJtGrViuHDh/Ps2TPmz5+f5O3QGVG0aNHX4n/rrbfo0aMHtWrV4sWLF+zatYsrV65QtWrVLKkzNwoICGDWrFkAuLm5cfr0adzd3RPtU69ePd5//32+/vprzp07R2BgYJJlXb9+na5duxIREYG5uTlLlixhxIgRqJMYVL9v3774+/sza9asTPWa3Lp1K4GBgVhaWqap96qtrW2S13X9+vXp3bs3NWvW5NatW5w8eZIzZ87QoEGDDMeWmqSu0Yx4tYyaNWvStWtX6tWrx9ixY4G4HsrJJVgBunXrxpgxY/D392fHjh0yhrAQQgghRB6iMjPDbcVyvLt3J9bvCVHXruH38ScUXbQw2yfAsjSzxE5jB0BEbARjDo9h+zvbyWeVL1vrFTmfSq3Gwipxkv/aYU+8zp/B6/wZilWuRvMBw3B2L55on+JOr3f2UhSF6Fg9VhqzbI05t5EhAoTI5RYvXszRo0cBePfdd9m9e3eSydWEChcuzIIFCzhx4gQFCxbMttg8PDwYNWqUYfngwYPZVldu8McffxAVFTc4+LRp015Lrr6qbt26tGvX7rX1iqLQu3dvw/AOGzZsYNSoUUkmV+MVLFiQFStW8OOPP6LRZGx20XXr1gHwzjvvZKgHbELW1taJro3z589nqjxTGzVqFMWKFQPikt9Pnz5Ndt8CBQrQtm1b4L9zKoQQQggh8g5zZ2fcV69GbRM3hmXogQMErMj+4aFUKhWfN/qcCgUqAPA47DGTjk1Cq9dme90i93l0/cp/j69dZvOUMRzZtIao8OSH99PrFWbuuc6gjeeJ0uqMEWauIQlWIXKx6OhoFi5cCICNjQ3r1q1LMcn2qgYNGlCqVKnsCg+ISxLGe/jw9UG1Hzx4wIQJE6hUqRL29vbY2NhQpkwZPvzww0S3Y2fEkydPWLVqFV27dqVMmTLY2tpiaWlJ0aJF6dSpEz/88EOK45EePXrUMKHR0aNH0ev1rF+/nubNm1O4cGHUanW6eh8+evTI8Lh06YzfqrN3714uX74MQIcOHejTp0+aj33//fdTHXIiKQ8fPuTs2bOGMrJCiRIlDI+jo6NT2DPnU6vVVKpUybDs4+OT4v7x5/DUqVOp7iuEEEIIIXIfq3LlcF2wAP697Tpg1SqCf9+b7fVam1uzrMUyClgVAODs07PMPz8/2+sVuc87YyfT8aNPcChYGABFr+fvfb+yfvyHXPnzIHr96wnUuftusunMQ07fC2T41otEx0qSNZ4kWIXIxQ4ePGjoKdezZ89Ue66aQsLekjpd4jffzZs3U758eZYsWcKNGzcICwsjMjISLy8vvvvuO2rUqMGXX36ZoXp1Oh1ubm6MGjWKn376CS8vLyIiIoiJicHPz489e/bQs2dP2rZtm6YJuKKionj77bcZPHgwR48e5fnz5+meRdHCwsLw+ObNm+l+TvESjms6fvz4DJeTHvG9pIFMj9kbL2HCPb73Z26W8PVNrZdw/DlUFIUDBw5ka1xCCCGEEMI07Fs0p9DkyYblJ598QuQ//2R7vS62LixtvhRzddyokNtvbWfXnV3ZXq/IXVQqFWXqNmTAolU07N4bcwtLACJDgvH8bjnbpn+E353E31vfqVoEW4u4oQGO3vZn9LZLaHUZn0Q5L5EEqxA5wK5du6hYsSI2NjbY29tTpkwZ+vfvz5EjR1I87tixY4bH8bcc5zQJe6G6uroaHu/du5cBAwYQHR2NnZ0dM2bM4MSJE5w5c4aFCxfi7OyMTqfjk08+YfXq1emuNz752aJFC+bPn8+BAwe4ePEiR48eZf369YbxPj09PRPdqp6cqVOncujQITp27MjPP//MxYsX2bdvX5K38CenZs2ahsdffPGFoRdqesVPCGVra0vTpk0zVEZG63RycqJkyZKZLi8yMtIwHqytrS2tWrXKdJmmljBpXrx48RT2hLJlyxqGWUjYjoUQQgghRN5SYOAAHLvG3b2kxMTgM3oMWj+/bK+3eqHq/K/+/wzLX/z1BReeXsj2ekXuo7GwpMH7HzBw8WrKNmhsWP/svhc7ZkwlJMDfsK5msfxsGFgX63/HX/W88YzVR+8ZPeacSCa5EiIHuHHjRqJlLy8vvLy82Lx5M++99x4bN27E0dHxteOuXPlvzJSEybucIjQ0lFWrVhmWmzVrBoBWq2XYsGEoioKdnR0nTpygevXqhv3q16/P+++/T4MGDXjy5AmTJk2iW7duODs7p7luMzMzbt++neSt+E2bNmXgwIHMmDGDzz//nC1btvDpp59SpkyZZMu7cuUKn376KbNnz05zDK9q0qQJVatW5cqVK/j7+1OjRg2aNm1K69atqV+/PrVq1UrydU7I19cXf/+4P3DVq1fHzMw4A4ufPn3aUGdahYeHc+3atUTrIiIiuHr1KitXruTGjRuoVCq+/vprnJycsjLc1/j6+r4WS1IyOhHWzz//zJ07dwBo2bIl+fPnT3F/lUpFjRo1OHLkCBcvXsxQnUIIIYQQIudTqVQU+d//0D58RMT58+hDQoi6fRtNgs4n2aVzmc7cCbrD1ptbiVVimXh0Itvf3U5Ru6LZXrfIfRycC9Fh/FR8Wrfj8MbvCHj0gKot2+LgnHjelrolCrC2f20GbjxPrWL5GfxWiWRKfLNIglVk3OkVcCYNA3UXqQa9diRet60nPElD770Go6Dh6P+Wo0NRraiLg6JHpUqlA/YH28C1xn/Ltw/A7xNSr9PCFsYY55c9GxsbOnbsSMuWLSlfvjx2dnb4+/tz7NgxvvnmGwIDA/nll1/o1KkTnp6er912HBAQYHic0mRVgYGBPHnyJMlttra2icbCzAohISGcPHmSTz75BG9vbyAuadq4cdyvYbt378bv319tP/300ySTdsWLF2f+/Pn06dOHiIgINmzYwOQEt9ekRqVSpTrO6f/+9z9WrVpFQEAAe/bs4aOPPkp237JlyzJz5sw0158UtVrNTz/9RLt27fDy8kJRFI4ePWq4/V6lUlG5cmXatWvH4MGDKVu27GtlBAYGGh4bc0iIx48fp7vOCxcuUKVKlWS3t2nThmnTptG8efNMx5eaTz/9lE8//TTV/dIz7ENMTAz3799n9+7dzJkzB4hr03Pnzk3T8fHn0tvbG0VRUP07PpcQQgghhMhbVBYWFF22lMejx1B4ymSsq1UzWt0f1f6Iey/vcebJGYrYFcFMJTO/i5S5V6pK33lLufLnQco1eCvRNl1sLI9vXqNRlepsH1qfSq4OWGnkmgJJsIrMiA6F0DTc2uCYxK9jEQFpOzY6NPGyoqAK9SNNaQjdKzMlxkamrU4L+7SUnkklbJsAAPnxSURBVCV8fX2TnI29devWjBkzhnbt2nHp0iWOHTvG6tWrGTt2bKL9QkP/Oz8pTVy0ZcsWJkxIOrnctGnTRONrZsTAgQMZOHBgsttLlSrFzp07DcuHDh0C4hKKgwYNSva4bt26MWrUKIKDgzl06FC6Eqyv0uv1PH36lNDQULTa/64NNzc3AgICUr1dv0ePHlnSW7R06dJcvnyZlStXsnbtWkOvR4hL7l29epWrV6+ycOFCxo0bx1dffYW5+X9v1Wl9zbNSdHS0od7Uemamx5EjR7C1taV06dK4u7tnWbnZKaUkaM2aNVm2bBn16tVLU1kFCsRNPBAdHc3Lly+z9NwKIYQQQoicxTx/fopv3WL0H9XN1ebMbzqfNVfWMKrGKKzNrY1av8id1GZmVG/T/rX1/xzcy9HNayhZsw7N+g/FSiPfYeLJGKwi4yztwd419X82SdzWbeOctmMtX0l2qlQo9q7o7VxQUjvW7JVJZsyt01anvUv2nbNXJJVcjVe4cGF+/PFHQ6/V5cuXv7aPvf1/5yc8PDzL48sMlUpFhQoVmD17Nv/880+iBFr8rdolSpRIseethYUFNWrUSHRMeiiKwtatW2nevDl2dnYULVqU8uXLU6VKFcO/f/4dZD5hb+CkVK1aNd31J8fGxobJkydz+/Ztw1AQ48aNo27duqjVcW/LOp2ORYsWMXjw4ETHmuI1f/HiheFxepKATZs2RVGURP9iYmLw9vZm5cqVODo6snv3burXr8+tW7eyI3SDDRs2vBZLUv8yysLCgsGDB9OoUaM0H5PwXOa09iuEEEIIIbJeUsnVmEePsr1eR0tHJtWZJMlVkSkRIcGc3vU9APf/Ps+mj0ZyYttGYqIiTRxZziA9WEXGNRyd+Pb99Hh1yIC0srRHmXCdkJAQHBwcUKnT8RtBubZx/3KRkiVL0rp1a/bt24eXlxd+fn6JJopKOG6lv78/Dg4OSZYzfvz412abz8pfTufMmUOnTp0M5drY2FCwYEHs7OyS3D8+YZeW281dXFwSHZNWUVFRdOnShf3796dp/8jIlP8oZFfvwlKlSlGqVCn69u0LxPVqnjlzJmvXrgVg8+bNDB06lLfeirs1I+Fr/uzZs2yJ6VVWVlaGx6mdp9RoNBo8PDwYOXIkTZs2pUaNGvj5+TFkyBBOnjyZ2VCzXcJJ24KCgrhy5QqLFy/m3r17jBo1ivDw8DT3tE54Ll8d/kMIIYQQQuRtik7H80WLeLF5C8XWrcW2bl2j1h8WE8btoNvUKlzLqPWK3Mva3oFWQ0ZyfOt6woJeoIuN5fyenynXsAmFPDI/EXJuJz1YhcjhKlasaHjs6+ubaFu1BGP3XLp0yWgxvapo0aJUrlyZypUrU6lSJUqUKJFscjWh7Lw9Zu7cuYbkatOmTdm5cydeXl6EhYWh0+kMPRbjx4VNrfeisSaTKlq0KGvWrKFnz56Gdbt27Uq0PX6yr8uXL6PT6bI9pnz58hmGKUhvojsllSpVon37uNtOTp06lWi4hJwq/jqvXLkyjRs3ZtSoUVy6dMnQw/mTTz7h/PnzaSor4blMbXIzIYQQQgiRt7z8+WderFsPWi2+Y8YapSdrPJ8QH/ru78uIQyO49SJ77yQTeYdKpaLCW80YuORb6r7XDTNzc6q2bifJ1X9JglWIHC6lJGTTpk0Njw8cOGCMcLJE/NiTaemB+fTp00THpIWiKIYeoI0bN+bw4cN069aNUqVKYWtra7gNH7I2YZiVhg4danjs5eWVaFuTJk2AuNvKjx07lu2xqFQqQ1I3KCgoS8suX7684XHC3qG5ib29PZs3b0atVhMbG5viZGkJxZ/LQoUKJeolLIQQQggh8r58nTtj+29nD11wMD7DR6ALCTFK3VtubsHrpReRsZGMOTyGgMiUh0sTIiELK2saf9Cf/gtX0ahHH1OHk2NIglWIHO7GjRuGxwmHBwB4++23KVy4MAA7duzA39/fqLFlVOXKlYG42dNTilmr1Rp65sYfkxYvXrwwJGa7deuWKKGaUFhYGLdv305zucaU8LV+NcmecEKxJUuWGCWeKlWqAGR5L9PY2NgkH+c21apVo1evXgCcOHEiTT94xJ/LSpUqZWtsQgghhBAi51GZm1N00UIsSpcCIOb+fXwnTEQxwmfij2p/RFXnuDuwnoY/ZeLRiWhfnSRaiFTkd3HF2s54k4TndJJgFSIH8/b2xtPTE4gbq7No0aKJtltaWjJx4kQgrjfj0KFD0ev1Ro8zvVq1agXE9TTdsGFDsvv9+OOPBAcHJzomLRIm6lKaPGjt2rVGTeqlZxKlCxcuGB6XLJn4lot33nnHMDzEb7/9xtatW9Nc7s8//5yhCZXih1K4ffs2oaGh6T4+OQmfZ8KJ0HKj6dOnG5L5c+bMSXHfkJAQQ3K/Xr162R6bEEIIIYTIeczs7XFfvRqzf+d7CD91imdffJnt9VqaWbKk+RIK2cTNiXHp+SXmnJ2TqUlfhXjTySRXeZhWq0WrTfuvUFqtFkVR0Ov1OTpJF/+mHx9rbvXbb7/Rrl07w9iWr3r27Bnvv/8+MTExAIwYMSLJ5zt+/Hj27t3L8ePH+fXXX3n//ff59ttvDbd0J+XV27wzch4THpPea6Zjx464urri5+fH3Llzefvttw09JOP5+PgwadIkAGxsbOjfv3+ydbx6LTg5OZEvXz5evnzJ9u3bGTduHJaWlomOOX/+PJ999lmyzymzzzEp3333HRcvXmTy5MmULl062f0ePnzI9OnTDcsdOnR4re4tW7ZQv359IiIiGDhwIMHBwXz44YfJ9tYNCAhg9uzZrFy5koCAAKyt0zeDaKNGjYC483Du3DmaN2+epuNSOmd79+41DHHg7OxM7dq1E+3/6ge89J7/rH79UouhbNmydO7cmZ9++olTp07x559/Jnuezp07Z3h+rVq1ytXvZUJkRl75my6Eqej1ehRFQavVGm28+IyI/06Snu8mQrwpVC4uuCxehO+QoRAbS9C2bZh5eJDvg56pH5wJ+TT5WNR4EYMPDSZaF83Pd3+mlEMpPij3QYbKk3Yu8qL0XM+SYM1DVq5cycqVKw2T3vzxxx/Y2Nik+Xhzc3NcXFwICwszJPVysqzsRWcKY8aMITY2lg4dOlCnTh2KFSuGtbU1gYGBnDx5ko0bNxIYGAhA/fr16dOnDyHJjMmzdu1aPvjgAy5evMgvv/zCoUOH6NKlC2+99RZubm7Y2dkRERHBgwcPOHPmDD///LPhWI1Gk2y5KYmKikr0OL1lLFq0iA8++ICQkBAaN27MmDFjaNKkCWZmZpw7d44lS5YYhg/4/PPPsbCweK2O+C/jWq32tW1du3Zl7dq1XLlyhUaNGjFy5EhKlSpFSEgInp6erFu3DltbW1xcXPDy8iI2Nva1MiIiIhI9zsh5SigkJIS1a9eydu1a6tWrR4sWLahWrRqFChVCrVbj5+fHyZMn2bJli+H6bt++PXXq1Hmtbnd3dzZu3MigQYMICwtj9OjRrFy5ki5dulCjRg2cnZ0JDw/n8ePHHDlyhL179xrKDA0NTTYRm5zKlSvj7OxMQEAA+/fvp1at1GcbDQkJ4a+//kq0LjY2Fj8/P/744w+2bNliWP/ZZ58lOt+vCg0N5Ztvvkm1Tjc3N8MYtQmv0fv3778WS1Ls7e1f60mbsJdzatfA2LFj+emnn4C46za58xQ/AZuTkxNVq1bN9LUlRG6X2/+mC2EqMTExREZGcvz48Vwx1E78nVlCiNc5dOmMy864yW39583jn2dPiShbNtvr7WjZkV0RcfUuuLgA/1v+lNYk3xkkNdLORV6S0nfUV6kU6QOe54SEhODo6EhAQAAODg5pPi4qKgofHx88PDxy9IQriqIQGhqKvb19ts5Cn91KlizJw4cPU92vS5curFmzhnz58qW4X1RUFNOnT+ebb75JlFhKjoODA6NGjeLjjz/G1tY2rWEbbNy4kcGDBwOwbt06BgwYkO4yNm3axIgRI4iOjk5yu5mZGZ9//jnTpk1Lcnv8OezXr99rQw0EBwfTokUL/vnnnySPLVCgAD/99BMzZ87k2LFjNG3alMOHDyfa5+jRo7Rs2RKAP//8k2bNmqXvCb5i586d9O/fP80/YHzwwQesWbMmxd6mV65cYfTo0Zw6dSrV8pycnPjf//7HyJEj051gBZg8eTKLFi2iZMmS3L17N9n90tODRqPRMHv2bCZPnvzaNkVRKFGiBD4+Pmkur2PHjuzevRtIfI1m5Ph4LVq0MPS0jf8BKyXvvvuuIYF66tQp6tev/9o+pUuXxtvbm5EjR7J8+fJ0xShEXpJX/qYLYSpRUVE8ePAAd3f3HP35XavV4unpSevWrdFoNKYOR4gcK2DxEl6uXw+ApkQJiu3+GZUReqcv/2c5G27EfZ9ysHBgc5vNFHMolq4ypJ2LvCgkJARnZ2eCg4NTza9JD9Y8TKPRpOuNTafToVKpUKvVGUq+GEt8r8X4WHOrTZs2cezYMc6cOcP9+/cJCAggJCQEOzs73N3dadiwIf3796dBgwZpKs/GxobFixczZcoUtm/fzp9//smNGzcICAggOjqafPny4erqSq1atWjRogWdO3dOVw/nVyU89xm9ZgYOHEjz5s1ZsmQJf/zxB48ePUKv1+Pq6kqLFi0YM2bMa0MHJCWpayF//vycOnWKRYsWsXPnTu7evYu5uTnu7u688847jBs3Djc3t2SfU1Y9x4R69uxJu3bt+OOPPzh27Bh///039+/fNwzZ4ODgQOnSpWnQoAG9e/dOUy/R6tWrc/LkSQ4fPsyvv/7K8ePH8fPz48WLF9jY2FCkSBFq167NO++8Q+fOnTP15WvYsGEsWrSI+/fvc+7cuSQTh6kxMzPD0dGR0qVL07x5c4YMGZLscAkZuV044bWQkdcrtfeVtJT56aefGhKsc+fOZe/evYm2nzlzBm9vb4AMJ7uFyCvyyt90IUxFrVajUqnS/bnfVHJLnEKYisukj4h99BCtnx/uq1ahMdIPJ+Nrj8c7xJujj48SEhPCr96/MrH2xAyVJe1c5CXpuZalB2seFN+DNS0Z9oSioqLw/j979x3X1PX+AfwT9gYRUBEU3AqKolg3Ttx7VlScrUq1dddRd9WqdVW0deHWuoW6oBYHTsSF4qwgiKKAyIaE5Pz+4Jv7SyCbQIA+79crLxLuuec+dyZ5cu45MTFwdXUt07+Ai0QipKenw8rKir6MEVLKevXqhQsXLmDixInYuXNniS6rop7rEydOxO7du9G9e3dcvHhR1+EQolMV9TwnpLSUl8/vAoEA58+fR69evSjxQogSoqwsgMeDXjEaw2gik5+J0RdGo7tLd3zb5Fu17yyh85xUROrk1+iTLCGEEJWtXr0aenp62L9/v1q37pMCcXFx2L9/PwBg2bJlOo6GEEIIIYSUNXrm5kWSq0woLDIArLZZGFngaJ+jmOwxmbrtIUQDlGAlhBCiMg8PD4wcORJ8Ph+rV6/WdTjlzurVqyEQCDB06FB89dVXug6HEEIIIYSUccLMLLzz/w6f/9c3a0ky1jcu8j++sOwPgE1IWUB9sBJCCFHLqlWrULt2bZiYmIAxRr9wq4gxhpo1a2LJkiUYP368rsMhhBBCCCFlnCgvD299fZH34gUyr16FkasrLDt3LrXlRyRGYP71+djQcQOa2DcpteUSUh5RgpUQQohanJ2dsXTpUl2HUe7weDz8+OOPug6DEEIIIYSUE3rGxrDs1g15L14AjCFh9hy4HDoIk4YNS3zZ9xLv4ZuQb5DP8vFD2A840vsIqphXKfHlElJeURcBhBBCCCGEEEIIIWWQnf9UWPXqBQBg2dmIn+qP/KSkEl+uh70HPBw8AABJOUn4IewH5ObnlvhyCSmvKMFKCCGEEEIIIYQQUgbxeDxUW/UzTDwKbtHP//AB8d99B1FuySY7DfUNsaHjBjiaOwIAnqQ8wZKbS0p8sC1CyitKsBJCCCGEEEIIIYSUUXomJnDeuhUG1aoBAHIfPcaHhYtKPNlpa2KLLZ23wNTAFABwPuY89jwp+cG2CCmPKMFKCCGEEEIIIYQQUoYZ2NvDefs28MzMAADp584hedu2El9ufdv6WN1uNfd68/3NuBp/tcSXS0h5QwlWQgghhBBCCCGEkDLOpEEDVF+/DuDxAADJv21F5tWST3Z2qdkF/k39AQAMDPOuz8O/X/4t8eUSUp5QgpUQQgghhBBCCCGkHLDs3BkOs2cVPPfxgVnLlqWy3G+bfAufmj4AgCxBFqb/Mx18Ib9Ulk1IeWCg6wAIIYQQQgghhBBCiGpsx4+HobMzLLt2BU+vdNrN8Xg8rGi7AnEZcYhNi8U0z2kw0jcqlWUTUh5QgpUQQgghhBBCCCGknODxeLDy8Sn15ZoZmmFLpy1IzUtFo8qNSn35hJRl1EUAIYQQQgghhBBCSDnGf/cOH39ZCyYSlehyqllUo+QqITJQC1ZCCCGEEEIIIYSQcir7/n288/8OwtRU8Az04TBrVqku//Sr03A0cyzVZRJS1lCClRBCCCGEEEIIIaScEmVlQ5iWBgBI2bkLRq61YDNoYIkvN1+Uj1/v/YqDzw6iknEljDceX+LLJKSsoi4CCCGEEEIIIYQQQsopi/btUGXBAu71hyVLkH3vXqks+98v/wIAUvNScSjzEHLyc0pluYSUNZRgJYQQQgghhBBCCCnHbEf5otLIrwteCAR499008OPjS3SZBnoGWOe9DjUsawAAEkWJ+PnuzyW6TELKKkqwEkIIIYQQQgghhJRzVRYsgHmbNgAA4ZcviJ88BcKMjBJdprWxNX7r/BssDC0AAOdjz+Php4clukxCyiJKsBJCCCGEEEIIIYSUczwDA1TftBFGtWoBAPj//ouEGTPB8vNLdLm1bGphetPp3Ov199aDMVaiyySkrKEEKyGk3HJxcQGPx8PYsWN1HQohFZK3tzd4PB7Wr1+v61DKlYiICPB4PNja2uLz58+6DocQQggh/yH6VlZw3r4N+tbWAICs8HB8XPNLiS93QO0BsNezBwA8SnqEkLchJb5MQsoSSrASokM8Hk+lR8eOHVWu88OHD9i0aRP69OmD2rVrw8rKCkZGRrC3t4enpycmTJiAY8eOITs7u1ix7927V268FhYWqFOnDkaMGIG//vqrWMupqF6+fIk5c+agZcuWsLW1haGhIczNzeHi4oIuXbpg7ty5OHfunMr7KSwsDD/88AM8PT1RtWpVGBkZwcbGBg0aNICvry8OHjwot66xY8dy+y42NlaLa1lUbGws5s2bh+bNm8PGxgaGhoawtbVFmzZtsHz5cnz69KlEl6+OuLg4rF27Ft26dYOLiwvMzc1hamqK6tWro3v37li5ciViYmJkznvlyhW554eZmRmcnZ3Rp08f7NmzB3l5eSrHlJWVhd27d2PYsGGoW7cubGxsYGRkBAcHB7Ru3RozZ87E7du3tbL+x48fx7Vr12Bvb48pU6YUma7oGqCnpwcrKyu4u7vj22+/RWRkpMJlSW6vpUuXqh1rx44dVb6eih9fvnyRqiM2NlZuWRMTEzg6OsLHxwebN29Genq6wni8vLzQvXt3pKamarQ+hBBCCCHFYVSzJqr/tgUwNAQA6Ftbl3iLUgM9A/Qw7cG93hS5CXwhv0SXSUiZwkiFk5aWxgCwtLQ0tebLyclh0dHRLCcnp4Qi0w6hUMhSU1OZUCjUdSjFBkClh7e3t9K6cnNz2ezZs5mpqalKdVpaWrKffvqJZWVlaRR7YGCgyvH36NGDZWRkaLQcRWrWrMkAMD8/P63XXZKWLl3KDAwMVNp28+bNU1jXo0ePWLt27VSqq1KlSmz9+vVFzh0/Pz+uTExMTImt9/79+5Uen7a2tiwkJIQxprtzPScnh/3www/M2NhY6Tbl8Xhs2LBhLC4uTqqOsLAwlc8PNzc3lbb777//zhwcHFSq08vLi924cUPjbSAUClmDBg0YALZmzRqZZdS5BvB4PDZ//ny5y5PcXkuWLFE7Xm9vb5VjET9SU1Ol6oiJiVF5XmdnZxYZGakwphs3bjAAzMjIqMjxQf5fRXpPJ0QXysvndz6fz86cOcP4fL6uQyHkPyX15Cn25a+/SmVZfD6fnT59mo27MI6573Vn7nvd2d4ne0tl2YSUFHXyawYghOjclClTMHXqVLnTzc3NFc6fnJyMfv364datW1z5YcOGoXPnzqhRowYsLS2RkpKC169fIzQ0FBcuXEBGRgZWrFiBXr16oVWrVsWKf+XKlejfvz/3+suXL7hx4wZ+/fVXJCUl4eLFixg/fjyOHTtWrOVUBGvWrOFatFlbW+Pbb7+Ft7c3qlWrBj6fj3fv3uHOnTsIDg7G8+fPFdZ14cIFDBs2DJmZmQAANzc3DB8+HF5eXrC3t0dWVhbevn2LixcvIigoCKmpqZg9ezYmTJgAGxubEl5TaTdu3MDYsWMhEomgp6cHPz8/9O/fH46OjoiLi8O+ffsQHByMz58/o3///njy5AlcXFxKNUag4Fzq27cv1wrU0tISI0eOROfOneHk5ARDQ0MkJibixo0bOHXqFF69eoVjx46hdevW+OGHH2TWWfj8/vTpE548eYJ169bh3bt3ePr0Kfr164cHDx5AX1+/yPwikQhTpkzBjh07AAB6enro378/evXqhTp16sDKygpJSUmIiorC2bNnER4ejoiICKxduxZnzpzRaDucOHECz58/h6mpqcJrk1jha4BIJEJSUhKuXLmCTZs2ITMzE6tXr0atWrUwceJEjWJSVVRUlErlrKys5E7r378/Vq5cyb1OTU3F8+fPsXHjRjx79gzx8fHo3bs3Xrx4IbeeNm3aoFWrVrh9+zbWrVuHLVu2qLcihBBCCCHFZDNoYKkuj8fjYUazGfC96IsuNbqgo3PHUl0+ITpVCglfUsqoBWv5gWK02BLLz89nHTt25Orq168f+/jxo8J5EhMT2axZs5i+vj67deuWRsuVbL0WGBgos0xMTAyztbXlyj169EijZclT3lqwJiUlMRMTEwaAOTk5KW3VdufOHXb+/HmZ0548ecLMzMwYAGZgYMC2bt2q8Jz49OkT8/f3l9lyrzRasPbu3ZtbRkBAgMwyM2fO5Mr4+/urfa5LtoLUhFAoZJ06deLq6NOnj8JzSSgUsv379zMHBwe2ceNGubHIO7/T09OZi4sLV+748eMyyy1evJgrU7duXaXnUXh4OPP09GT9+/dXWE6RNm3aMABs+PDhcsuocg1gjLG///6b8Xg8BoDVq1dPZhlttmDVlGQLVnnXFD6fz1q1asWVW7duncI6t2zZwoCCOwbUfU/+r6hI7+mE6EJ5+fxOLVgJKTuyIiOZ4PNnrdcreZ7HfInRev2E6II6+TXqg5WQcm7jxo24cuUKAKBPnz44ffo0HBwcFM5TpUoVrF+/HtevX4e9vX2Jxebi4gJ/f3/u9aVLl0psWeVBSEgIcnNzAQA//vgjnJ2dFZZv2bIlevbsWeT/jDH4+vpyfaoGBgbC398fenryL+n29vbYunUrTpw4AcP/9cVUmm7evAkAqFy5stwWkYsXL+aei1tjl6bNmzcjLCwMANC9e3el55Kenh5Gjx6NyMhINGnSRO3lWVpaYtGiRdzrv//+u0iZyMhIriWlo6MjwsPDlS6rbdu2uHnzJkaOHKl2TADw/Plzbn/5+vpqVIekLl26wNPTE0BB38PK+i8tywwNDaVatsraZ5KGDx8OfX19ZGRk4Pjx4yUdHiGEEEKIQl9OnsJbv7FI+P4HMH7J9Y/qYu1SYnUTUlZRgpWQciwvLw+//vorAMDMzAy7d+9WmGQrrHXr1qhdu3ZJhQegIEko9vbt2yLTY2NjMWPGDLi5ucHS0hJmZmaoW7cuvv32W5Vv9ZXnw4cP2LZtG4YMGYK6devC3NwcxsbGqF69Ovr3748///wTIpFI7vySA+9cuXIFIpEIe/bsQadOnVClShXo6elh7NixKscTFxfHPa9Tp47G63Xu3Dk8evQIANC3b1+MGjVK5XkHDx6stMuJksD/3wc4V1dXuWWsra1hZ2cnVb608Pl8rF+/HgBgYmKCPXv2wMBAtV50nJyc0LlzZ42W27hxY+55fHx8kemrV6/mjtHffvtN6Y8nYsbGxhg2bJhGMYm78jA3N0ePHj2UlFaN5H5XZ1CvskjZPpPk4OCA9u3bAwCOHDlSonERQgghhCgizMjAp40bAYEA2XfvInHFihIf+EosX5RfKsshRJcowUpIOXbp0iUkJiYCAEaMGKFy8qU0SbaWFAqFUtP279+PBg0aYNOmTYiOjkZmZiZycnLw+vVr7NixA82aNcPq1as1Wq5QKISTkxP8/f1x8uRJvH79GtnZ2eDz+Xj//j2CgoIwYsQI9OjRg+vDVJHc3Fx0794dEyZMwJUrV/Dp0ye1P5AYGRlxz589e6b2OokFBgZyz+X1+1nW1K9fHwAQExMjt0x6ejqSk5OlypeWS5cu4f379wCAoUOHwtHRsVSWK3lMFG5Z/OXLF64PVRcXFwwYMKBUYhK34vX09NRaa2fxjyumpqYl2mq+NCjaZ7KI+7i+fv06srKySiwuQgghhBBF9C0t4bz1N/D+91nmy/ET+Lx3X4kuU8REOPfmHHqf6o2Hnx6W6LII0TVKsBJSBhw/fhyNGjWCmZkZLC0tUbduXfj5+XGJDnmuXr3KPddWSzNtk2yFKpm0OnfuHMaOHYu8vDxYWFhgyZIluH79Om7duoVff/0VdnZ2EAqFWLBgAbZv3672csXJz86dO2PdunW4ePEiIiMjceXKFezZswetW7cGAISGhkp1YyDPvHnz8Pfff6Nfv344deoUIiMjcf78eZm38Msjvk0aAFatWsW1QlXX9evXARS0MPT29taojtI2efJkAEBKSgp+//13mWVWrFhRpHxpkTyXevfuXWrLlUy0Fx7UKzw8nPtRokePHmq1TteUQCDAnTt3AABeXl5aqfPKlSuIjIwEAPTr108rdeqSon0mi7gVP5/P10nXF4QQQgghYqZNm6Lazz9zrz+tXYsMJd85iyMkNgQ/Xv8R77PeY/299aXWYpYQXVDt/kdCSImKjo6Wev369Wu8fv0a+/fvx4ABA7B3715YW1sXme/x48fcc8nkXVmRkZGBbdu2ca87duwIoCCJ880334AxBgsLC1y/fh1NmzblyrVq1QqDBw9G69at8eHDB8yePRtDhw7lbh9Xhb6+Pl68eCHzVnxvb2+MGzcOS5YswfLly3HgwAEsWrQIdevWlVvf48ePsWjRIqkkoLo6dOiAJk2a4PHjx0hKSkKzZs3g7e2Nbt26oVWrVmjevLnM/SwpISEBSUlJAICmTZvKHHW+LBo/fjzCw8Oxf/9++Pv7IzIyEv369UO1atUQFxeHAwcOcK01Fy5ciK5duyrsvkHbJJPdzZs3L5VlCoVCrFu3jns9ZMgQncf04MED5OTkAACaNWum8nwJCQl48uQJ91okEiE5ORlXr17Fpk2bIBKJUL16dan+S0uKZBzyVKpUCdWrV9eofslW9YX3mSyS+y4yMhJdu3bVaLmEEEIIIdpg3bcP+DFvkLxtO8AY3s+ajZpHjsCkfj2tL6trza6oZV0Lb9Le4FHSI4S+DYWPi4/Wl0NIWUAJVqKxfU/3YX/0fqXlGtk2wm9dfpP637TL0xD9OVrOHP9vTKMx8HPz415nCbLQ70w/MBEDT4+ncN4tnbfArbIb9/pq/FUsv71c6TLNDMwQPDBYaTltMDMzQ79+/dClSxc0aNAAFhYWSEpKwtWrV/H7778jJSUFZ86cQf/+/REaGlrkdlTx7dQAFN52m5KSgg8fPsicZm5urrBfTE2kp6cjPDwcCxYs4G4Jb9WqFdcX4enTp7nbsRctWiSVXBWrWbMm1q1bh1GjRiE7OxuBgYGYM2eOyjHweDyl/ZwuXrwY27ZtQ3JyMoKCgjBr1iy5ZevVq4elS5eqvHxZ9PT0cPLkSfTs2ROvX78GYwxXrlzhBinj8Xhwd3dHz549MWHCBNSrV/RDTkpKCve8LHYJIY++vj727duHvn37YtWqVdi1axd27dolVaZTp05YsGCBThJQpbldk5KSEBUVhcWLF+PBgwcAChJ17dq101lMYu/evdNomYsWLZIasEuSsbEx5s2bh+nTp5dK1wuSfaTK4+fnh71796pc55cvX/Ds2TOsXr0awcEF7w+tW7fG8OHDlc4ruR3fvHmj8jIJIYQQQkqK3XffIe9NDDIuXoQoOxvvpkyBy/FjMKhcWavLMdAzwKwWs+B/ueCOwY2RG9HRuSOM9I2UzElI+UMJ1jLq+fPn8PDwAJ/Px4ULF8rk7d9Zgix8yv6ktFxV86pF/vc577NK82YJpPurY4ypNB8ACIQCqde5wlyV5jU3LL0BgBISEmBjY1Pk/926dcO0adPQs2dPPHjwAFevXsX27dsxffp0qXIZGRncc0UDFx04cAAzZsyQOc3b25tL8Glq3LhxGDdunNzptWvX5gbOAf5/5G0ej4fx48fLnW/o0KHw9/dHWloa/v77b7USrIWJRCIkJiYiIyMDAsH/HxtOTk5ITk5Weru+eDTw4qpTpw4ePXqEgIAA7Nq1Cy9fvuSmMcYQFRWFqKgo/Prrr/j+++/xyy+/SA22pOo+L4uePXuG/fv3yx287NatW9i9ezcaNmyocetCTZXkdl22bBmWLVsmc5qZmRkmT56MNWvWlGpM8ohbRwMFrTy1IS8vD/v27YOFhQUWLFhQKl0dFNe+ffuwb5/sPskMDQ3h6+uLzZs3q9QHq4mJCUxNTZGTk8P1mU0IIYQQoks8PT04rl6Ft+/eIffJEwjev8c7/+9QY99e6Bkba3VZ7au3x1dVv8KdxDt4l/kOR58fxRi3MVpdBiFlASVYy6gpU6bA0NCw1EfSVoe5oTkczJS3cLI1tpX5P1XmLZzs5PF4cDBzUKkFq6G+9BdfE30TlZZpZmCmtIy2yEquilWpUgUnTpxAgwYNIBAI8NtvvxVJsFpaWnLPs7KyYGVlVVKhqo3H46FBgwYYOXIkfvjhB1hYWHDTxLfwurq6Kmx5a2RkhGbNmuHKlSsq3fZbGGMMhw4dwu7du3Hnzh3u1mdZJFsDy9KkSRO1ly+PmZkZ5syZgzlz5uDff//FzZs3ERkZiVu3buHevXsQiUQQCoXYsGEDkpOTpRI9hfe5LinaJ66urlJJwevXr6Nv375IS0tDzZo1sXLlSnTr1g22trb4+PEjgoKC8NNPP+Ho0aO4du0aQkJC0LBhwyL1duzYUaq/VHl4PPnXB1mtF3V1LjVt2hTTp0+XmajTxb7+/Pkz91ydBGtgYCDGjh0r9b/MzEw8e/YMO3bswK5du/DTTz/h0aNH+PPPP0s0yVrSfXvVrVsXM2bMUOsYqVSpEnJycnR+zhJCCCGEiOmZmsIpIACxw4Yh/+NH5D57htyn0TDzVL2bKFXweDzMajELw/8aDgaGPx7/gf51+sPaWHHXaISUN5RgLYMOHDiAO3fuYO7cuXJbPZUFfm5+Urfvq6NwlwGqMjc0R+jgUKSnp8PKykqtL+nezt647HxZo+XqSq1atdCtWzecP38er1+/xvv376Vusa0scQtHUlKS3C/8P/zwQ5HR5hUloNS1cuVK9O/fn6vXzMwM9vb2UklVSeIkjiq3IFetWlVqHlXl5uZi0KBBuHDhgkrlFSVfAe215iusdu3aqF27NkaPHg2goFXz0qVLudvn9+/fj0mTJnG3j0vu848fP5ZITKpSdCt2WFgY1+duXl4evv76a6SlpaFq1aq4ffs2t1+BglbEU6dOhbe3N1q0aIH379/Dz88Pd+/eLelV4BTertpMsE6ZMgVTp04FAOTn5+Pdu3c4ceIEDhw4gJs3b6Jjx464e/dukR8bdLGvTUxMuOfKzgllLCws4OXlBS8vLzg4OGDVqlU4ceIE9uzZg4kTJxY31BLVv39/rr9YkUiE9+/f4+LFi/jjjz8QHR2Njh074tatW6hfv75K9Ym3pSotXgkhhBBCSothFQc4bQtAwsyZqL5+PUxV6GpJEw0rN0Tf2n0R9G8Q0vnp2PF4B+Z4aX53IiFlUdm/T+8/5suXL5g9ezZ+/PFHlUYnJhVfo0aNuOcJCQlS0zw8PLjn4r4cdaF69epwd3eHu7s73Nzc4OrqKje5KkmbSd7Cfv75Zy656u3tjWPHjuH169fIzMyEUCgEYwyMMa5fWGWt3kprMKnq1atj586dGDFiBPe/48ePS00XD/b16NEjbpT5suzixYvcsTtt2jSp5KokNzc3jBo1CkDBYECyum0IDAzkulEo/NizZw9XTl6ZqKgo/CwxcqqY5Ll0//79Yq1vYQ4ODtz50bRpU/Tp0wd79+7l4o2NjZWZcCzJmOSRTPKq+6OGIrNmzeJ+EJPcT2WVjY0Nt8+aNGmCHj16YNOmTfjrr79gYGCA1NRUjBw5UqXzTyQSIS0tjauXEEIIIaQsMXVzQ+1z50osuSo2rdk0GOsXdD9w+PlhxKfHl+jyCCltlGAtY3788UdYWFhg7ty5ug6FlBGKkpDe3t7c84sXL5ZGOFpha1vQbYQqrfLEfRaK51EFY4xrAdq+fXv8888/GDp0KGrXrg1zc3Opls/aTCJp06RJk7jnr1+/lprWoUMHAAW3jatyu3xJESepZT3ErVeBgr5XxTw9PRXWKTni+vPnz4tMd3V15RJfhR+Sg7XJK+Pu7i6zf1fJc+ncuXMqrX9x+fn5YfDgwQCAoKAg/PPPP1LT27VrxyX2L1y4AJFIVOIxSSZYU1NTtVavra0tV7e8PnjLgy5duuD7778HUJD0VmWgrLS0NG7f1ahRoyTDI4QQQgjRCM+g6M3N+Vr8LAgUjM0yplFB36v5onz89lCzu1oJKasqZII1OzsbFy5cwMqVKzFo0CDUrFkTPB4PPB5P5VHAMzIysHTpUjRu3BgWFhawtraGl5cXfv311xLrF/XOnTvYuXMnNm/eLHWbJvlvi46O5p4XHoG7e/fuqFKlCgDg6NGjUgPUlGXu7u4AgJiYGIUxCwQCrmWueB5VfP78mUvMDh06VG5XEpmZmXjx4oXK9ZYmyX1dOMkuOaDYpk2bSiskjUkO0pWfn6+wrOQAZAYyPuiVlO7du3Pb/Pjx40Vai5eUVatWcUnUBQsWSE2zsbHBgAEDAABv377FmTNnSjweyW4fJAdg0wbxvld2DJR1CxYs4LqQWLZsmdLPBJLb0c3NrURjI4QQQggpLiYU4uOaXxDTrz8EWu6makLjCbAztUO/2v0ws/lMrdZNiK5VyATr3bt30atXL/z00084ffo04uLi1Jr/7du3aNKkCZYtW4YnT56AMYa8vDzcu3cPs2fPRqtWreS27GGMITc3V6WH5JcyoVCIKVOmoFevXujTp0+x1p9UHDExMQgNDQVQ0Fdn4ZZ3xsbGmDmz4I0pKysLkyZNKpVWbsXVtWtXAAXnS2BgoNxyJ06c4G6tFc+jCskEjqJBZXbt2lWqyR51Bt+5d+8e97xWrVpS03r37s3dPh4cHIyDBw+qXO+pU6dKfaAdyZal169fV1hWskWu5HwlzcjICLNnzwZQ0H/vhAkTVO5+ISEhoUjrU1XVq1cPw4YNA1DwI5v4fBebP38+9wPBtGnT8OnTJ5XqzcvLw7Fjx9SOx9HRkTveIiIi1J5fntjYWKSkpAAAnJ2dtVavLtja2sLf3x8AEB8fLzUInSyS2/Grr74q0dgIIYQQQoor6bff8HnvXuQnJeHdlKkQZWdrrW5zQ3MEDQjCz+1+RlVz2d2GEVJeVcgEK1AwIE2XLl0wZ84cHDlyRG6ff4Xl5+ejb9++iI2NRbVq1RAaGoqsrCxkZ2fj6NGjsLS0xIMHD7h+Agt7+/YtTE1NVXpI3iq7detWPHv2DJs3b9bK+pOyLzg4WGFy7+PHjxg8eDCXiBcPklPYzJkzuVvGz549iyFDhiA5OVnhsrV5668mBgwYwLUW/Pnnn2XeMhwfH88lvMzMzKRabSpjb2/P9XV45MgR5OXlFSkTERGBn376SYPoNbdz50588803RW75L+zt27dYuHAh91o8gJgYj8fDoUOHYGZmBqCgReu2bdsUJteTk5Mxffp0DBkyRKqVaGno0qULF+v27dvl3iJ+4cIFnD59GkBBX7NNmzYtrRABAN9//z06deoEALh06RIGDhyosIU1YwyHDx9G8+bN8fjxY42Xu2DBAq6VsnhgJbHmzZtj0aJFAID379+jXbt2Sm+xv3XrFtq2bYvDhw9rFI+4X2JtDTImEonw448/cq979+6tlXp1acaMGdwxvWbNGoXJePF2rFGjhsqDYhFCCCGE6IrtmDEwdHICAORGR+P9vB/BtNiIx9LIUmt1EVKWlN79l6Woffv2RfpVlPxyp8i+ffu4L68nT55E69atAQB6enoYPnw4RCIRRo4cifPnz+Py5cvo0qWL1Px2dnYKW+RJEo9KnpaWhp9++gljxoyBnp4eYmNjAYBLkn38+BGxsbFwdnYutYF2SMmbNm0aBAIBBg8ejNatW8PFxQWmpqZITk7GlStX8Mcff3DHQLt27bgWU4UZGBjg+PHj6Nu3L+7evYvTp08jNDQUI0aMQKdOnVCjRg1YWloiKysLb968wbVr1/Dnn39y84uTBKXJyMgIO3bsQN++fZGeno62bdtizpw56NKlC/T19XHz5k2sWbOGa623fv16bmAnVejp6cHX1xcBAQF4/Pgx2rVrh5kzZ6Ju3bpIS0vD+fPnsW3bNlhYWMDR0VHrt0LLw+fzsXPnTuzcuRNt27aFj48PmjdvjipVqkBPTw8JCQkICwvDrl27kJGRAaAguVr4OgMU3Gp84sQJDBs2DJmZmfD398f27dsxfPhweHl5wd7eHllZWYiLi0NISAjOnDmD9PR0pTGeOHFC6bY2MjLCyJEjVV5vGxsb/Pjjj1i8eDEyMjLQpk0bTJs2Dd26dUOlSpXw8eNHnD17Fjt37uSSxGvWrJHbtUNJ0dPTw7Fjx9CnTx/cuXMHwcHBqF27Nnx9fdG5c2c4OTnB0NAQiYmJuH37Nk6ePCmzn1h1ubu7o1+/fjh79iyuXbuG8PBwtGvXjpu+ZMkSJCYmYseOHXj16hWaNm2KAQMGoHfv3qhTpw4sLS2RlJSEJ0+eICgoiGsF7PS/D8bq6t+/P/bt24e4uDi8fv0aderUUTpPQkICnjx5IvW/rKwsREdHY+fOnbh16xYAoHLlypg3b57Cuh4+fKhS36adO3eW2adp4TjkqVmzJiwtNfuAb29vj0mTJmHz5s148+YNDh8+jNGjRxcpxxhDWFgYAGDgwIEaLYsQQgghpDQZ2NrCefs2xH49EqLMTGSEhiJp8xY4zPihRJaXJciCkZ4RDPUNS6R+QkoN+4+oWbMmA8CWLFmisFz79u0ZANapUyeZ00UiEXN1dWUA2JgxY7QSW0xMDAOg9BEfH69SfWlpaQwAS0tLUyuOnJwcFh0dzXJycjRZjVIjFApZamoqEwqFug6lWMTHpLLH4MGDWWpqqtL6cnJy2IwZM5iJiYlK9VpZWbEFCxawzMxMjeIPDAzk6goMDNSojr179zJjY2O5Merr67NVq1bJnV+8Df38/IpM+/LlC2vatKncum1tbdnVq1eZt7c3A8C8vb2L1BEWFsaVDwsL02gdJf3555/MyMhIpf0DgI0cOZJlZ2crrPPRo0esbdu2KtVXuXJltmXLliLnjp+fn8oxAWDW1tZqr7tIJGI//PAD4/F4Cus2NDRk69atY4ypf65L7q/iyMnJYd9//71K+4rH47FRo0axhIQEubEoe99hjLG7d+9y5X18fGSW2b59O7O3t1dpH7Vp04bdvXtXo/UXCASsatWqDABbtmyZ3HKS1wBVHq6uriwyMlJmXZLbS9XH6dOnufnF57Gm8zMm/V4s65pSWHx8PHeMNGzYUOZxeuXKFa7OiIgIpXX+F1WU93RCdKW8fH7n8/nszJkzjM/n6zoUQoiKMq5dY9ENG7Ho+g1YdP0G7MuZMwrLq3ueC4QCduzFMeZ91Jvtf7pfGyETonXq5NcqZAtWTWVnZ+PGjRsAgJ49e8osw+Px0KNHD2zfvh0hISFaWa6DgwN3W6ykf/75B7/99hsWL16MZs2aqdWCj5R9+/btw9WrV3Hr1i28efMGycnJSE9Ph4WFBZydndGmTRv4+flxraiVMTExwYYNG7huMS5fvozo6GgkJycjLy8PNjY2cHR0RPPmzdG5c2cMHDhQJ61XJfn5+cHb2xubNm1CSEgI4uLiIBKJ4OjoiM6dO2PatGlSg+6ow9raGjdu3MCGDRtw7NgxvHr1CgYGBnB2dkbv3r3x/fffa9zCT1PDhg1D9+7dERISgqtXr+L+/ft48+YN12WDlZUV6tSpg9atW8PX1xfNmzdXWmeTJk0QHh6Of/75h2sB+f79e3z+/BlmZmaoVq0aWrRogd69e2PgwIE6G0CPx+Nh48aNGDVqFHbt2oXw8HC8ffsW2dnZsLCwQJ06deDt7Y1vv/0W9erV00mMYiYmJti0aRNmzpyJI0eO4O+//8bLly+RlJQExhhsbW3h7u4Ob29v+Pr6ombNmsVeppeXF7p164bQ0FCEhIQgIiICXl5eUmUmT56MUaNG4ejRo7h06RIePHiApKQk5OTkwMbGBrVr10abNm0wYsSIIvOqw8DAAOPGjcPq1atx+PBhLF68WKN6TExMYGdnBw8PD/Tr1w+jRo3S+TVHm5ycnODn54edO3fi2bNnOHnyJIYOHSpVRtxNg5eXF1q0aKGLMAkhhBBCNGLRvj2q/PgjPq5aBQD4sOgnGDo7w0yiq8Pi+PfLv1hxawUYGH5/9Dv61e4Ha2NrrdRNiC7wGFNj1JVyzMXFBW/fvsWSJUuwdOlSmWUiIyO5L0Dnz5+Xm2Tdtm0bd7t2SkoKbG1tSyTmvXv3Yty4cbhw4QJ69Oght1xeXp5UH5Pp6elwdnZGcnIyN9KxKnJzcxEfHw8XFxedJWFUwRhDRkYGLC0ti4yuTgipOOhc1524uDg0aNAAeXl5uHbtGtq2bavrkMqdjIwMuLi44MuXLzh06BBGjBih65DKJDrPCSme3Nxcriuxsvz5XSAQIDQ0FN26dYOhId0GTEh5wRhD0sqVSD92HACgb1sJTocPw7DQ4MuAZuf54luL8VfMXwCA0Q1GY4bnDO0FT4gWpKenw87ODmlpaUrza9SCVcL79++554VHa5ckOe39+/cllmBV1erVq7Fs2bIi/w8JCVGrtZCBgQGqVq2KzMxMbmClskzcRyUhpGKjc7302djYYOLEiQgICMCyZctw4sQJXYdU7mzYsAFfvnxBgwYN0KNHD5X6QP4vo/OcEM3w+Xzk5OTg2rVrCgdPLStCQ0N1HQIhRF1Nm6L6/Qcwf/0aws+piJ7+PRImjJdbXJ3zvIGoAS7iIvKRj8PPD8M+wR62+rrNrxAiKTs7W+WylGCVIPnhXlFiUnJaSX4hGDt2LMaOHau03Pz58zFz5kzutbgFq4+Pj0YtWC0sLMr0L+DU2oWQ/wY613Vr6dKlOHjwIC5fvoznz5+jZcuWug6p3MjKysL27dsBAOvWrYONjY1uAyrD6DwnpHhyc3NhamqKDh06lOnP79SClZDyTdjBG+9G+ULPzBwuWzbDo0qVImU0Pc8/P/qMPU/3QAghoipF4Zd2v2gzdEKKRZ1GEpRgrQCMjY1hbGxc5P+GhoZqXdiEQiF4PB709PRKffRudYhHGRfHSgipmOhc1y07OzscOHAAkZGR+Pz5M+0DNcTHx8Pf3x+2trbo06ePrsMp0+g8J6R49PT0wOPx1P7cryvlJU5CiDRDu8qouWcP9CtVgp6pqeKyap7nk5pMwpl/z+Bz7meExoViTOoYNHVoWsyICdEOdY5lSrBKsLS05J4ragYsOU1yHkIIIaQi6du3L/r27avrMMqdRo0aye3vnRBCCCGkPDJ0dCyRei2MLODf1B8rbq8AAPx671fs77mf7mwh5Q41FZDgKHHBSEhIkFtOcppjCV1kCCGEEEIIIYQQQsoiYUYG4qf6I+v27WLXNajuILhauwIAHiY9xN9xfxe7TkJKGyVYJTRs2JC7Pe3Jkydyy4mnVa1aVecDXBFCCCGEEEIIIYSUlvyUFLwdORKZ//yDd9O/R15MTLHqM9AzwKzms7jXOx/vBGOsuGESUqqoiwAJZmZmaNu2La5fv46LFy9izpw5RcowxnDp0iUAgI+PT2mHqBaBQACBQKBWecYYRCIR1ydaWSS+0IpjJYRUTHSuE1Lx0XlOSPGIRCIwxiAQCKCvr6/rcOQSfydR57sJIaTsYubm0K9aDXj1GqL0dMRPnoIqewMBaH6et67SGq2qtoKzpTO+bfwt8vPztRkyIRpR53imBGshfn5+uH79OsLCwnDnzh189dVXUtOPHz+ON2/eAADGjBmjixDlCggIQEBAAIRCIQAgJCQEZmZmKs9vYGCAqlWrIjMzE3w+v6TC1JqMjAxdh0AIKQV0rhNS8dF5Tohm+Hw+cnJycO3atXKRjAgNDdV1CIQQLdHr2gXOr17COPEjBG/f4uXEScCE8cU6z3uxXtDL1cPtf4rf7QAh2qBofKbCeKyCtrtOTU3lEo0A4Onpifj4eMyZMwdz587l/m9iYgILCwvudX5+Pjw9PREVFYXq1atj37596NKlC0QiEU6ePImJEyciPT0dPXv2xPnz50t1nVSVnp4Oa2trJCcnw8rKSuX5cnNzER8fDxcXF5iYmJRghMXDGENGRgYsLS2p42tCKjA61wmp+Og8J6R4cnNzERsbC2dn5zL9+V0gECA0NBTdunVTa0RmQkjZJkhIwLuRIyH8nAoA+NKyJZps3wYjIyMdR0aIdqSnp8POzg5paWlK82sVtgVrs2bN8Pbt2yL/X7duHdatW8e99vPzw969e7nXBgYGCAoKQqdOnRAbG4uuXbvCzMwMIpEIubm5XN2HDh0q8XUoLkNDQ7U+wAiFQvB4POjp6XF90ZZF4lsIxbESQiomOtcJqfjoPCekePT09MDj8dT+3K8r5SVOQohqDF1c4LQ1AHF+fmACAWzu3kX28eMwHztWK/Wn5aWBL+TD3sxeK/URoi513rPok6wMLi4uePz4MRYvXgx3d3fuQ0vz5s2xfv163L59G5UqVdJ1mIQQQgghhBBCCCE6Y+bZDNVW/cy9Tl7/K3Kjo4tVJ1/Ix76n+9DzVE+sjVhb3BAJKRUVtgVrbGxssea3tLTEsmXLsGzZMu0ERAghhBBCCCGEEFLBWPfti+zoaHwJ3Avk5yNhzly4njwBPQ27LuEL+djzZA8y+Bm4GHsRoxqNgoe9h3aDJkTLqAUrIYQQQgghhBBCCNFY5WnTkOtYDQBgVKMG2P+6WNSEhZEFpnpM5V6vj1iPCjp8EKlAKMFKCCGEEEIIIYQQQjTGMzRE4ogRsF+8GE7bAqBvY1Os+gbXGwxXa1cAwMOkh/g77m8tRElIyamwXQSQgtE6BQKBWuUZYxCJRNygE2WR+JcrcayEkIqJznVCKj46zwkpHpFIBMYYBAIB9PX1dR2OXOLvJOp8NyGElC8CgQD8KlVg1q0b8vPztVLn902/xw9XfwAAbLi3AW2rtIWhPg2UR0qPOu9bPEbtrCuMgIAABAQEQCgU4uXLlzh8+DDMzMxUnt/AwABVq1aFs7MzjIyMSjBSQgghhBBCSHHx+XzEx8cjMTFRawkNQgjRJr2cHIhMTAAeT+15GWPYk7UHMfkxAIBepr3QxriNtkMkRK7s7GyMHDkSaWlpsLKyUliWEqwVUHp6OqytrZGcnKz0AJCUm5uL+Ph4uLi4wETDzqhLA2MMGRkZsLS0BE+DizQhpHygc52Qio/Oc0KKJzc3F7GxsXB2di7Tn98FAgFCQ0PRrVs3GBpS6zNCKiJZ53nW9ev49NNiVP5+OqwGDtSo3mefn8H3oi8AwNrIGmf7nYWVkep5DkKKIz09HXZ2diolWKmLgArM0NBQrQ8wQqEQPB4Penp60NMru93zim8hFMdKCKmY6FwnpOKj85yQ4tHT0wOPx1P7c7+ulJc4CSGaE5/nuS9e4MNUfwBA0ppfYNmqFYxq1FC7viZVmqBvrb4IfhOMNH4a9j7bi1ktZmk7bEJkUuc9iz7JEkIIIYQQQgghhBCtMalfHzZDhwAAWHY23s+ZC6ZhVybTPafDWN8YAHD+zXnk5udqLU5CtIUSrISQUrV3717weDzweDzExsbqOpwSc+XKFW49r1y5UmT60qVLuenlEZ/PR926dcHj8XDixAldh1Ms/v7+4PF48PPz03UoRAPe3t7g8XhYv369rkMpVyIiIsDj8WBra4vPnz/rOhxCCCGEVEBVfvwRhv9rtZrz6BGS//hDo3qqmlfFhMYTMM59HE4POA0Tg7LbJQr576IEKyE6IpmAK/wwMzODs7Mz+vTpgz179iAvL0/X4ZYpHTt2lNpey5cvV2m+n3/+WWq+jh07lmygFdjmzZvx+vVruLu7Y/DgwUWmSyaQJR96enqwsrJC/fr1MWrUKFy6dEnpsgqfK5aWlsjOzlY6X05ODqytraXmlZXsnjdvHoyMjHDgwAFERkaqtP6qePnyJebMmYOWLVvC1tYWhoaGMDc3h4uLC7p06YK5c+fi3LlzKq0LAISFheGHH36Ap6cnqlatCiMjI9jY2KBBgwbw9fXFwYMH5dY1duzYUvthIzY2FvPmzUPz5s1hY2MDQ0ND2Nraok2bNli+fDk+ffqktWUdP34c165dg729PaZMmVJkuuQPOvKORXd3d3z77bdK973kcbh06VK1Yy183VLl8eXLF6k6YmNj5ZY1MTGBo6MjfHx8sHnzZqSnpyuMx8vLC927d0dqaqpG60MIIYQQooyeuTmqr/0F0NcHACRv246cR480qmuKxxTMbD6T+l8lZRYlWAkpg3JycvDu3TucO3cOEyZMQPPmzSt0a8/iOnTokErlDhw4UMKR/DdkZGTgl19+AQAsWrRIrVa44gFtXr58iUOHDqFHjx4YPHiwWj8iZGZm4syZM0rLnT17VmmSCQBq1KgBPz8/MMbw008/qRyHIsuWLYObmxvWr1+PiIgIpKamIj8/H9nZ2Xj79i3++ecfrFu3Dn369FH6A8Hjx4/Rvn17dO7cGZs3b8aDBw/w8eNHCAQCpKWl4cWLFzh8+DBGjx4NJycn/Prrr1y/lqXtwIEDaNSoEdauXYv79+8jLS0N+fn5SE1Nxa1bt7BkyRI0bNgQoaGhxV6WSCTC4sWLAQCzZs2Cubm5WvOLj8WnT59ix44d8PLywoIFC4odl67k5eXhw4cPCA0NxQ8//AB3d3fcv39f4Tzi7ffHH38gPj6+NMIkhBBCyH+MadOmsBP/EC4UImHuXIiysnQbFCElgBKshJQBU6ZMQVRUFPe4fPkyNm/eDCcnJwDA06dP0a9fPwiFQh1HWraIR8t9+fIl7ty5o7BsREQEXrx4ITUf0cz27duRkpKCGjVqYOjQoUrL79mzhzu2Hz16hL/++gsLFiyAqakpAODUqVOYOXOmSssW7ztVkuXiMqrs71mzCjrKv3DhQrFbsa5ZswZLly5Ffn4+rK2tuZaq9+/fx+3bt3HixAnMmTMHDRo0UFrXhQsX0LZtW4SHhwMA3NzcsHz5cly4cAH37t3D1atXsX//fowcORIWFhZITU3F7NmzVUosa9uNGzcwduxY5OTkQE9PD+PGjcOZM2dw9+5dnDhxAn379gUAfP78Gf3798ebN2+KtbwTJ07g+fPnMDU1xdSpU5WWX7lypdR19tGjR/j777+xaNEiWFhYgDGG1atXY9euXcWKSxWScSh6KBqptH///lJlr127hh07dqBhw4YAgPj4ePTu3VvhsdCmTRu0atUKfD4f69at0/p6EkIIIYQAgN3kb2Hi0QQAIHgbh49rfil2nck5yXia/LTY9RCiLQa6DoCUHIFAAIFAoFZ5xhhEIpHOWj+pgjHG/S3LcSojGbu9vT0aNWrEvW7UqBE6duwIPz8/NG3aFLGxsYiKisLJkycxZMgQXYSrNZLrXdxjrUqVKqhSpQru3r2L/fv3w8vLS27Z/fv3AwC++uorJCYm4u3bt0Xi0SZl6yk+jksyhpIgFAqxdetWAMCIESMAyI5fcv1q1qwpdXy7u7ujZ8+eGDRoEFq1aoX8/Hzs2LEDCxcuRNWqVYvUIVlX3759cfz4cYSGhuL9+/dS5SV9+vQJISEhAIB+/frh2LFjXKyy4q1bty48PT1x//59bNmyBYGBgaptkEKSk5OxbNkyAICTkxPCw8Ph7OwsVcbLywsDBw7EmjVrcPfuXaSkpMiM6enTpxgyZAiys7NhYGCAjRs3YvLkyUVGWm/Xrh18fX2RlJSE5cuXY9u2bUXWs/DxVhLH3KpVq7h6t2zZInXLfvPmzTFw4EDMnj0bGzduRE5ODn799Vf89ttvGi9v8+bNAAqOCXNzc5nrJPm/atWqSR2HYp06dUKHDh3QvXt3MMawbt06jB8/XmFdxX3/kRWHPIWvJWLW1tZF6mnbti3GjBmDjh074vbt20hMTMQff/zB/YAgy9dff43bt29j7969WL58ucKkbkmoKO/phOiKSCQCYwwCgQD6/7sFtywSfydR57sJIaR8UXaeO6xahfghQ8FycvDl+HGYtGsHi86d1F5Ovigfe57uwb5n+1DZpDJO9j4JQ33VR3onRB3qvG9RgrUCCQgIQEBAANfKMSQkBGZmZirPb2BggKpVqyIzMxN8Pr+kwtSajIwMXYdQLJJ9Jebl5cltZTRz5kxMnz4dQEFrNh8fn1KJr6Tk5v7/iI+ZmZkatbTL/9/okyKRCEOGDMHdu3fx559/YunSpTA0LPrmmp+fj6NHjwIAhgwZgi1btnD/L6mWfpL7Nzs7u8hyJG+J10VrQ039/fff3K3E/fr1kxu75PrJWn8AqF27NgYNGoRjx44hPz8fFy5ckNmfa05ODve8ffv2uHbtGj5+/Ii9e/fKbbm4d+9e5Ofno0qVKmjXrh2XYJUXCwAMGjQI9+/fx4kTJ7By5UpYWlrK2QrynT17ljvGp0+fDmtra4X7V9yKtXAZxhhGjhzJHUdbt27F8OHDkZmZKbcuY2Nj/Pzzz/jqq6+4VqRikh8MND3vlLl58yYAwNbWFr6+vjKX8f3332Pjxo0AClq8ahrHy5cvueUNGDBAbj2S15vc3Fy55by8vODh4YGHDx/i5cuXePfuXZFEo6rXbHnyJUbN1XS9Jfe/QCCQW8+PP/6IAQMGAAAuXbqESZMmya2zZ8+emDlzJjIyMnDgwAGMHj1ao9iKq7y/pxOiK3w+Hzk5Obh27ZrUdaas0kYXMYSQsk3ReW7VuxeqnjgJobEx7t+9g8zcHLll5WGM4VLWJeTk5+Bd5jssC1qGNsZtihMyIXKpOl4GQAnWCsXf3x/+/v5IT0+HtbU1fHx81GqJkpubi/j4eFhYWJTpW6jF/eZZWlqW2xHYAUglv42NjeXuK8lWmR8/flS4T8PCwrBv3z6Eh4cjMTERBgYGqFmzJrp3744ffvgBjo6OSuMqbh2pqalYu3Ytzp49i7dv38LS0hJNmjTBpEmTMHToUKljy8LCQqPWUgYGBZcuPT09+Pn5YeHChUhJScGNGzfQr1+/IuWDg4ORnJwMQ0ND+Pn5cS0wDQwMiiw/NjYWtWvXBgDs3r0bY8eOlRtHrVq18PbtW4wZM6ZIi0fJ/WtmZlZkOcbGxtxzRdsgNzcXu3fvxpkzZ/D06VN8/vwZNjY2aNKkCYYPHw4/Pz9ueyiLLzIyElu3bsW1a9fw/v178Pl8tbudOHfuHICCFp+tW7eWW05y/WStv5inpyeX/ExJSZEqJz7XxV0JAIC5uTm+/vprbNq0CSdOnMCPP/4os97jx48DAEaOHCnVN6eiWEaOHIlFixYhOzsbYWFhGDVqlNz1kyc5OZl77u7urnFrwL/++gtPnjwBAPTp00dhgqwwWXFL/vCg6XmnjDiJW6tWLbn1W1lZwc7ODsnJyRAKhRrHceHCBQAFx8OgQYNk/rACSHcPYWJionB5tWvXxsOHDwHIviares2WR/I81XS9LSwsuOeGhoZy6/nqq6+45x8+fFC4PCsrK7Rv3x5XrlzB2bNn4e/vr1Fsmqoo7+mE6Epubi5MTU3RoUOHMv35XSAQIDQ0FN26dZN7zSaElG+qnOesZ098treHVf/+qF+9usbLcv3silEXCz7z3hDewNyuc2nwK1Ii1GkYQQnWCszQ0FCtDzBCoZAbWbnwLahlifgWQnGs5ZVk7IrWRfLDspGRkcxyubm5GDduHNdKU9KTJ0/w5MkT/P777zhy5AjXD2JJ1PHs2TN07doV79+/l6r38uXLuHz5Mi5duoQOHTpw07RxrDk4OKBnz54ICgrCoUOHuFZbksSDYPXq1Qv29vZS0wovX/K1qvHJ2n/K6pFMJMhbxqNHj9C/f3+uOwOxpKQkbpvu3LkTwcHBqFKlisL4duzYgWnTphVp3aLu9r9y5QoAoFWrVgrnLbx+8spKJmILH9+S57pkXWPGjMGmTZvw4MEDPHv2DG5ublJ1RkdHc4P7jBkzhkuaKYvF1dUVVatWRWJiIi5duoQxY8bIXT95JNfnxYsX6Nmzp9p1AMC+ffu45zNmzCj2eaLq/iiO+vXr4/79+4iJiZFbf3p6OpeErl+/vsZxiI9DT09PqW1emDrnc1xcHADA1NRU5vmk6jVbFZrOq8n7hqGhodLltWrVCleuXMH169eRk5Oj9oBhxVFR3tMJ0RU9PT3weDy1P/frSnmJkxCiOWXnedXvvy/2MjyqeKBPrT74681fSOOnYe+zvZjVQn6XSIRoSp33LPokS0gZ9+zZM+65i4tLkemMMQwZMoRLjPbt2xcHDhzAjRs3cOvWLWzevBk1atRAVlYWhgwZgnv37pVIHenp6ejevTuXXB0+fDjOnz+Pe/fu4fDhw2jRogUCAwOxbds2bWwWKeJbWoODg/HlyxepaWlpaQgODpYqVx68fv0a3t7eePv2LaysrDB//nycPn0a9+7dw6VLl+Dv7w8DAwNERESgf//+CvuGiYiIwHfffQcnJyds3boVt2/fRnh4OFavXq1WTO/evUNsbCwAKOzvVh3Kjm9ZmjVrxiVVZQ12Jf6fu7s7mjZtqlY8LVu2BABcvXpVrfnEPD09ueerVq3Co0ePNKrn+vXrAApaaHp7e2tUR2mbPHkygIKWyL///rvMMitWrChSXl0CgYAb1E5bx+GVK1e4wc1ktYIvb9Q9r8THPZ/Px61bt0oqLEIIIYQQmURq3IYtNr3ZdBjrF/zQfujZIbzLeKftsAhRC7VgJaQMEwqFUiM7yxrgateuXTh37hwMDQ0RFBSEHj16SE1v1aoVRo8ejfbt2+Pp06f44YcfuBHJtVnHihUruL45V61ahfnz53PTmjdvjiFDhqBPnz7cwEPa1LdvX9jY2ODLly84fvy41O3Ux48fR25uLipVqoQ+ffpofdklxc/PD2lpaWjWrBlCQkJgZ2cnNd3Hxwd9+vRB7969cefOHezdu1fubeTR0dFo3Lgxrl27BhsbG+7/bdu2VSsmcZ+XQEGSs7ji4+O51sU2Njbo2rWryvOOGTMG8+bNw+HDh7F69WquhSZjjKtTkxaozZs3R1BQEBISEvDx40eFLYNl6dChA5o0aYLHjx8jKSkJzZo1g7e3N7p164ZWrVqhefPmsLa2VlhHQkICkpKSAABNmzYt04OWSBo/fjzCw8Oxf/9++Pv7IzIyEv369UO1atUQFxeHAwcO4MyZMwCAhQsXqrW/JT148IDrl1ed4zAhIYHrdgEoaDmZnJyMq1evYtOmTRCJRKhevTpWrlypUVzqkIxDnkqVKqG6hrfOSf54osrAiM2bN+eeR0ZGarxvCCGEEELUIczMwsfVq5D77Blcjx4Fz8hI5XmrWVTD6EajsStqFwQiAbbc34K13mtLMFpCFKMEK9FYSuBefN67V2k5k0aN4LxdutVi/JSpyI2OVjqv7dixqDxuLPdamJmFN717QyQSIUnJrYROAQEwdf//W4czwsKQuHSZ0mXqmZmh9oXzSsuVpKSkJERFRWHx4sV48OABgIIvye3atZMqxxjDL7/8AqBgQJ3CiVGxSpUqYd26dejVqxdu3LiBV69eoW7dulqrg8/nY/fu3QCAJk2ayOwX09DQELt370atWrW0PoKssbExhg0bhh07duDgwYNSiUZxa8Zhw4YpvJW4LLl+/TqXzNy3b1+R5KpYjx49MGTIEBw7dkxhghUoGARPMrmqiXfv/v9XYQcHB43qEIlEePfuHcLDwzF//nxkZWUBKEjQS/YvqYyvry/mz5+P+Ph4XLlyBZ06FYxAeuXKFcTHx0NPTw8jR45UOz7J9Xrz5o3aCVY9PT2cPHkSPXv2xOvXr8EYw5UrV7hb2nk8Htzd3dGzZ09MmDAB9erVK1JHSkqKzHjKOn19fezbtw99+/bFqlWrsGvXLuzatUuqTKdOnbBgwYJiJfA0PQ4XLVqERYsWyZxmbGyMefPmYfr06Sr1VV1cjRs3VlrGz88Pe1V4jxX78uULnj17htWrV3Ot9lu3bo3hw4crnbfwcU8IIYQQUhrez56NzP99Tk76bSscZs1Ua/4J7hNw6tUpfM79jAuxFzCq0Sg0sW9SApESohx1EUA0JsrMRP7Hj0ofws+fi8wr/PxZpXlFRUbMZgX/T0pSOi8T8KXnzMtTaZn5nz6V4FaTbdmyZeDxeNzDwcEBXbp0wY0bN2BmZoaZM2fi8OHDReaLjo7Gv//+C0B5KyXJvk8lbwHVRh2RkZFITU0FUJAUkDdQiZOTE3x8fBQuQ1Pi2/+vX7/O9Vn69u1b7lbr8tQ9QFBQEICCPiqVJWLE+yQiIkLu6MHOzs5o3759seMSt6oEChLuqurUqRN3bOvr66NmzZrw9fVFXFwcHB0dsWvXLnz33XdqxVK9enUuqSrZTYD4eefOnTVq/Wdra8s9T0xMVHt+AKhTpw4ePXqEtWvXFkmgMsYQFRWFtWvXolGjRpg1a1aR/SY5mnpp9oWpDc+ePcP+/fsRFRUlc/qtW7ewe/duJCQkaLwMTY9DRfLy8rBv3z7s2bOH6xO0rNu3b5/U+0alSpXQpk0bBAcHw9DQEGPHjsXFixdV6jfKxMSEG0xO0+OeEEIIIURddtO+A/73WSVl1y5kR0SoNb+FkQWmeEzhXq+/tx6MMa3GSIiqKMFKNKZnYQGDKlWUPvQlEhZi+ra2Ks2rV6RFG6/g//b2SuflGUrfXsAzNlZpmQZlrMVY06ZNMX36dJlfkiX7Qm3durXUl+3CD8nWgZJfoLVRh2QyRVmfiOK+/rStXbt2qFWrFhhjOHjwIICCZBtjDLVr11b7dnhdEu+TFy9eKNwfPB6PS0wKBAJ8lvFjBlDQqlgbJOvXVmKrZ8+e8PX11WhecRcAJ0+eRE5ODnJycnDixAmpaeqSXC9x61pNmJmZYc6cOXjx4gVev36N/fv34/vvv0fLli25gXyEQiE2bNiACRMmSM1raWmplRi0QTzAnaxH4diuX7+O1q1bIzg4GNWrV8eBAweQmJgIPp+P+Ph4BAQEwMzMDEePHkXLli3x9OlTjWLS9DgMDAwEY0zqkZGRgbt372LixIlITEzETz/9hOHDh5d4krVwHLIe6rReLaxu3bqYMWMGrKxUH01XvC11fcwRQggh5L/D1M0N9tOnFbxgDAnz5kEo0dhAFYPrDYaLlQsAICEjAR+zP2o5SkJUQ10EEI1VHid9+746CncZoCp9C3PUDvsH6enpsLKyUmvEYctOnWD5vxZvZc2UKVMwdepUAEB+fj7evXuHEydO4MCBA7h58yY6duyIu3fvwt7eXmq+Txq2ts2W6ERcG3VIJjyU3bKr7i3X6hg1ahSWL1+OAwcOYOHChVxrxlGjRpXYMkuCNvaJJG0lQyVHJs/JyZFKBCqyZ88eLvGek5OD169f448//sDVq1exe/duJCYmIjg4WG7LZ3kGDRqEKVOmID09HWfPnuUSZubm5hg0aJBadYmJ+/YE1BsxUpHatWujdu3aXCvqhIQELF26lLt9fv/+/Zg0aRLXBUjlypW5eT9+1O0HREUtqMPCwtCxY0cABS1Av/76a6SlpaFq1aq4ffs2qlatypV1cnLC1KlT4e3tjRYtWuD9+/fw8/OTOWCeMoWPw+KwsLCAl5cXvLy84ODggFWrVuHEiRPYs2cPJk6cWKy6S1r//v25/mJFIhHev3+Pixcv4o8//kB0dDQ6duyIW7duoX79+irVJ96WNLo3IYQQQkpT5fHjkXX1GrLv3UP++w9IXLEC1deq3peqoZ4h5njNQXRKNMY0GgMzQ7MSjJYQ+SjBSkgZ4ODgAHd3d+5106ZN0adPH3Tq1Aljx45FbGwsJk6ciLNnz0rNJxQKuefBwcEqj8IumQTVRh2S1E2SadPo0aOxfPlyvHjxAlu3bsXLly+5/5cn4n3i4eHBtcZVhbxb4rU1SJJkgv/z588qJ1hdXV2ljm8vLy+MGDECEyZMQGBgIM6dO4dNmzZhxowZasVjYWGBgQMH4tChQ1xrZQAYOHCgxrfWS/5YUNw+a+WpXr06du7ciczMTBw9ehRAwWBs4gRr9erVYWdnh+TkZDx69AhCobDMD3R18eJF7rb/adOmSSVXJbm5uWHUqFHYtWsXIiMj8ejRI3h4eKi1rMLHobbMmjULa9asgUgkKhcJVhsbG6nzqkmTJujRowf69u2LHj16IDU1FSNHjsTdu3eVHj8ikQhpaWlcvYQQQgghpYWnrw/HX9bgzYCBEGVkID0oGBbe3rDu3VvlOjo4dUAHpw7KCxJSgijBWoEJBAK1BhMSCARgjEEkEpXpPujESRRxrOWVZOzy1mX06NEICgrCqVOnEBQUhL///hudO3fmpku2TLSyskKjRo3UXr426pAcFf3Dhw+oU6eO3HkkuxbQxrEmOX+tWrXQunVr3Lp1C3PmzAEAtGnTBq6urnKXo2j5+fn5CqeLb6WVtf8kX8taT8m+gQpPE7dgzMzMVGt/yKpLXnyakGxZmZKSAmdnZ7llC6+frOVv2bIFly9fRlxcHJYtW4bRo0dL9YEqea7Lq2vUqFE4dOgQQkJCpP5XePsri0VMMmHn5ORUoteYCRMmcAnWV69eSS2rffv2OH36NLKyshAWFiZ13mtClf0hi+QPMLKI64mWGLSwadOmCutv1qwZ9zw6OlqlAZ8kFT4OFS1LnX1vY2MDe3t7fPz4EVFRUQrP6eKeU5rOq0oMnTp1wvTp07Fhwwbcv38fe/bsKdINRWGpqalcXc7OzqX63lpR3tMJ0RWRSATGGAQCQZn+MU78nUTbA50SQsqOYp3nDg6wX7gAH3+cDwBIXLoMhk2awFDOj/aElBZ1jmdKsFYgAQEBCAgI4L4Qh4SEwMxM9ebxBgYGqFq1KjIzM8Hn85XPoGMZavbNUtZI3s6dl5eH9PR0meXmz5+Ps2fPQigUYv78+QgNDeWmSQ6g888//2jU16Y26qhVqxb3PDw8XGGLNMnBsTIzM+WutyLiQYFEIlGR+QcPHoxbt24hNzeXe124jPhLfH5+vsLlJyYmyp2emprKjfYuEAiKlJPcv9nZ2UWm5+Xlcc8LT2vUqBFu3ryJN2/e4NWrVxp3qyBeT1nxaUJyPz969EjqdWGS6ydr/cVmz56N6dOnIy0tDStXrsTSpUuLlJG8DTw3N1eqLi8vL1StWpVL3FerVg1eXl5SZcTHgrJYgII+R4GCUeUdHBy0st3kkewfUygUSi1r2LBhOH36NADg119/RYsWLYq1LMkPBpqed4pIDtSVnp6usP5MicELNTk2JVvZR0VFKUw+S+77wseOLOLtJOvaoOo1W57C20gTqm677777Djt37kRGRgaWLVuG/v37w8jISGZZAHjw4AH3vFatWiV63MtT3t/TCdEVPp+PnJwcXLt2Te5gl2WJ5OdYQkjFpPF5zuOhalMPWD18BFFGBp5Nnox3EycCanQLKJYqTEW8MB5NjLQzFgX575LXDZ8slGCtQPz9/eHv74/09HRYW1vDx8dHrQEucnNzER8fDwsLC6k+7soacT+LlpaWOr0dvbgkk9/GxsZy95WnpyeGDh2Ko0eP4t69e7hz5w66desGoKCVm5OTE969e4cDBw5g7ty5au87bdTRoUMHVKpUCampqThx4gR+/PFHmfsmISEBYWFh3GsLCwu1jlExA4OCS5eenl6R+f38/LBs2TLw+XwYGRlhzJgxRcqI++41MDAoMs3Kygo2Njb48uULnjx5Ije+Q4cOcS2vDA0Ni5ST3L9mZmZFphsbG0stU9LgwYOxa9cuMMYQGBiIVatWyd4QSojXU1Z8mvD29oaJiQlyc3Px9OlT+Pn5yS0ruX6y1l/sm2++wfr16xEXF4c9e/Zg0aJFXCtW8bkuHt0cKOh/s3Bdo0ePxpYtW7jnhW9xljyeFcUCAI8fPwZQ0MpSsqWkqhhjKl+Xnj9/zj2vV6+eVFxDhw7FmjVr8OjRI1y8eBFBQUEq9yV86tQpdO/eXaqbBMl+NTU97xRp0KAB9/z+/fsYNmyY3LJ37tzhnjdq1EjtWKysrFCrVi28efMGUVFRCueX3Peyjh1JsbGxXAtmZ2dnhee0omu2POLrlngdNCE50KCi89rKygr+/v5Ys2YNEhIScPr0aUyaNEluvc+ePeOee3t7a/34UKSivKcToiu5ubkwNTVFhw4dyvTnd4FAgNDQUHTr1o36eiakgtLGeS5s2w7xQ4YgPzERle0d4ObtDX0VuyUT2/54O/ZG7wUAjOk6Bo4WjhrFQgigXsMISrBWYIaGhmpd2IRCIXg8HvT09NQaPKq0iVvliWMtryRjV7YuCxcuxJ9//gnGGFatWoXu3btzdSxYsABTp07FmzdvMHbsWBw4cEAquSUpPT0d+/fv50ae11YdpqamGDduHDZs2ICHDx/i119/xdy5c6Xmy8/Px7fffivVOlobx1rh+e3s7NS6CMpafocOHRAUFISgoCDExMSgdu3aUtNfvHiBxYsXc69l7T/J17LWUzKRUHhajx490LJlS9y9exfr16+Hp6enwoRVVFQUYmNj0bdvX5nTtXWumJiY4KuvvsLVq1cRERGhsM7C6yevrLGxMebOnYvvvvsOGRkZ+O2337Bs2TIA0ue6orrWrl2LtQo6wle2L8Ty8vK4BKuPj49G22zHjh24d+8e5s6dq7CrjLdv3+Knn37iXg8YMKDI8g4dOoSWLVsiOzsbEyZMQGZmJiZPniw3ruTkZCxfvhxbt27F58+fi1xjxEriGt+tWzeYmZkhOzsbv//+O0aPHi3z1v8LFy7gzJkzAAr6mvX09NQolvbt2+PNmzdKj0NV971IJMKCBQu4171791Z4Thf3nNJ0XnVimDlzJrZs2YLs7GysXbsWEydOlHv7cEREBACgRo0aaNiwoUaxaaqivKcToit6enrg8Xhqf+7XlfISJyFEc8U5zw3tKsPxl1+Q9/wZKo0eDZ4Gnw1EEEEgKrgrKSAqAGs7qD5gFiGFqXMsU4KVkHLA3d0d/fr1w9mzZ3Ht2jWEh4dzA+JMnjwZoaGhOH36NI4fP4779+/j22+/RcuWLWFtbY309HQ8f/4cV65cQVBQEExMTKSSo9qqY/HixTh27BjevXuHefPm4eHDhxgzZgwcHBzw8uVLbNiwAREREWjRooVGI4eXpqlTpyIoKAg5OTno2LEjli5dimbNmiEzMxOXL1/G5s2bYW9vD319fSQlJZVIDIcPH0bLli3x+fNnDB8+HAcPHsTw4cNRt25d6Ovr49OnT3jw4AGCg4Nx+/ZtzJo1S26CVZv69++Pq1ev4u7du1yrs+KaMGECVq5cicTERPz222+YPXu2VupV17Vr17hbxAcOHKhRHXw+Hzt37sTOnTvRtm1b+Pj4oHnz5qhSpQr09PS4Vty7du3ibonu378/unTpUqQuNzc3nDhxAsOGDUNmZib8/f2xfft2DB8+HF5eXrC3t0dWVhbi4uIQEhKCM2fOqPTjwokTJ2BnZ6ewjJGREUaOHKnyetvY2ODHH3/E4sWLkZGRgTZt2mDatGno1q0bKlWqhI8fP+Ls2bPYuXMnl1Bbs2aNxgm1/v37Y9++fYiLi8Pr168VJrPFEhISuC4gxLKyshAdHY2dO3dy3ZdUrlwZ8+bNU1jXw4cPsXfvXqXL7Ny5M2rUqFHk/4XjkKdmzZoanwv29vaYNGkSNm/ejDdv3uDw4cMyB/xjjHF3Fmh63BNCCCGEaIv5Vy1h/lVLjeef2HgiTr8+jc+5n3Eh5gJGNxyNxvbq9flPiEYYqXDS0tIYAJaWlqbWfDk5OSw6Oprl5OSUUGTaIRQKWWpqKhMKhboOpVjCwsIYAAaALVmyRGn5u3fvcuV9fHykpvH5fDZlyhTG4/G4MvIerq6uMuvXRh1PnjxhVatWlTvf2LFjWWBgIPc6JiZG3c3GGGPM29ubAWA1a9bUaP6aNWsyAMzb21tumenTp8tdjxo1arDo6GiuHj8/vyLzS+7fsLCwItOXLFnCTZfnxYsXzN3dXen+AMCWLVsmdz1lxaep5ORkZmxszACwffv2yS0nuX6y1r+wtWvXcuVXrVrFGPv/c/3y5cvctMDAQLVjljzmFMUyduxYBoC5ubmpvQyxP//8kxkZGam0zwCwkSNHsuzsbIV1Pnr0iLVt21al+ipXrsy2bNlS5Pro5+enckwAmLW1tdrrLhKJ2A8//KD0GmJoaMjWrVundv2SBAIBd62RdeyLSe57VR6urq4sMjJSZl2S57Sqj9OnT3Pzi69bms7PGGMxMTHcNFXO6/j4eO54bNiwocz3zStXrnB1RkREKK1T2yrKezohulJePr/z+Xx25swZxufzdR0KIaSElOR5LszLU6v8kWdHmPted+a+152NOT+GiUQircdE/hvUya/RvViElBNeXl5c36shISHcLZ1AQbP1bdu24dGjR5g2bRoaN24Ma2tr6Ovrw9raGk2bNsWECRNw4sQJqb72JGmjDjc3Nzx9+hRz585F3bp1YWxsDDs7O3Tq1AmHDx9GYGCg9jdMCdm8eTMOHz6MDh06wMrKCqampqhfvz5+/PFH3L9/v1Ruo61Xrx4ePnyIw4cPY/DgwahRowZMTU1hZGSEatWqoWPHjli0aBEiIyOluiwoSZUrV8agQYMAFLSy1ZYpU6Zwfa9u3LhRrc7EtSE3NxenTp0CUNCCWVPDhg3Dp0+fcOzYMfj7+6N169aoUqUKjIyMYGRkBDs7O7Rq1QozZszAvXv3cOjQIak+ZmVp0qQJwsPDcfnyZUyfPh1NmzaFg4MD14dw/fr14evri8OHD+Pdu3eYNm2aTm615vF42LhxIyIiIjB58mS4u7vD0tKSu4Y0b94cM2fOxJMnTzB79uxiLcvAwADjxo0DULzj0MTEBE5OTujduzf++OMPPHnyBJ6ensWKrSxxcnLi+kp+9uwZTp48WaSMePt5eXkVezA1QgghhBBty7pzF//26IHM8BsqzzO43mC4WLkAAO5/uo9/4v4poegI+X88xv43SgupMMSDXKWlpak9yFVMTAxcXV3LdCf54pHjraysqL82QnTgzp07aNWqFfT19fHvv/+iZs2aJbKc0jzXDx48iNGjR6Ny5cqIjY2VGkyIlE1xcXGoV68e8vLyEB4ejrZt2+o6pHInIyMDNWrUwJcvX3DkyBGMGDGi1GOg93RCiqe8fH4XCAQ4f/48evXqRX2wElJBlcR5nn3/Pt76jgIYg4G9PVyDzsKgUiWV5g2LC8P0sOkAgBqWNXCm/xkY6tP1h6hHnfwafZIlhBCilq+++gqDBg2CUCjE6tWrdR1OsYlEIqxatQoAMGfOHEqulhM1atSAv78/AGDFihU6jqZ82rp1K758+YJGjRopHEiPEEIIIUQXTJs2hfn/fkTPT0pC4uIlULWNYEfnjmhRpeDunLiMOBx7eazE4iQEoAQrIYQQDaxatQoGBgYIDAzEu3fvdB1OsRw/fhzPnj1DjRo1MH36dF2HQ9SwcOFCWFtb49KlS7h7966uwylXsrKysGHDBgDAunXrqPUoIYQQQsocnp4eqv38M/RtbAAAGaGhSDt9RrV5eTzMbvH/3VJtf7Qd6XzlA8ISoikDXQdACCGk/Klfvz727NmDf//9F3FxcXByctJ1SBoTCoVYsmQJOnfurLQ/VFK22Nra4sCBA4iMjERycrKuwylX3r59C39/f9ja2qJXr166DocQQgghRCbDKg6ounwZEqZ/DwD4uHIlzLxawMjZWem8bnZu6F2rN869OQdLQ0u8z3wPK1vVu1EkRB2UYCWEEKKR0aNH6zoErRg5cqSuQyDF0LdvX/Tt21fXYZQ7jRo1wtKlS3UdBiGEEEKIUlY+PsgcPAhpJ09BlJ2N93PnoeaB/eAZKE9pfd/sezSybYQRDUbASN+oFKIl/1WUYK3ABAIBBAKBWuUZYxCJRBCJRCUYWfGI+1wRx0oIqZjoXCek4qPznJDiEYlEYIxBIBBAX19f1+HIJf5Oos53E0JI+VLS53nluXORfTcCgvh45Dx4gE+//wHbb79ROp+dsR2+rvc1IAIEIroGEfWoczzzmKo9BJMyLyAgAAEBARAKhXj58iUOHz4MMzMzlec3MDBA1apV4ezsDCMj+mWHEEIIIYSQsozP5yM+Ph6JiYnIz8/XdTiEEFKiTN7Gwfn338ETicD09BA/dQpyVegqgBBNZWdnY+TIkUhLS4OVleLuJSjBWgGlp6fD2toaycnJSg8ASbm5uYiPj4eLiwtMTExKMMLiYYwhIyMDlpaW4PF4ug6HEFJC6FwnpOKj85yQ4snNzUVsbCycnZ3L9Od3gUCA0NBQdOvWDYaGhroOhxBSAkrrPE/Ztg2p238Hz8QEDiuWw7JHD7Xmf5P2BudizuE7j+/oswdRKj09HXZ2diolWKmLgArM0NBQrQubUCgEj8eDnp5emR5NWHwLoThWQkjFROc6IRUfneeEFI+enh54PJ7an/t1pbzESQjRXEmf51X8/cFSPsN27FgY13JVa95dUbuw9cFWCJkQHg4e6FKzSwlFSSoKdY5l+iRLCCGEEEIIIYQQQso8noEBqi1fpnZyFQBqW9eGkAkBABsiN0AgpD5ZifZQgpUQQgghhBBCCCGElFtMhX6oOzp3RIsqLQAAcRlxOPbyWEmHRf5DKMFKCCGEEEIIIYQQQsodUW4uElf+jHf+30HZEEM8Hg+zW8zmXv/+6Hek89NLOkTyH0EJVkIIIYQQQgghhBBS7sRPnoLUgweRefUqvhw9qrS8m50betfqDQD4kvcFux7vKukQyX8EJVgJIYQQQgghhBBCSLlTedxY7vnHX9Yi780bpfNMbzYdRnpGAICDzw4iITOhpMIj/yGUYCWEEEIIIYQQQggh5Y6FtzcqjfwaAMByc/F+9hwwPl/hPI4WjhjVaBQAQCASYPP9zSUeJ6n4KMFKCCGEEEIIIYQQQsolhzlzYFSrFgAgNzoaSQHblM4zsfFEVDKuBAC4EHMBUUlRJRojqfgowUoIIYQQQgghhBBCyiU9U1M4rlsLGBgAAFJ27ED2vXsK57E0ssSUplMAALWta0PIhCUeJ6nYDHQdACGEEEIIIYQQQgghmjJ1c4P99OlI2rABYAzv586D69kz0Le0lDvPkHpDYGFogZ6uPWGgR+kxUjzUgpUQQgghhBBCCCGElGuVJ4yHaYvmAADB+/f4uHKlwvKGeoboW7svJVeJVlCClRBCyoArV66Ax+OBx+PhypUrug6n2Pbu3cutT2xsrK7DUYu2Yh87dix4PB5cXFy0EtfixYvB4/HQp08frdRHSo+2j4XSsHTpUu48INI6duwIHo+Hjh076joUlfTu3Rs8Hg9LlizRet179uwBj8dD48aNwRjTev0VFWMMjRs3Bo/HQ2BgoK7DIYSQCoOnrw/HNb9Az8ICAMCPfwdRTo6OoyL/FZSmr8AEAgEEAoFa5RljEIlEEIlEJRhZ8Yg/wItjrSj4fD5OnjyJixcvIiIiAklJSUhPT4e1tTVq1qwJLy8vDBo0CJ07d4aeHv02oqqsrCwcPXoUISEhePjwIZKSkpCdnQ0bGxvUrl0brVq1wtChQ9GqVSudxil5LJf2OZibmwtHR0ekpaWhZs2a+Pfff9VKqvj6+uLo0aMAgHv37qFZs2ZaWR9dneva2heSyYbixh8XF4d169YBAH766acKde37L9DmsVBaSivm8v6eXloxX7x4Efv27UNERAQSExMhEolgb2+PZs2a4euvv8bQoUPlfjZYuHAhzp8/j/Xr12PChAlwcnLSSkyZmZlYsGABAGDRokVgjBVJsurr68ud38TEBHZ2dvDw8MCQIUMwcuRIGBjI/2pSq1YtvH37FjVr1sSbN2/UinXv3r2YMGGCWvNs2LAB33//vdT/OnfujKtXrxYpq6enB2tra7i6uqJt27aYNGkS3NzcFNY/f/58+Pr6YuHChRgyZAjMzc3Vik9MJBKBMQaBQKBwe+ua+DuJOt9NCCHlS1k5z3lVHGC/aCEECQmoNH48hAYGEKoYU/TnaOx+shtLWy2FpZH8rgXIf4c6xzMlWCuQgIAABAQEQCgs6Jw5JCQEZmZmKs9vYGCAqlWrIjMzE3w+v6TC1JqMjAxdh6A1wcHBWLRoEeLi4opMS0lJQUpKCu7fv48//vgDderUwcqVK9G9e3cdRFq+BAYGYvXq1UhKSioyLSkpCUlJSbh9+zY2bdoET09PrFq1Cl999ZUOIgWys7Olnqenp5fq8vv374/9+/fj7du3uHjxItq2bavSfBkZGTh79iwAoGHDhqhduzbS09ORm5vLlcnMzCyyPlOnTsWRI0fg7OyMx48fK11GaVIWu6rEb8YikajY+3Pp0qXIzc1Fly5dUL9+/VI/PsqDSpUKRoGdN28efvzxRx1HI02bx4I84eHh6Nu3L4CC95R27doVq768vDzueWkcb7p+T1+zZg1++eUXAEBqaqrCsvn5+dzfkt42eXl5+OabbxAUFFRk2rt37/Du3TsEBwfjt99+w5EjR2BtbV2kXKNGjdCpUyeEhYVh2bJl+PXXX7US26+//oqPHz+iQYMG8PHxUXtb5Obmcutw7tw5bNq0CceOHYODg4PM8uJktibnkeR1XZ15Ci9HvO9lxZaamorU1FTcv38f27Ztw8KFCzFjxgy59ffo0QN169bFq1evZCZzVcXn85GTk4Nr167Jja8sCQ0N1XUIhJASVibOcx4PcHICQkJUnuVu3l0E5RS83+YH56O7KX3fJtLf05WhBGsF4u/vD39/f67Vo4+PD6ysrFSePzc3F/Hx8bCwsICJiUkJRlo8jDFkZGTA0tKyQty6uHLlSqnb9rp27Yp+/fqhYcOGsLGxwefPn/Hy5UsEBwfj77//xuvXr7F69WoMHTpUh1GXbSKRCFOnTsXOnTsBFLQs6devH3r27Ik6derAysoKSUlJePLkCYKCghAeHs59IerWrZtOYpb8McTMzEytc1cbxo8fj/379wMATp8+jZ49e6o036lTp5Dzv9tuxo4dy8U9efJkTJ48We58hoaGAAr2jbx11dW5Lnn9s7Cw0HhfHDx4EAcPHix2PAkJCTh8+DAAYO7cuaV+bJQ3xsbGZW4baetYUETb1xBjY2PueUluz7Lynq7O+opbWRoYGJT4sTZ58mQuuerg4IA5c+agWbNmMDQ0RFRUFNatW4e3b9/i1q1b+Oabb3DhwgWZ9cyZMwdhYWE4ePAgli9fjmrVqhUrrpycHGzfvh0AMGPGDJmJXUktWrTA7t27pf6XmZmJJ0+eYNu2bXj06BEePXqECRMmyGwhCoBroavofUMeyev6ihUr0K9fP6XzVKtWrchyJFvYPnr0iHvO5/Px5s0bnD17FocPH4ZQKMTy5cvRqFEjhZ/XZsyYgalTpyIgIABz5szR6PN3bm4uTE1N0aFDhzL9+V0gECA0NBTdunXjPgMQQiqWsn6eM5EIPAV3gjbNbIqLf10EX8THHcEdzOs+D44WjqUYISmL1PlRlxKsFZihoaFaFzahUAgejwc9Pb0yfQu6uAWDONbyLDAwkEuuOjg44NixY/D29i5SzsfHB9999x2ePHmCGTNmICkpqdyve0latmwZl1ytW7cuTpw4gSZNmhQp17NnT8yZMwc3btzA9OnTdXpMSS5XF+dghw4d4OrqipiYGJw4cQIBAQFSCQd5Dh06BKDgNtBRo0apHLdkIkXePLo613W9Lwr7/fffIRAI4OjoiK5du+o8nrKuIrw3aELbx60q56g2lJX3dE3XtyRj/vjxI5eUrFSpEiIjI6Vu7+/QoQNGjx4NDw8PxMbGIiQkBPfv30eLFi2K1OXj4wMHBwd8+vQJO3fuxNKlS4sV2+HDh5GSkgJjY2MMGzZM6XYwNzeX+T7cpk0bjB49Gp6ennj+/DnCw8Nx584dtG7dWmF96m53yfJOTk4yY1FX4TpatGiBYcOGoVWrVpg+fTqAgmTu8OHD5dYxfPhwfP/990hKSsKxY8cwduxYtePQ09MDj8dT+3O/rpSXOAkhmiuL53nOo0f4sGgRHNevh0n9+jLL1KxUE76NfBH4JBB8ER/borbhlw6/lHKkpKxR51j+730DIaSMSEhIwHfffQeg4IvH1atXZSZXJbm7u+PSpUuYPXt2aYRYLkVGRmLl/0aLdHR0RHh4uNIvUm3btsXNmzcxcuTI0gixTOLxeBg9ejQAIC0tDcHBwUrneffuHTcgV5cuXeDoSL/waptIJMLevXsBACNGjPhPJg4J+a+6c+cOl4AeN26czL5TrayspG5Dv3Xrlsy69PX1uURfYGBgsQekEid+e/fuDRsbm2LVZWpqCn9/f+51REREserTNX9/f9SoUQMA8PTpUyQmJsota2trix49egBAkRa+hBBCtCPzxg3EjvRF3qvXeD97DkQS3SAVNqnxJNgY2wAAzsecx5PkJ6UUJakI6JsaITqyceNGrj+P5cuXo0GDBirNp6enh1GjRsmdHh4ejtGjR8PFxQUmJiawsbFBs2bNsGjRIpl9kYrJGsX+2LFj6NKlC+zt7WFqaor69etj7ty5+Pz5c5H53759y7WiWLhwodL1OHLkCLe88+fPK19xFa1evZr7Qvrbb7/J7cutMHErHHmCg4MxZMgQODk5wdjYGJUrV0br1q2xZs0aZGZmKq0/JycHq1atgoeHB8zNzVG5cmW0bdsWO3fuVGuQFKFQiH379qFPnz5wdHTkYmnXrh02bNjA3a6viTFjxnDPDxw4oLT8oUOHuNgl5wUKBhQR79/Y2Fju/+KRyfft2weg4LgRl5N8yJKbm4utW7eiS5cuqFq1KoyMjODg4ICuXbti9+7dCvuec3FxAY/H41oHRUZGYuzYsXB1dYWxsbHCW5Pz8vKwfv16eHp6wtraGlZWVvjqq6+wbds2rs9rWbQxcnx4eDjev38PABg8eLBK8zx58gTTpk1D48aNUalSJRgaGqJq1aro2rUr1q5diw8fPihcni6uH5JevnyJadOmwd3dHZaWljAyMoKjoyOaNm2K8ePH488//5TqH1S8b8WWLVtW5HiSbBVWOFaRSIQ9e/agU6dOqFKlCvT09KTKi0Qi/PPPP5g9ezbatm0LOzs7GBoawsbGBk2bNsXs2bNl9p8tSdmxII5H3KowIiICX3/9NXe9qV69OkaPHo1nz54VmTc2NhY8Hg+dOnXi/tepU6ci20CcqNe24p6X+vr6mDp1KgDgxYsXmDRpElxcXGBsbIwqVapg4MCBuH37ttI48vPzsWXLFrRs2RJWVlawsbFBixYtsHHjRvD5fG47Fd4W4mvVsmXLuP/JuiZJXscKS0hIwMyZM1GnTh2YmpqicuXK6N69u9zb9VUl2R9+rVq15JarXbu2zHkKE19D4uLicOPGDY3jevv2Le7cuSNVZ3G5urpyz/MUfPEtD/T09KQGuIqPj1dYXrwNb9y4obQsIYQQ9Zl5ecG4Th0AQN6rV0jasFFuWUsjS0zxmMK9Xn9vfbF/lCT/IYxUOGlpaQwAS0tLU2u+nJwcFh0dzXJyckooMu0QCoUsNTWVCYVCXYeiMZFIxOzs7BgAZm5uztLT04tdp1AoZP7+/gyA3Ie1tTULCQmROX9YWBhX7vLly2zUqFFy66lTpw778OFDkTratWvHADBXV1el8fbu3ZsBYPb29kwgEBR7/RljLDU1lenr6zMAzMXFRSvHSE5ODhs4cKDC7ero6MgePHggt44PHz6whg0byp2/e/fu7NKlS9zrsLAwmfW8ffuWeXh4KIylTp067MWLFxqvb5s2bRgAZmhoyJKTkxWWdXNzYwCYpaUly8rKkpoWGBjIxRQTE8P9f8mSJQrjFz8Ykz7XHz58yGrWrKlwHi8vL5aYmCgzVvG8fn5+bPv27czAwEDmMgvHfv/+fda8eXO5y+zQoQPLyMiQuUw/Pz8GgNWsWVOFLS/b0qVLuf2Rm5ursGx+fj6bMWMG4/F4CreTn59fkXnLwvWDMcaOHTvGjIyMlB4fUVFR3DzKjovC6ywZ64ULF1jXrl0VllflmDUzM2OnTp2Su2+UHQviepYsWcICAgJkHp/i5Vy9elVq3piYGJXOqcDAQLnxySK53vJo67z8+uuv2YkTJ5iZmZnMOvT19dnRo0flxpGWlsZatWolN4aWLVuyBw8eyNwWkue7oofkdczb25sBYN7e3iw8PJx7P5f1WLdunVrbXdLjx4+5embOnCm33ObNm7lywcHBcstlZWVx75ELFizQOK69e/dyy/v3338VlhWX8/b2VlguICCAKytvX4uPF02uqZL7Wd1zQZJ43ys6LxhjrH///lw5RZ8PGGPs+fPnXNkdO3aoHVN5+fzO5/PZmTNnGJ/P13UohJASUpbP85wXL9izxk1YdP0GLLp+A5Z544bcsnwhn/U+1Zu573Vn7nvd2eW3l0sxUlLWqJNfowRrBUQJ1rIvKiqK+zDdo0cPrdQ5Z84crk5XV1f2+++/s7t377KwsDA2Y8YMZmhoyAAwIyMj9vDhwyLzSyYdxEm2AQMGsFOnTrHIyEh2/vx5LikKgI0YMaJIHdu3b+em31DwppWcnMzF4+/vr5X1Z4yx4OBgbvmTJ0/WSp3Dhg3j6vTw8GD79+9nERER7NKlS2zcuHFcMsvW1pa9e/euyPwCgUAqQefj48NOnz7N7t27x06dOsUld7y8vLgyshKsycnJzNnZmQFgxsbG7LvvvmPHjx9nERERLCwsjM2fP59LTtSqVYt9+fJFo/X9/fffuTi2bt0qt9z9+/e5cmPHji0yXV6C9ePHjywqKor78uno6MiioqKKPBj7/3P9xYsXzNramgFgVlZWbP78+dw2vHTpEvP39+cSUl999ZXMD3XiL+aNGjVi+vr6zMXFhW3dupXdvn2bhYeHs9WrV8uMXbxfhg8fzs6fP8/u3bvHDh8+LLW/BgwYIHMbaSPB2qVLFwaAeXp6Ki07fvx4LqZq1aqxn3/+mYWFhbH79++zS5cusRUrVjAPDw+ZCdaycP1ITExk5ubmDABzcHBgy5cvZyEhIez+/fvsxo0bbN++fWz8+PHM1tZWKsH64sULqWvqlClTihxPkuemZKxNmjRhAFi/fv2kYpVM8CxcuJBVq1aNTZ06lR04cIDduHGDRUZGsjNnzrC5c+cyCwsLBoCZmJiw6OhomftG1QRrq1atmJ6eHvPw8GB79uxhERER7Nq1a2zGjBlMT0+PAWA1atRgeXl53Lx8Pp9FRUWxPXv2cPXs2bOnyDZITU2VuWx5lCVYX716pbXz0sPDg5mYmDBXV1fuvLx16xZbunQpMzEx4Zbx6dMnmbH07NmTi7Vt27bs6NGj7N69e+zChQvM19eXi0FWgi01NZVFRUWxKVOmcNNlXZMk4xcn2erVq8fs7OyYg4MDW7NmDQsPD2d3795lGzZsYDY2NgwAMzAwYE+ePFFr20sSn0+2trYsISGhyPT09HTm6urKXfsljw1ZxMd827ZtNY5pwoQJDACrXLmy0rKqJFizs7NZo0aNGFDwo7O8H/fKU4K1Xr16XLnPnz8rLCsSibjjxdfXV+2Yysvn97KceCGEaEdZP89T9u3jEqwv23dg+Qo+G/399m8uwdrnVB/GF5bNdSIljxKs/3GUYC37Dh48yH3wXrhwYbHre/z4Mffl293dXeYX6QsXLnBlWrZsWWS6ZNIBAFu5cmWRMiKRiPn4+HBfGgt/2VU1cSqZiL1586b6KyzHypUruXp37txZ7Pr++usvrr4uXbrI/OK6Y8cOrsywYcOKTN+6dSs3/ZtvvpG5HMnEmLwE68iRI7kvlm/evJFZz/3797kElaatk1JTU5mxsTGX7JFnxowZXLz//PNPkenyEqxiqiQfxee6OMHQrFkzlpSUJLOs5PEtqwWQZCu7xo0bK0w2FW7RtmrVqiJlBAIB6969O1fm3LlzGq2jIiKRiNufEyZMUFj27NmzXCytW7dWuH5xcXFSr8vK9WP37t1SCS55srOzWXZ2dpH/i+ddsmSJ3Hllxbpo0SKF5WNiYhR+UYiPj2fVq1dnANioUaNkllE1wQqA9erVS+a1RvL6Jqu1rOR6yWsFrw5lCVZtn5fNmzeX+blF8v1yw4YNRaafOXOGmz5o0CCZnw3Wr18vtY1lJdhUabErJplkq1mzpswf165fv879ADd9+nSldcrz4sULLoFapUoV9uuvv7KwsDB2/fp1tn37dm6anZ0du3XrltL6xo0bx4CC1tAikUijmMR3ZHTp0kVpWfF2atGiRZGk9Z07d9iuXbtYs2bNGADG4/FYQECA3Lq0lWBduXKlzCS6rB/6ClMlwXry5Empzw6q6NSpEwPAGjRooPa6lZfP72U98UIIKb6yfp6LhEL2dtx4LskaP/17ue+FIpGI+V3w45Ksh6IPlXK0pKygBOt/HCVYyz7J2/k2b95c7PokW97cvn1bbrmJEydy5e7evSs1TfLLefPmzeW+2Vy8eJErd/bs2SLT+/btywDFt/6LuxKoVauWGmupnGTST1Zs6hK3ijI0NCySlJIkboVqYGDA3r9/LzVN3CqnSpUqRW6jF8vIyGD29vZykyMxMTHcbZ2Kbv9kjLG5c+cyoKBlqKaGDBnCxfLq1asi0/Pz81nVqlUZUNCaTtaxoq0E67lz57h6Hj9+rDBucWvjNm3aFJkmmci5du2awnokY2/SpInccyE+Pp77QaF3794araMiKSkpXBzz589XWLZ169Zc0kRWKzdFysr14+eff2YAWKVKldSKX0yTBGu9evVYfn6+RsuTtGnTJgYUtLKUte6qJlhNTEzYx48fZZZJT0/nuk+YMWNGkemlmWC9du2a1s9LebdRi0Qi5ujoyACwgQMHFpneo0cPBoCZmprKbeEqEomYp6dniSRYg4KC5JYTd1vQrFkzpXUqkpyczFasWMG1lpZ8GBoastmzZ7P4+HiV6po3bx43b+H3K1VZWloyoKBrB2UKxyvv4ePjI/PHOknaSrCq+pBFXoI1Ly+PPXv2jK1atYq7m8TMzEzhNVXS8OHDGVBwh4q6ie/y8vm9rCdeCCHFVx7Oc37iR/ai5VdckjX11Gm5ZaOSopj7Xnc2IngEu//xfukFScoUdfJrBiBEQw//jsPDv5V3xm9fwxK9p0qP4n5u22MkxWUonbdpV2c07VqDe83PzcfhpbchEjHo6fEAyB+YpteUxnCoacW9jn2cjCuHXyhdpqGxPnyXtVJarjgyMv5/3c3NzYtd399//w0AcHNzw1dffSW33KRJk7Br1y5uHi8vL5nlRo4cKXfQn+bNm3PP37x5U2S6r68vgoODkZSUhNDQUPTs2VNquuTgGiNHjlSwVurT5nbNz8/H1atXAQA+Pj5wdnaWW3bSpEn4+++/kZ+fjytXruDrr78GAHz48AHR0dEAgGHDhsHMzEzm/BYWFhg2bBgCAgJkTj937hyEQiHMzMyKbM/COnTogLVr1+L9+/eIi4vjRjJWh5+fH06cOAGgYLArycFfACA0NJQbFXnUqFEKB4gqLvEgMfXr10fjxo0Vlu3QoQOOHTuGiIgI5Ofnw8Cg6Fucs7Mz2rdvr/Ly/fz85K6fk5MTfHx8cO7cOVy5cgVCoRD6+voq162M5KBSlSpVklsuJSWFGwRo+PDhcHR0VGs5ZeX6Ua1aNQBAamoqzp49i/79+6u+EhoaPny42vssPT0dKSkpyM7O5gY9EJ/b6enpiImJUTggkSLdunWTOzCfpaUl6tati6dPn8q89pamoKAgANo7Lxs1aoQmTZrImLtgwKlmzZrh/fv3RdZb8jrdo0cP2Nvby61j9OjRuH//vtJ1U4eNjQ169+4td3rz5s1x+/btYu+v4OBgHDp0SOaAigKBAMeOHYO9vT3mzJmj9Hpsa2vLPU9MTOTOO1Xl5eVx77WKrkvqCgsLg7m5OerUqaPw/bYsUbStPT09sWXLFoXXVEni/ZKXl4cvX75oddsSQgj5f4ZVHFB1xXIkTP8eAPBxxQqYtWgOIxnvPe527tjfcz887D2gx6Px4YlydJQQjfFz8pH1JU/pIyej6Ii2ORl8lebl5xQafZgBWV/4yEkXIOuL4jpEQiY1a75ApNIys9JKfvRaS0tL7nlWVlax6srLy8OrV68AQOkH+WbNmsHQ0BBAwUjj8jRo0EDuNMkvZ5IJTbF+/fpx63fo0KEi048cOcIlJXx9fRXGqy5tbtc3b94gOzsbgPLtKjldcrtGRUVxz+Ulo8Ratmwpd9q9e/cAANnZ2TAwMJA5wrX40adPH24+cRJUXT169OCSPLL24YEDB7jno0eP1mgZqnr48CGAgtHFFa03j8fDd999B6Ag4SBvpHp5SRx5VN1vWVlZWk96Sa6Doi/bDx8+5M4pdZLHQNm6fvTr1w82NjYAgIEDB6Jz587YuHEjIiMjIRQKFcamKVWPh7dv32LatGlwcXGBtbU1atWqBXd3dzRu3BiNGzfGN998w5VNTk7WOB5F2w74/+0n69pbmsTXJG2dl/Xq1VO4PHnr/e+//yInJweAdPJelhYtWihfMTXVrVsXenryP0prY3/NmjUL48aNw/PnzzFgwADcuHEDmZmZyMnJwf379zFu3DjExcVh3rx5GDJkiNJzRfJaosn7pKrXpcK8vb3BCu6c4x58Ph8xMTEICAiAtbU1Tp8+jVatWuH58+dqx6WOwMDAIrHIemjKyMgIEyZMQNu2bVWep7j7hRBCiOqsfHxgPWgQAIAJBMhV8Lm2mUMzSq4SlVELVqIxI1MDmNsYKy1namkk83+qzGtkWugQ5QHmNkYqtWDV05eeZmCop9IyDY211wJNnsqVK3PPP378WKy6UlNTuefyWj6JGRoaonLlykhMTJT7RReA3JaWAKS+TMr6ImdqaoqBAwdi//79OHPmDLKzs6XqEyfsPD09lSYT1KXN7Sq5fZRt16pVq8qcT506qlSpInfap0+fFM4rjzhBrC4DAwN8/fXX2Lx5M/7991/cvHkTbdq0AQBkZmbizJkzAAqSj9reh4VpmqySt+7qtgpSZ78pOqc0YWJiwj0XJ5FkkdxG6rZGK0vXj8qVKyMoKAhff/01EhISEBYWhrCwMACAlZUVunTpgvHjx0v9iFBcqhwPFy5cwJAhQ1Q+nxTtK2UUbTvg/7dfSSWcVaXta5KpqanC+eStt+TxK6/1qqrTNaHq/hKJRBrVf+7cOWzYsAEAMHbsWAQGBkpNb9asGfbs2QMnJyesWLECp06dwrZt2zBt2jS5dUoen+IfTNSh6nVJFYaGhnBxccHUqVPh7e3NtVSeOHEiwsPDi1V3aZD8ETU1NRWPHz/Gxo0b8e+//8Lf3x9ZWVmYM2eOSnUVd78QQghRT5UFCyD88gX230+HSf36Ks/HGCvRu/dI+UYJVqKxpl1rSN2+r47CXQaoysjEAGNWtUF6ejqsrKwUthwpzKWJHcY2sdNoudrm4eHBPdfmLYtl5WLv6+uL/fv3IysrC2fPnuVumX/69Cn3hUTbrVeBsr1di1OHOKlgZ2fHJZxU4erqqvEyx4wZg82bNwMoaLEqTrCePHmSS5KMGTNG4/pVJV53Dw8PHDx4UOX5qlevLvP/6t4OrstzSjIhpO3krSxl4frRvn17vH79GidPnsT58+dx7do1vHv3Dunp6Th9+jROnz6N7t2749SpU0qTW6pQdjwkJydj5MiRyM7OhoWFBWbPno3u3bujdu3asLa2hpFRwQ+I//zzD7p06QIAxWr5Vl5o+7wksom75ODxeFi5cqXccgsWLMDGjRuRmZmJPXv2KEywSl5LxC3G1WFjYwMDAwPk5+dr9brk5uaGXr164ezZs7hx4wZevnyptGWzrrm7u0u9bt++PcaMGYN27drh8ePHWLBgATp27Kj0TghAer9YW1trPVZCCCHS9C3M4bxNdvds8txLvIcNkRuw4KsFcLdzVz4D+c+hBCshOuDm5gY7OzskJyfj+vXrXMJYE5ItsJS12szPz0dKSgoA6Vt1ta1Lly6oUqUKPn78iEOHDnEJVnHrVT09PYwYMULry23Xrh309fUhFApx4cIFiEQitZLwkiS3j7LtKnkrvuR86uwbRdPFLXMzMjLQsGFDrfbzKY+npyfc3Nzw9OlTHDt2DJs3b4aRkRHXPYChoSG3X0uSeBtmZmYW+TJbGj5+/KjwS77kftP2OSWZYJVsqVeYnd3//3D04cMHtZZRFq8fJiYm8PX15X6EiYmJwblz5/Dbb7/h5cuXuHTpEhYuXIiNGzeWWAxiJ06cwJcvXwAAp0+fRteuXWWWK40EeFkivibp6rwUkzx+JfsslkXZ9LLo2bNnAApalytKTpuYmMDNzQ137txRenu95LVEk75OeTwe7OzskJiYqPC6pIkGDRrg7NmzAApah5b1BKsslpaW2L9/Pzw9PZGfn49Zs2bh2rVrSucTb0sHBwepVsKEEEJKl7wWqlfjr+K7fwq6PVoXsQ57e+wtE40TSNlCnUkQogM8Hg9+fn4ACvraErdS0YSxsTHq1q0LALhz547Csg8ePIBAIABQtOWFNunr63MJ1JCQEKSkpIAxhiNHjgAAOnXqpPZAPKqwsbHBgAEDABT0mSi+lV0TtWrV4lrIKduud+/e5Z5LblfJwV8iIiIU1qFoerNmzQAU9Jcp7vuwNIhbqH7+/Bnnz5/nbtsGgF69ekl1yaAuVT+QiPvIfPPmjcZ9yhaHqvvNzMxM44GN5JE8t1++fCm3XLNmzbjtqcoXeXnLKCvXj8JcXV3x3XffISIiAk5OTgCAY8eOlcqynz59CqAgoSwvuQqgVM9LeUrzQ774mqSr81Ksdu3aXDIqMjJSYVll+6gsfkkSDwiWn5+vpCS4c1PWIGKSxNcSV1dXjVuBi9/bFF2XNCG5nqqsc1nl4eHBDeJ5/fp1XLx4Uek84m3p5uZWorERQgiRjfH5+PTrr/i4YoXM6W2qt0FNq5oAgPuf7uOf+H9KMzxSTlCClRAdmTFjBvflZvHixSoP6iASiYoMPCT+4v/06VOpZF9hkolcRckCbRC3PhOPcHzz5k3ExsZKTSsJ8+fP51qtTps2TeW+AvPy8qSSNgYGBvD29gYAhIaG4t27d3LnFW9XAwMDdOzYkfu/o6MjGjZsCAA4fvy43P7qsrKyFCaM+vbty33537Rpk0rrow2jRo3ituWBAwdw6NAhri/B4nYPIE6K5OUpHlSuZ8+eAAp+TRZ3WVCaDhw4IPeW74SEBISEhAAAOnbsWCIti8WDVilK9Nra2nJdOBw7dgzv379Xaxll8fohi5WVFXerray+eVU9ptQhTvLk5ubK7UczOztbauA3XZFs9abNbSBLv379AOjuvBQzMDBAhw4dAAAXL16U20qVMaZ0H5Xm9lOVuJuXlJQUrjWrLJ8/f+YGnlPWNYw40azq6PayiK9LL1680OqAa5JJcE1a15YlCxcu5N4/FXXvAADp6el48eIFgOLtF0IIIZphjOHt+PFI2bkLqYePIENGl2yGeoaY0XwG93pj5EYIRILSDJOUA5RgJURHqlevjq1btwIoSLB5e3vj6tWrCueJjo5Gjx49sG7dOqn/T5kyhfsg/8033yA9Pb3IvCEhIdi9ezeAgpHPVekTrDi8vLy4lnGHDh3C4cOHARR8iR08eLDMeVxcXLiRpzXVvHlzLFq0CADw/v17tGvXTmogCllu3bqFtm3bcjGK+fv7AwD4fD4mTJjAtRCStGfPHi7JNmjQoCKDDE2ZMgVAQTcCs2bNkrn8GTNmKEwE169fH0OHDgUAHD16lBv0RJ6YmBiutXBxODo6cv1K/vXXX9i5cyeAgoRecQcaEm+nT58+KfyC3rlzZ7Rs2RIAsG7dOqUtF6OiohAcHFys2CQ9fPiwyPkGFCTeJk2aBD6fD+D/97O2iRMZycnJiImJkVtu3rx5AAqSfUOHDkVaWprcsoV/LCgr149Lly4p7OIgLS2NSwDLSiKJj6l///1XazGJr2HZ2dkyjz2hUIiJEyeqndQuCZLXHm1uA1l8fHx0el5K+vbbbwEUDBI0efJkmYnwDRs2KO2XuzS3n6r69u3LPf/hhx+4640kkUiE6dOnc9MUXZvfvHnD/Tjh4+OjcVzi65JIJNJa6+1z585xn4Hs7Oy446u8atCgAQb9b4TqGzduKOw//d69e9wPecXZL4QQQjTD4/Fg3bcf9/rDwkXIl/FjfmfnzvB08AQAvE1/i+MvjpdajKR8oD5YCdGhcePG4d27d1i8eDE+ffqEjh07wsfHB/3790fDhg1hY2ODz58/4+XLlzh37hwuXrwIoVAoNZgTUHC73qxZs7Bu3To8evQInp6emDdvHpo1a4asrCwEBwdjy5YtEAqFMDIywh9//FEq6+fr64ulS5fi5s2bXOuaPn36aNzfrKqWLFmCxMRE7NixA69evULTpk0xYMAA9O7dG3Xq1IGlpSWSkpLw5MkTBAUFcV/qxLcfi/Xu3RtDhw7F8ePHERISglatWmHmzJlo0KABUlNTcfToUezZswdAQdJRVuJzypQpCAwMxIMHD7B9+3bExMRg8uTJcHZ2Rnx8PLZt24aQkBC0aNFC4RfV7du34969e3jz5g1mzZqFs2fPYsyYMXBzc4OxsTFSUlLw6NEjXLx4Ef/88w8GDhyolT5Sx4wZg9DQUPD5fLx+/RoAMHz4cG5wH02JW1yKRCJMnjwZ06ZNk+pLtE6dOtzzgwcPolWrVvj8+TOGDx+OgwcPYvjw4ahbty709fXx6dMnPHjwAMHBwbh9+zZmzZollZgojhYtWmDevHl4+PAhxowZAwcHB7x69QobNmzgkn19+/bV6sj2knr16gVDQ0MIBAJcvnwZEydOlFmub9++mDBhAnbv3o2bN2+iUaNG+O6779C2bVtYWVkhOTkZ9+7dw59//gkPDw/s3buXm7esXD+OHDmCvn37olu3bvDx8YG7uztsbW2RkZGBJ0+eYOvWrUhISAAATJ48ucj8bdq0QUxMDIKCgvDHH3+gbdu2XKtEKysrODg4qB3TsGHDsGDBAuTl5WHcuHF4+PAhunXrBmtrazx9+hS//fYbIiMj0bZtW9y4caN4G6CYatSoAScnJ7x79w7r16+Hk5MT6tevz7WsrlKlCiwtLbW2vMOHD6Nly5Y6OS8lDRo0CD4+PggJCcGpU6fQoUMHTJ8+HXXq1EFSUhIOHjyIgwcPomXLltw5K+tHPPE1CSj40WvhwoWoVq0aV9bFxUXp7ffaNnbsWGzatAnPnj3j3iemTZsGDw8P6OvrIzo6Gtu3b8etW7cAFOzjmTNnyq3v8uXLAApa/hbnmtWmTRvY29sjKSkJly9fRqdOnZTOk5WVxX0OEBMIBEhISMC5c+ekWsivXr1a4bbOzMyUuobJU6NGDXTu3LnI/xMSEorEIouVlRVq1NBsMFegYPCxEydOAChoxSpvO4n3i52dHdq1a6fx8gghhGjOZthQZF65gsywMAg/f8aHhYvg9Pt2qc8MPB4Pc7zm4OtzBd+xtj/ajr61+8LSSHufr0g5x0iFk5aWxgCw5ORkxufzVX6kp6ezp0+fsqysLCYUCsvsIz8/n6WmprL8/Hydx6Ktx/Hjx5mLiwsDoPTh5ubGLly4UKQOgUDApkyZonBea2trmfMKhUJ2+fJlrtzly5cVxisut3jxYoXlXrx4USSGkydPyi1fs2ZNrpw2tmtAQACzt7dXabu2adOG3b59u0gdWVlZbMCAAQrndXR0ZJGRkXLjiI+PZ/Xr15c7f7du3diFCxeUbv+EhATWvn17ldZn7NixWtmGGRkZzMLCQqruGzduKJ1v9+7dXPl///1X5vHaqlUrufELhdLn+rNnz5i7u7tK67506VK5x9aYMWPUiv3evXusWbNmcpfVtm1b9uXLF5n1jBkzhgFgNWvWLNY+GDRoEAPAOnXqpLAcn89n/v7+jMfjKdw+srZBWbh+iLeXsse3337LBAJBkXojIyOZsbGx0nVWJ1ahUMh27drF9PT05MYzbNgwFhISorBOZceCqtdUb29vBoB5e3vLnL5161a5ce7evVut427x4sVKr8faOi+//vprhe/pyrZfSkoKa9mypdxlN2vWjN29e5d7ffjwYZn1DB06VG4dktcxZftBnW2o7PHmzRvm4eGhdPu6uroqfB8SCoWsY8eODADr1auXxvGIHzNnzmQAWK1atVQ631V5GBoasjVr1sitS/IzgiqPfv36cfNKXtc1mb/wvld1n/bs2ZMrL++909XVlQFgU6dO1WhfZGVlsadPn7L09HS1PveX9iMrK4udOXOGZWVl6TwWetCDHiXzKO/nefaHD+xF6zYsun4DFl2/AUs6eFBmuTlhc5j7Xnfmvtedrb+7Xudx06NkH8nJyQwAS0tLU5qLoxasFUhAQAACAgIgFAoBFNzSqc4ABgYGBqhatSoyMzNl3oZW1miz3y9d69q1K+7cuYOzZ8/i77//xoMHD5CUlITMzExYWlqiRo0aaNGiBfr374927dqBx+PJvI131apV6NOnD/bu3Ytbt24hKSkJRkZGcHFxQbdu3TBlyhTY2dnJnDc7O1vquawyheXl5Sks5+DggObNm3ODj9jY2KBdu3Zy55G8tVOV5SszcuRI9OvXD6dOncI///yDx48fIzk5Gbm5ubC2toarqytatmyJQYMGwdPTU+5yAwMDMWzYMBw5cgT37t1DSkoKzMzMUKdOHfTq1QuTJk2ChYWF3JgtLCwQFhaGgIAAnDp1CrGxsTAyMkK9evUwfPhwjBs3Djdv3uTKy9v+ZmZmCAoKwqVLl3Dy5ElERETg06dPEAgEsLa2Rq1atdCyZUv06NEDbdu21co2BAr6WxR3n1C7dm00atRIad25ubnc88zMTJnljx07hi1btuDixYuIjY1FVlYWd5ukZPmMjAxUrVoVV65cwenTpxEcHIz79+8jJSUFQqEQtra2qFOnDlq1aoU+ffrAw8OjyPLEx5ZAIFArdkNDQ5w/fx7bt2/H6dOnERsbC8YYt+8mTJgAxpjMOsVdSohEomLtC19fX5w6dQpXr17FixcvinRDIWnlypUYOnQoAgMDcePGDXz48AF8Ph+2trZwc3NDly5dMHz48DJ5/Vi2bBnatm2L69ev4+nTp/j48SOSk5Ohr6+P6tWrw8vLC6NHj0br1q2RmZlZpL5atWohJCQEW7ZswZ07d5CUlMT1pSm539WNdfDgwXBycsJvv/2GO3fuIC0tDZUrV4abmxt8fX0xcOBAhIeHK6xT1WNB2TVV3Cdsfn6+zHK+vr6wsrLC3r17ERUVhdTUVKl+ZNU5DiX7IZU3n7bOS0Dxe7qy7aenp4e//voLu3btwrFjx/D69WvweDy4uLhg4MCBmDJlCl69esWVNzQ0lFlPQEAA3N3dERQUhFevXiEzM5OLUfI6pmw/iKmyDZWpVKkSQkNDcfLkSQQFBeHRo0fcwJGVKlWCm5sbevXqhREjRsDc3Fzuct6/f88Ngufn51fs94cRI0Zgw4YNePPmDS5fvqxRtyH6+vqwsrJCrVq10K5dO4wZMwa1atVS6TOCKiT3j+R1XZP5Jf8npso2/P7773HhwgUAwNKlS4t0p3H37l2u+5dRo0ZptF/4fD5ycnJw7dq1cjE4WGhoqK5DIISUsPJ8npv364fq/7tL4uOaX3A3JwcCe3upMo2EjRCKUOQjHwejD6Lyu8qopFdJB9GS0iD53UEZHmNyRu8g5VZ6ejqsra2RnJys1q3Yubm5iI+Ph4uLi9RgD2UNYwwZGRmwtLQsk6P+EkK0g871AowxeHh44OnTp1ixYgUWLFig65AI0ZrSOs8PHjwIPz8/AAUjtteuXbvEllUW/fzzz1i8eDEaNmyIqKgorWzr3r174+LFi5gwYQJ27NihhSj/eyZNmoQ9e/bAx8eHS8SqKzc3F7GxsXB2di7Tn98FAgFC/6+9+46K4nr7AP5dYOlFQCyIAvYCitgrlsQWe+8aYtRojC0aNUVNohJbEhWNJfZojBqNJfZgjVGJJSp2AUVQpPe2O+8fvDu/XdgGLCzi93MO58zu3HvnmdmZ3eXZO/eeOoV3330XUqnU2OEQUTEoK9d59LffImlP7g9iFvXrw23nDkjy7M+PN37EtnvbAADd3btjUZtFJR4nlYykpCSUL18eiYmJOvNr7MFahkml0gK9sclkMkgkEpiYmIgTnpRGih4MiliJqGzitf4/S5YsQe/evfHjjz9i+vTpsLGxMXZIRAZRUtf5nj17AAAuLi6oWbPmW/WjTUpKCn788UcAuWOUK8blLaqAgACcPHkSO3bswPz581G1alWDtPu2ePbsGXbs2AEA+Prrrwt9/puYmEAikRT4e7+xvClxElHhvenXeeU5c5Bx9RqyQkORGRKChA0bUGHaNJUy433G44+nf6CWYy2M9R77Ru8vaVeQ1/bt/o+ViIjoDdCrVy+0a9cOMTExCAwMNHY4RKXKixcvkJ6ernH9pk2b8OeffwLInbjvbUquArlDH8TGxqJ58+YYPHiwwdpt1KgRhg8fjqysLCxZssRg7b4tlixZguzsbAwaNAgtWrQwdjhERPT/TKys4Lp0KfD/Ey6m/n0Zwv8PV6Rgb26P33r+hp+7/Iz6zvWNESaVQuzBSkRE9AYIDAzE/v37YWtra+xQiEqVU6dOYfbs2Rg6dCg6dOgAd3d3yOVyPHnyBHv27MHBgwcBABUrVsTcuXONG6wR2NnZYf78+ejfv7/Bk8uLFy9GjRo1YGlpCUEQ3rrkdWEJggB3d3fMnz8f/v7+xg6HiIjysPL2gsvUTyBPSYXL5En5hggAgMq2mudFoLcTE6xERERvAG9vb3h7exs7DKJS6fXr11i9ejVWr16tdn3lypVx9OhRODs7l3Bkxjdp0qRia7tq1apYsGBBsbVfVkkkEsyZM8fYYRARkRblP/ywQOVz5LkTDZqZMM32tuIQAURERET0xurZsyfWrVuHvn37onbt2nB0dISZmRnKly+Pdu3aYenSpXjw4AEaN25s7FCJiIiojBEEARciLmDQ4UH47cFvxg6HjIipdSIiIiJ6Y5UvXx4TJ07ExIkTjR0KERERlVEZ9+/j5bffosqKFZBWrCg+H5oYiklncu8WWXdrHXrV6AU7cztjhUlGxB6sREREREREREREaiQHBSFs0GCkB/+LqLlzIcjl4rrq5aqju2d3AEBCZgI23d5krDDJyJhgJSIiIiIiIiIiUsO6cWOYOjkBAFL/voz4HTtU1k/1nQqpSe5EWDtDdiIyJbLEYyTjY4KViIiIiIiIiIhIDdNy5eAasER8HL1iJTIePBQfV7GtgpH1RgIAsuRZWHVjVYnHSMbHBCsREREREREREZEGNq1awWnsWACAkJWFyFmzIM/MFNePazgO5SzKAQCOPj2KuzF3jRAlGRMTrERERERERERERFq4TJ8Gi9q1AQCZDx/i9fc/iOvsze0xsdH/JtxcFrwMgiCUdIhkREywEhERERERERERaWFiYQHXZcsgkeaOtxq3dStSL18W1w+uPRju9u4AgH9f/Yug50FGiZOMgwlWIiIiIiIiIiIiHSzr1IbLzBni48g5cyFLSAAASE2lmO47XVy36voq9mJ9izDBSkREREREREREpAen0aNh3aolACAnJgap166J6zpV6wTfCr7o4NYBKzushEQiMVaYVMLMjB0AERERERERERHRm0BiYgLXJUsQMXUqKn3+OawaNvzfOokE695ZB2uptREjJGNggpWIiIiIiIiIiEhP0kqV4PHrr2p7qDK5+nbiEAFEREREREREREQFoO/t/1myLCRmJhZzNGRsTLASEREREREREREVkpCTg9dr1yJ206b/PScIOBF2An0O9sGSq0uMGB2VBA4RQEREREREREREVAhCTg7Cx4xF+r//AmZmsG7RElbeXkjNTsW3/3yLhMwERKREYFS9UWhQvoGxw6Viwh6sRKRVhw4dIJFI0KFDB2OHUqZJJBJIJBIsWLDA2KEAAGJjY+Hk5ASJRIJrSrNiUukXFhYmnk9bt241djh6K23XQGlx9uxZ8dicPXvW2OHodO3aNUgkEjg5OSEuLs6gbWdlZaFWrVqQSCTYt2+fQdsu6/bu3QuJRILatWsjOzvb2OEQERGVKRIzM1g3a5r7ICcHkbNmQZ6WBltzW0xsNFEstzx4OQRBMFKUVNyYYCUqYcrJj6L8kW5yuRyHDh3ChAkT4O3tjQoVKkAqlcLJyQne3t7w9/fHwYMH+c+mGl999RXi4+PRo0cPNGvWzNjhEJERyeVytGrVSq/PoGbNmqFr166Ij483eLL8xx9/xOPHj+Hl5YUBAwbkW79gwQKNn5mmpqYoV64cfH19MWPGDDx48EDrtrZu3VqkHyo8PDwK9Jlerly5fG0oJ9fz/llbW6Nq1aro2bMnNm/ejMzMTK3xDBgwAPXr18ejR4+wevXqAu8PERERaecyeTIsvbwAAFlhYXi1bBkAYHDtwXC3dwcABL8KxtnnZ40UIRU3JliJqEw6d+4cfHx80KdPH2zYsAF37tzB69evkZOTg/j4eNy5cwdbtmxBv3794OHh8Ub19Ctu4eHh2LhxI4DcRCvlp5zIeVuxd3vRvEk9dteuXYt//vlH7/KK943169fj+fPnBokhOTkZ3333HQDgiy++KPC1J5fLkZiYiBs3buD777+Ht7c3AgMDDRKbMaSnpyMiIgJHjx7FBx98gCZNmiAsLExjeRMTE3z++ecAgICAAKSmppZQpERERG8HiVQK16VLIbG0BAAk7P4VyWfPQmoqxXTf6WK5lf+uRLacHXzKIo7BSlTCqlSpgtu3b2tc7+3tDQBo2rQptmzZUlJhlSlbtmzBhAkTxJ6pLVu2RP/+/eHj4wNnZ2ckJibiyZMn+PPPP3H06FFERkZi2rRpGDt2rHEDLyW+++47ZGdno02bNmjRooWxw6EC8vDw4K1HZDAvXrzAvHnzIJFI4OzsjJiYGJ11WrdujZYtW+Kff/7BsmXLsGrVqiLHsW7dOsTGxqJatWoYNGiQzvKbN29W6X0vk8kQFRWFY8eOYd26dcjOzsbHH3+MOnXq4J133ilyfJq4urrixIkTOsuZmppqXf/RRx9h0qRJ4uPo6GjcuXMHy5YtQ0REBO7evYvevXvjxo0bGtsaMmQIZs+ejRcvXmD9+vWYMWNGwXaGiIiItLKo7omKcz7DywULAQBRn38Bq0N/oFO1TvCt4Ivr0dcRlhSGfQ/3YVjdYUaOlgyNCVaiEiaVSuH1/7cOaGNjY6NXOVJ15swZjBs3DnK5HDY2Nti6dSsGDhyYr1zHjh0xbtw4hIWFYc6cOTh+/LgRoi19EhISsH37dgDAyJEjjRwNERnbxx9/jOTkZPj7++PJkyc4d+6cXvWGDx+Of/75B1u3bsW3334Le3v7Qscgk8mwZs0aAMCwYcNgYqL7BixPT898n6GNGjVCt27d4OPjA39/fwDAsmXLijXBqu9nvi4VKlTI106nTp3w/vvvo2HDhggLC8Pt27dx4MABtZ95QG4Sd8iQIVi5ciXWrFmDadOm6XUsiYiISH/lhgxBStBZpJw7B1lsLKK++BJuawMxs+lMjPhzBABg3c116Fm9J+zM7YwcLRkSE6xlWHZ2doHGlszOzoYgCJDL5ZDL5cUYWdEoemYpYi3LStv+lbZ48kpLS8PIkSMhl8thYmKCw4cPw8/PT2vc1apVw65du7B9+/ZSsX/GPq937dqF1NRUSKVSDBgwwKixlOZrXbmHaGmLzRgMeQxK4+tdnHTtr/K6kv58/v3333Hw4EGUL18eAQEBKj1HdcUxaNAgTJ8+HcnJydizZw8++OADteX0uc5PnDghDjUwbNgwjeXyXpeayo0ZMwazZ89GTEwMrl27pracIY97Yesq19N0fGxsbDBv3jyMHz8eAHDq1Cn0799fY5vDhg3DypUrERoaijNnzqBz586Fio1KD7lcDkEQkJ2drbMntDEp/ifhuPdEZRev8/8pv2AB0gf0hywuHilBQYjd/SvqDRqIru5dcSL8BOIz47Hx1kZM8Zli7FBJh4Kcz0ywliGBgYEIDAyETCYDAJw8eRLW1tZ61zczM0OlSpWQkpKCrKys4grTYJKTk40dQrHKyclBUlKSynOOjo4AgM8++wxz5szRWLdnz564dOkS2rRpgyNHjqisu3jxInr16gUAOHz4MFq3bo1du3Zhz549ePDgAWJiYjB06FCsXbtWjEM5nkePHiEwMBBBQUF49eoVypUrh1atWmHSpEl6TYZ0+fJlbN26FZcvX0Z0dDQsLCzg7u6OLl26YOLEiShfvrz+BymPDRs24OXLlwCAcePGoXHjxvmOoSZ9+/bVWPbu3bvYuHEjLly4gKioKJiamsLNzQ0dO3bExIkTUa1aNZ3t7927F9u2bcOdO3eQk5ODqlWronfv3pg0aRIcHBzEcpmZmVpjvnDhAnbt2iUePzMzM7i5uaFz586YNGkSKleurNf+avLrr78CANq2bQupVKrz+GVmZmL37t34888/cefOHcTGxsLMzAzVqlVDs2bN0KdPH3Tq1EnteIkpKSnYuHEjjh07hkePHiEtLQ3ly5dH06ZNMWzYMHTr1g2A+ms97zkeGRmJwMBAHDt2DFFRUbCyskLjxo0xceJEvPvuuxrjl8lk2LNnD/bv3487d+4gPj4elpaWcHFxgZubG9q3b4/33nsPdevWBZCbgJ48ebJKG+r+ob1165Z4XuSN9cmTJ/jpp5/w119/ISoqCunp6SrlX758iSNHjuDChQu4c+cOXr58iZycHDg7O8PHxwcDBw5E3759NfY8e/bsGRo1agQg93Nh+PDhKusDAgLEsSzj4+ORkZGBDRs2YP/+/Xj69CkAoHbt2hgyZAj8/f1hZqb6VWHSpEnYvXu3+PjcuXP5jkHVqlXx33//aTzu2ui6Bm7duoWtW7fi4sWLiIqKgiAIcHV1Rbt27TBp0iTUrFlTbT3l1+7WrVtwc3PD9u3bsWvXLjx8+BDZ2dnw8PBAv379MGnSJJ2fnyEhIVi1ahUuXLiA2NhYlC9fHq1bt8bkyZPRqFEj8TjlPRYNGzZUGZf066+/xtdff63S9rBhw8T34LS0NPH5tLQ0JCUl4cCBA9i6dSvu3r2L1NRUuLm5oXv37pg+fbr4OVFUSUlJ+OSTTwAACxcuhFQqFT8PFOu1sbS0RKtWrXDx4kX88ssvOm/r1/aZvmvXLgBAjRo14O7urnHbyhM9KY6VJlWrVkVMTIzG8y0jI0NlWd/PEgVFMlQulxe4roLya6/tuqhevbq4HBoaqnV7NWvWhLu7O8LDw7Fjxw5OYlgGZGVlIT09HefPn1e5RkurU6dOGTsEIipmvM5z2fTujSpbtwEAnm3ZgudWlmggNMBpnIYMMuwO2Q23F26wkFgYOVLSRvn7mC5MsJYhkydPxuTJk5GUlAQHBwd06dKlQLfkZWRk4Pnz57C1tYXl/w/MXBoJgoDk5GTY2dmV6QlmzMzMNL5+FhYWWl9bRUJEXRvKSQMTExMMHjwYZ86cUSkjlUrFesptXbp0CUOGDFGZHOPVq1c4ePAgDh06hOXLl2Pq1KlqY5LL5Zg6daqYNFDIzMzE7du3cfv2bWzatAl79uzRmhDTZs+ePQByJ4/59NNPi3RLqkJAQAC+/PLLfD2H7t+/j/v372Pz5s346aefMHr0aLX1c3JyMGLECOzbt09t/X379uHkyZPi85pe24yMDPj7+4v7qOzevXu4d+8etmzZgl9++UVMoBdUZmYmrl69CgBo06aNzuN38+ZNDBw4EKGhoSrPZ2Vlifu3Y8cOPHnyBB4eHiplbty4gd69eyMyMlLl+cjISBw6dAiHDh1Cv379sHbtWri4uOS71pXPy9u3b6N///4qY0NmZGTgr7/+wl9//YWlS5di5syZ+eJPSUlB7969ceHCBZXns7OzkZycjKdPn+L8+fMICQnB3r17AUDv90ZbW1u111BQUBBGjRqVb4IZRXmZTIYGDRqo7akWFRUljiG5e/du7N+/H7a2tmq3rWBpaZnvdbSw+N+XuPT0dPTo0QM3b95UKXP9+nVcv34dFy5cwIEDB1SSuVKpVOf+m5iYFPr603QNyOVyzJo1Cz/++GO+MWYfP36Mx48fY8eOHVi9erXYm0+Z8mun6b0vJCQEISEhOHXqFE6fPg0bGxu1Me7cuRPjxo1T+UX7xYsX2Lt3Lw4ePIj169eLxynvsdDnlmzl92Dl92xLS0t8/PHH+OWXX/Lt/+rVq3Hs2DGcO3cOlSpV0rkNXebOnYuoqCi0b98eEydOBACVZLs+r2+bNm1w8eJFXL58GaampmqPpz6f6ZcuXQIAtGrVSut2lc9ta2trrWUjIiIA5N7FoK6c8vmi7jrSRfE6F+VaUH7ttX3uKyfVraysdG6vZcuWCA8Px19//WWQz0kyroyMDFhZWaF9+/al+vt7dnY2Tp06hXfffVevzxEievPwOs+jRw9Ep6dDIpWi+tSp8P7/9+jYG7F4lfYKHzf6GK62rkYOknQpyA/lTLCWYVKptEBvbDKZDBKJBCYmJqV6TC5F4kERa1mmaf8Ksu95yyk/njt3Lv777z/07t0bY8eOhbu7O169eoWkpKR89SIjIzFy5EiYmZlh8eLF4szhQUFB+O6775CUlIQZM2bA09MTffv2zRfHnDlzxOSqp6cnPvvsM/j6+iI1NRWHDh3CmjVrkJiYiN69e+Pq1atiDzx9JSUliUmiOnXqoFatWgWqr87atWvFWZddXFzw2WefoU2bNpDJZDh9+jSWLVuG1NRU+Pv7o0KFCujRo0e+NmbPni0mV+vUqYPZs2ejYcOGSExMxN69e7Fx40YMG/a/Ac7VvbaCIGDw4ME4evQoAKBXr14YPHgwqlevDhMTE1y9ehUrVqzAs2fPMHjwYFy6dAlNmzYt8P7++++/Yg+w5s2baz3H7t27Bz8/P6SkpAAA+vXrh6FDh6J69eqQyWR4+PAhTp48KSbnlNt68eIF3n33XcTHx0MikWDs2LEYOnQonJ2dERISghUrVuDWrVs4cOAAAGDfvn0aY4mKikL//v1hYmKCgIAAtG3bFubm5rh48SK+/vprJCQkYN68eejRowcaNGigUvfrr78Wk6s9e/bEiBEjUK1aNVhaWiI6Oho3btzAkSNHVOLv378/mjdvjrVr12LdunUAoHbSuqpVq+aL+dmzZxg9ejSsra3x5Zdfol27djA1NcW1a9dgb28PExMT8f2tU6dO6N69O7y9veHi4iImfDdu3IjLly/j9OnTmDJlCrZt25Zv28rbVfd+rpzEGjhwIEJCQvDJJ5+gV69ecHJywoMHD/DNN9/g3r17OHLkCH7++WdMmDBBrLN48WLMmjUL77//PoKDg9VOyGdubl7o92dN729TpkwR30Pat2+PsWPHonr16rC2tsatW7fwww8/4O7du/joo4/g6uqK3r17azwuEyZMwD///IMxY8Zg8ODBqFSpEp49e4alS5fi8uXLuHr1KhYvXowlS5bki+Pvv/+Gv78/ZDIZrK2tMWPGDHTp0gUWFhYIDg7GkiVLMHHiRJXzTXnbJ0+eRFZWljihYd7Ji4DchJlygk5h/vz5+Pvvv9G3b1+MHj1afM8ODAzE0aNH8fjxY8ycOVOlh3FhXL58WUwSr1u3Tu3roc/rq5gkLysrC1euXFE71qmuz/SIiAiEhYUB0P2+pHxua/sus23bNrx+/RoA0KdPH537V9TvRYWtq1xP2+f+gwcPxGVPT0+d22vRogX27NmDFy9e4OnTpxp7fdObwcTEBBKJpMDf+43lTYmTiAqP1/n/uC5YkO8H5JnNZpbpjmJlTYHOZYHKnMTERAGAkJiYWKB66enpQkhIiJCenl5MkRmGTCYT4uPjBZlMZuxQigUAAYDg5+encd38+fO1tuHn56exjaCgILEdAMIXX3yhV1sABAcHByEkJCRfmTt37gj29vYCAKFKlSpCVlaWyvr//vtPMDExEQAIXl5eQnx8fL42jh07JpZp3ry51pjUuXjxohjniBEjClw/r+joaMHa2loAILi6ugrPnj3LV+b69euCjY2NXvvt6+srJCcn52tj27ZtKq+Hutd2w4YNAgBBKpUKx44dUxtvXFyc0KBBAwGA0KZNm0Lt83fffSfG8fz5c61lfX19BQCCiYmJsHv3bo3lYmJihLS0NJXnBg4cKG5n06ZN+epkZGQIHTt2FMscOXIkXxnl89Ld3V2IiIjIV+bChQuCRCIRAAiffPJJvvVVq1YVAAgDBw7Uuq+xsbH5nps/f764fV2UY3V1dRXCw8M1lpXL5cKjR4+0tvfVV18JAASJRCI8fPgw3/rQ0FBxe1u2bNEau1QqFYKCgvKViY2NFSpWrCgAEBo2bKh1v9S9zxSGtmvg5MmTWs8ZQcj9DOvUqZN4TmRnZ6us37Jli8q1tmPHjnxtZGRkCF5eXgIAwdnZOV8bgiAIPj4+AgDBwsJC+Oeff/Ktf/XqlVC9enWV87Og+6ss73v2t99+m6+MXC4XunTpIgAQzMzMhOjoaK1tapOVlSW+l8ydO1dlnfK5rI/w8HCxfEBAgNoyuj7T9+zZI7Zx4cIFrdtTPrc3b94s3L59W/y7efOmcOLECWHatGmCubm5AEBo0KCB8Pr1a7VtKZ8v6q4jXdzd3cVrXjkOTX+vXr3K14bya6/pPMnJyREaN26s9zESBEE4d+6cWP7XX38t8L5R6fKmfH/PysoSDh48mO+7EhGVHbzOqSwqSH6tbHf/IyKtateujQULFuhd/ssvv0S9evXyPd+gQQOxp+eLFy/wxx9/qKxft26d2Etp06ZNKFeuXL42unXrJs7qfPXqVVy7dk3vuAAgNjZWXK5QoUKB6qqzZcsWcbyVlStXomrVqvnKNG7cGHPnzgWQu98HDx5UWf/TTz+J+71hwwa1t3OPHj0a3bt31xiHIAjieJmffPKJOC5pXo6Ojli2bBmA3NtpHz16pGMP81PcMgtoP4YnT57E9evXxZiGDh2qsayzszOsrKzEx5GRkWLP1G7duqmd+MbCwgKbN28Wb0cODAzUGvfq1atRpUqVfM+3bdtW7EGXdxgAAOJ4ve3atdPavpOTk9b1BREQEKB1zF6JRKKzN9lXX32F8uXLQxAEHDp0qEjxTJkyReyNrszJyQnvv/8+gNweuomJiUXaTlEFBAQAAAYMGKBxsiRLS0txpvnw8HAEBQVpbK9///4YOXJkvuctLCzw8ccfA8h9TwkJCVFZf+XKFbGn/JQpU8TzS1mFChXw/fff696pQmjSpAnmzZuX73mJRIIZM2YAyB2W5PLly4XexnfffYe7d+/C09MTX375ZaHbAVTfRxTj+xaUvu9Lefn7+8Pb21v88/HxQdeuXfHDDz/AysoKixYtwsWLF4s07rc+IiMjVeLQ9Jd3+BxdXr9+jb/++gt+fn64ceMGgNwe6W3bttVZ1xCvCxERERVc5tOniPhkKmQp/xsuLC07DU8T+XlcFnCIACq04CMH8O/RgzrLVfCsgX6zv1J57sDSrxEd+kRn3Sbv9UXTnv3Ex1npadgy4yNxlnht+s76EhWr/y9R8eTfqzi9SXuiBgCkllbw//4nneXKgiFDhug946xEIsGYMWM0rn///fcxZ84cCIKA06dPY+DAgeK606dPA8hNxKpLSCh8+OGH2LRpk1inIJNvKE+QomncxIJQxFyuXDmtMzKPGzcOX3zxhVhHeSIXRRve3t5o0qSJxjb8/f1x7NgxtetCQkLw5EnutaJ8TNVp3769uHz58uUCD5OguGXW2toa5ubmGsspT5w2bdq0Am3j7Nmz4kR8mhJlAODh4YF33nkHx48fx7lz5yCTydSeq+XKlcN7772nsZ0mTZrgn3/+UZtEqFy5Mp49e4Y9e/Zg3LhxBZoUsDDMzc11TvSTl1wux8uXL5GcnKwy3qebmxtiYmJw69atIsU0YsQIjesU56wgCAgNDYWPj0+RtlVYSUlJOHv2LADd10C9evVQvnx5xMTE4PLlyxrHc9Znv4Hc5FPDhg3Fx4prGgBGjRqlsY333nsPzs7OKj/8GMLw4cM13lKWN+7CePToERYtWgQAWLNmjcqPI4VhaWkJKysrpKeniz9oFJTifQmAwSbwSkxMxKZNm+Ds7Kwy/EVptnDhQixcuFDtOmtra0ycOFH8IUIX5R+NCvu6EBERUcEknz6NF5/OgpCRARNbW1Ra9A3+ePwH1txYA2upNX7v8zukJhxa4U3GBCsVWlZ6GlLidP/zaOecv3dIelKiXnWz0lVnbBME6FUPAGQ52SqPc7Ky9KprXsR/KN8kyokDXTw9PbX29HFxcYGHhwdCQ0NVxqTMzMwUe1NqS64CuT1CpVIpsrOzcefOHb1jAwA7OztxOe8EQoWh2L6vr6/WcVcqVqwIDw8PhIWFqcSsvN+6EsXNmzfXuC44OFhcbtWqlV6xA4X7pzkuLg6A7iSGordUtWrV4O7uXqBtKB8jXedDixYtcPz4caSlpeHp06dqE8a1atXS+mOLIpGgbobyMWPG4JtvvsHff/8NT09PDBo0CJ07d0bbtm3h4uKi7y7prVatWnpNQCIIAn755Rf8/PPPuHLlCtLT0zWWVZ7YqzDq1q2rcZ1yEkbbDO/F7caNG2JP8GHDhqmMWayNtmugsPutOH8tLCzyjemrzNTUFD4+Pvkm0Sqq4n69JkyYgIyMDAwYMEDtmNKF4ejoiPT09EK/LyvelxRt6SsoKEild7YgCEhKShLH6z1w4AAmTpyIBw8eYOXKlYWKTR/u7u7iGLLFxcfHB5988oneY4QpH0dDfF4SERGRbhZ160JiagoBQOLvv8PGrz0Oyg8iOj0aSAf2P9yPoXU13xlIpR8TrFRo5lbWsHVy1lnOyt5B7XP61DW3Uu1RJpEAtk7OevVgNTVT/UfDzNxcr21KLd+eBGtB/lnV59bMihUrIjQ0VOUf4vj4eL3bkEqlcHZ2xsuXL1Xa0Iez8/9e21evXhWorjqK7euz35UqVUJYWFi+/Rb+f6ZzXW1UrFhR47ro6Gh9ws1HMbxBQSiSf9oSesD/knqVK1cu8DaUj1FBjoum80FXr1PF+4QiQafsyy+/xIsXL7BlyxZER0cjMDBQHI6gQYMGGDBgACZNmqT19SkIfa63jIwM9O/fX2OP5rx0vVa6aDt+yu+xil7HxlAc10Bh91vxfubk5KSz939xJOmL8/XavHkzgoKCYGdnhx9//LFQ8amjOEcLO9mF8o8S6enpKj+mFYREIoGDgwPat2+P9u3bY8SIEdi1axe+//579OjRQ+0EXKWJ8mRoOTk5iIiIwL59+7Bjxw78/fff6NChA65evarXeaf8vsFJSIiIiEqGuZsbKn7xBaL+f4i5V1/Nx8ytARgRnTv02tqba/Fe9fdgZ1647zpkfEywUqE17dlP5fb9gsg7ZIC+zK2s8WHgFiQlJYmzbuurRpPmqNFEc0/Bt5G+wwMAMMhMh8U5W6K3t7c4C7tifFBDMPZ+KydKDh8+DA8PD73qFWYcWsU/5gkJCRAEodhntzT27JlSqRQ///yzOOv6X3/9heDgYGRlZeHu3bu4e/cuVq5ciZ07d6JPnz5F3p4+19uiRYvE5Kqfnx8mT54MX19fVKpUCVZWVuJ7Xvv27XHhwgUxiV+WKV8D69evR+vWrfWqZ6jbyd8WirGe/fz81I5ZDKgmu3/99VcAuUOy9OrVS215uVwujt+rbuxtfSgnDOPi4gqdYM1r1qxZ2LVrF4Dc5HJpT7BWqFABXl5e4mMfHx/07NkTHTt2xNixYxEWFoZx48blGwNdHeUfrAr7uhAREVHBOfTtg5SzZ5F84gRkiYkot2wHuo/qhmPPjiM+Mx6b72zGVN+pxg6TCokJVqI3iEQigSAIanvjKSuOW/706RWqKKN8q6pykkNXGzk5OeKYhQWdWMje3h4+Pj64fv06Hjx4gMePH+ucLEgbJycnREVF6bXfiluRlWNW/qdVVxva1iv3zC1XrpzKP9iGpkhkKJIimv7xVgwVERUVVeBtKB+jV69eqZ08THm9unqGVr9+fXzzzTf45ptvkJGRgYsXL2LXrl3Yvn07UlJSMGzYMDx58qRQPXYLQhAEcQzidu3a4a+//tL4I1JBe3i/yZSvAWtr62K9BnRRvJ/FxcVpHBdYQXns0DdBZmYmgNwxlpXHWdZEMVSDu7u7xgRrYmKi+HmlbXI3bZQTrPHx8QUelkQT5eEWlIe1edOMGTMGhw8fxv79+3Ho0CH89ddf6NSpk9Y6yneWFPZ1ISIiooKTSCSotGA+0m/cQE50NFL//hsTW03CaUspsuXZ2BGyA4NrD0Zl2+L9v4OKh/7d/4jI6BQ9d5T/OcpLEAQ8fvzY4NsODQ3VOmHL69evxXHmlBMgFhYW4tiZV65c0bqNGzduiBP5FCaJopj1XBAErFq1qsD1lSm2f/36deTk5GgsFx0djfDwcJU6QO5trYr9vnbtmtZtaVvfuHFjcfnSpUu6Ay8Cb29vcfnhw4cay/n6+gIAnj17Ju67vpSPka7z4erVqwByk2rVq1cv0HYKy9LSEu+88w42b96MZcuWAci9nTZvwqk4et/GxcWJyfpBgwZpTK6mpKTgwYMHBt9+QZVUD2QfHx9xW8V9DeiiGHc1MzMTd+/e1VhOJpPh5s2bJRRV6aX8PqJtzFpt9H1fKijl93Vt7/FvgsWLF4vJ/nnz5uksb4jXhYiIiArHzNERlZcsFh9nrd6ECXa5Y99nyjKx6kbR/o8l42GClegN4unpCUB14qO8jh07hoSEBINvWxAEbN++XeP6rVu3ircr573VUvH47t27YtJMHUXvPXVt6MPf3x+VKlUCAAQGBuLcuXN6192xY4fKY8X2ExIS8Pvvv2us9/PPP+vc79u3b4sTQ6mzefNmjet8fX3h5uYGANiwYQMyMjK07EXRtGvXTlzWlvRV7q32/fffF2gbHTp0EBMB2vb72bNn4oztfn5+BRrOwlA6d+4sLuedTEp5XEhFz7+iUk7yaOuFvmnTplKREFIcA0PtvyYuLi5o2bIlAGDXrl1G7RmqfE7kfc9QdvToUa0/SAEld/z0FRYWBkEQtP75+fmJ5RXPaZvASfl9RNekdpo0bdpUPFa6fqwqCOXPUW096d8EtWvXxuDBgwHk/nB16tQpreUVx1EqlYo/mBEREVHJsW3TBk5jRgMAhKwsdN7yH5xN7QEAR54ewd1YzT/kU+nFBCvRG0Txz+2VK1fU9uR6+fIlpkyZUmzb/+abb9T2nLt37x4WLVoEIHfio7zjVX700Udib7zx48cjKSkpXxsnT57Ezz//DABo3rw5mjVrVuD4rK2tsXPnTnEs1vfeew/79+/XWufZs2cYNmxYvuP2/vvvixPKzJw5Ey9evMhX99atW1i8OPfXxypVqqBv374q6ydMmCD2vBs/frzapNkvv/yCP//8U2N8JiYmYo+kp0+fYvTo0VoTMklJSVizZo3G9dpUrVpVvP1WWyL8nXfeQZMmTQAAq1evFsdiVCc2NlZlQhVXV1f065c7dvOxY8ewbdu2fHWysrLg7+8v9maePHlywXdGh7i4OBw+fFjrGKYnT54UlxU/bigoDxfw5MkTg8Tk4uIiDsuwe/duta/ztWvX8OWXXxpke0WlOAZPnz4t9rFgv/jiCwC55/fAgQO1/oiUmZmJwMDAYvkxolWrVmjYsCGA3HNfXS/s169fY/r06TrbUhw/Q50/pZHifaRatWqoU6dOodowNzcXk7Pa3pcKIjMzU+U6eu+99wzSrjHNmzdP/Lz59ttvtZZVHMdWrVrB1ta22GMjIiKi/FxmzIBFrdwh7bIfPsZMdBHXrQhe8VbMtVDWcAxWojfI+PHjsXbtWuTk5KBXr1746quv0LZtW2RlZeHSpUtYuXIlsrOzUatWLTx69Mig265ZsyZev36Nli1b4rPPPkOHDh0AAGfPnkVAQIA4kcnq1athbm6uUtfb2xszZ87EsmXLcOvWLfj6+uKzzz5D48aNkZqaisOHD2PVqlWQyWQwNzfH+vXrCx1n586dsWnTJkyYMAGpqakYOHAgWrZsiYEDB8LHxwdOTk5ITEzE06dPcfz4cRw6dAiZmZlwcHBQacfFxQXLli3D5MmTERERgSZNmmDOnDlo3bo1cnJycPr0aSxbtgwpKSmQSCTYsGFDvtmYGzVqhMmTJ2PNmjUIDg5G06ZN8dlnn8Hb2xuJiYnYu3cvNmzYgKZNm2rtlTxx4kScOnUKBw4cwN69e3H9+nVMmDABzZs3h4ODA5KSknD//n2cPXsWhw4dgqWlJT7++ONCHb8+ffpg1apVCAoK0jrR1Y4dO9C8eXNxjNK9e/di6NChqF69OmQyGR4/foyTJ09i3759uHPnjsrkXN9//z3OnDmD+Ph4+Pv74+LFixgyZAgcHR1x//59LF++XLy9ul+/fujevXuh9kWbpKQk9O7dGx4eHujfvz9atGgBd3d3mJmZISoqCocPHxZ7VFepUgU9e/ZUqa880dL06dPx+eefo3LlyuLx8vDwgJlZwT5iTUxMMGLECAQGBuK///5D27ZtMWPGDNSqVQuJiYn4888/sXbtWtja2sLV1dWgt0sXRuvWrbFlyxZER0djxowZGDlypHgdSaVSg42VCQA9evTA1KlT8eOPP+L8+fOoV68eJk6ciLZt28LZ2Rmpqal4/PgxLly4gN9//x3x8fEYM2aMwbavLDAwEH5+fsjMzESnTp0wY8YMdO3aFRYWFggODsaSJUvw8uVL+Pj44ObNmxqvodatWyM0NBSHDh3C+vXr0aZNG7Gnpr29faEmqitNBEFAUFAQAIg/qhRWnz59cO7cOVy9ehXJycl6TXQVGhoqjhetkJSUhJs3b2Lt2rXiEA81a9bE+PHjtbZ18eJFveLs3bt3vvGis7OzcefOHb3q16xZU6V3fEF4eXmhd+/e+OOPP3D+/HlcvHgRbdu2zVcuOTlZ7MFa1NeFiIiICs/EwgKuy5bhxbTpqPzN16jp64Of/riKZ8nPcCfmDp4nP0c1e46V/kYRqMxJTEwUAAiJiYkFqpeeni6EhIQI6enpxRSZYchkMiE+Pl6QyWTGDqVYABAACH5+fmrXr1y5UiyT98/JyUk4f/684Ofnp7GNoKAgsXxQUJDOeJTbOnLkiGBtba122yYmJsLy5cs1tiOTyYRJkyZpjB2A4ODgIJw4cULPI6Xd2bNnBS8vL63bU/xVq1ZN2LVrl9p2Fi1aJJiYmGisa2FhIWzbtk1jHFlZWUL//v011vf09BSePHkiPp4/f77Gdj766CNBIpHo3B9PT89CH7fbt2+L7Zw7d05r2eDgYKFq1ao64wkNDc1X9/r164Krq6vWev369ROioqLUXuvaznFl8+fPF9tTFhoaqte5UblyZSE4OFht24MHD9Zrn/WNVRAEISEhQfDx8dF6jZ87d05rm8r7tmXLFr2PSV663iuSk5OF6tWrq43T3d1d577mpesakMvlwsKFCwUzMzOdr5uNjY2QlpamUn/Lli1az0kFXcdPEARh69atglQqVbttMzMzYePGjcKoUaMEAELdunXVtnHjxg3BwsJCbRtjxowRyxXkPVvXMSwqxXmn69wRhNz3YEXZa9euaSynz2d6TEyMeKy0vd8qn9v6/DVq1EjjuaB8vuj7d+PGDbG+u7t7keoLguprr89revXqVbF8ly5d1JbZunWreJ5GRUXpbJNKvzfl+3tWVpZw8OBBISsry9ihEFEx4XVeOPKcHHH5dNhpYf6l+UJ0arQRIyJlBcmvcYgAojfM9OnTcfz4cXTt2hWOjo6wsLCAp6cnJk+ejBs3bqiMo2lo7733HoKDg/H+++/D3d0d5ubmqFChAgYMGICLFy9i5syZGuuamJggMDAQ58+fx4gRI1CtWjVYWFjA3t4ePj4+mDdvHh49eoQuXbpobKMg/Pz8cOvWLfzxxx/48MMP0aBBA5QvXx5mZmYoV64cvLy84O/vjz/++ANPnjwRZ8TOa968ebhx4wY+/PBD1KhRA1ZWVrCxsUG9evUwdepU3L9/H6NHj9YYh1Qqxf79+7Fjxw60a9cODg4OsLa2Rr169TBv3jz8+++/ek3gJJVKsXbtWty6dQtTpkyBt7c3HBwcYGpqCgcHB/j4+OCDDz7Avn37cO/evUIfNy8vL7Rq1QpA7niX2jRp0gQPHjzAqlWr0KlTJ1SoUAFmZmawtbWFt7c3xo8fjzNnzqj0XlVo3LgxHjx4gCVLlqBFixYoV64czM3N4erqiv79++PQoUPYt29foXtz6eLu7o6rV69iwYIF6NKlC+rUqYNy5crBzMwM5cuXR/v27bFs2TLcv39fHA4hr507d2Lp0qViT2JNk1IVhIODAy5duoRvvvkG3t7esLS0hK2tLerVq4dPP/0Ut27dQvv27Yu8HUOwtbXF33//jalTp6JevXrikBrFRSKR4KuvvsLDhw8xe/ZsNG3aFE5OTjA1NYWdnR3q16+PESNGYNu2bYiKioKVlVWxxTJmzBgEBwdjxIgRcHV1hbm5OapUqYLBgwfj4sWLGDdunDgUSt7e8Qo+Pj64fPkyhg0bJr4fliWK949mzZqhadOmRWrL2dkZ/fv3V2m3MKytrcVe67t27UJwcLDa96c3VbNmzfDuu+8CyB3iRN2YtYrj169fP3HMciIiIjIeidJcE53dO2NB6wVwsXYxYkRUWBJB4MAOZU1SUhIcHByQmJgIe3t7vetlZGQgNDQUnp6exZbUMAS5XI6kpCTY29sbJKFBRKp+++038Zb9Z8+eGW2MPl7r9CarWbMmnjx5gpEjR2qdEKssSk5ORrVq1ZCQkIDdu3dj6NChGsvqe51fuXIFLVu2hKmpKZ48eWLQISjeFuHh4ahRowZkMhkuX74sTh5Hb7Y35ft7dnY2/vzzT/To0SPfkEpEVDbwOi86QS5H3LbtMCtfHg69euquQMWuIPk1/sdKREQqBg0ahCZNmiA+Pr7QE2YRvc2uXbsmTl71Niax1qxZg4SEBNSvX1+c3b6oWrRogf79+0Mmk2HJkiUGafNts3jxYshkMnTr1u2tPC+JiIhKM3lGBp75f4Do777Dy4ULkRXxAklZSbj4Qr+x4Mn4mGAlIiIVEokE3333HQBg5cqVSE1NNXJERKXL48ePNa6LjY3Fhx9+CACwsLDAkCFDSiqsUiE1NRUrV64EACxbtsygvc8XL14MMzMzbNmyBREREQZr923w/PlzbN26Faampli6dKmxwyEiIqI8TCwtIa2YO8mpPCUF/33yAXru64FpQdMQlRJl5OhIHwWb4piIiN4KnTt3xqpVqxAbG4vw8HDUr1/f2CERlRrvvvsuPD090a9fPzRs2BAODg6Ij4/HpUuXsHbtWkRF5X4J/uKLL/LNZF/WhYeHY/LkyXByckKPHj0M2nadOnWwefNmPHnyBM+ePYObm5tB2y/Lnj9/jrlz56J69erw9vY2djhERESkRsUvvkDatWBkR0bCJiQc7c+b4I9WJlh9YzUWt1ts7PBIByZYiYhIrSlTphg7BKJSSRAEBAUFISgoSGOZSZMmYd68eSUYVelQv359LFiwoNjaHzVqVLG1XZa1bt0arVu3NnYYREREpIWpnR1cl36H8FGjAUHAkPNy/OcpwWEcxsj6I1HfmZ1eSjMOEUBERERUANu2bcPMmTPRrFkzuLm5wcLCAjY2NqhRowbGjBmDS5cuITAwkJOzEREREVGBWDdtCuf/H27KTA5MOSSDebaA5cHLwTnqSzf2YCUiIiIqAD8/P/j5+Rk7DCIiIiIqg1w+nozUixeRERICt1hg5F9ybO56DecizqFD1Q7GDo80YNcKIiIiIiIiIiKiUkBibg7X5csgsbQEAHS7LqDxYzlWBK9AtjzbyNGRJkywEhERERERERERlRIW1auj4mezxcd9L8sRlhiK3x/+bsSoSBsmWImIiIiIiIiIiEqRckOHwtbPD/JenbF4iCkgkWDtrbVIyUoxdmikBhOsREREREREREREpYhEIoHb6lVosGwNOtbuDgDIlmfjQfwDI0dG6nCSK8qHM9MREREREZV+/N5ORFS2SczNAQBTfaeignUFjG84Hg4WDkaOitRhD1YSmZjkng5yudzIkRARERERkS6K7+2K7/FERFQ2udm5YVazWbB6nYyXX38NIZuTXZU27MFKIqlUClNTU6SmpsLGxsbY4RARERERkRYZGRkwMTGBmRn/rSMiKuuSTp1C1Jy5kKemwtTRCS5TPjZ2SKSEP3WSSCKRwM7ODklJSbzdiIiIiIiolEtJSYG1tTV7sBIRvQWkFStCnpEBAIj56Sfs/30xczelCD+JSYWDgwOys7MRGRnJC5WIiIiIqJSKj49HWloa7O3tjR0KERGVAKuGDVF+8qTcBzIZnJbuwIWHJ40bFIl4LwmpsLa2hpubGyIiIpCeng57e3tYW1vD1NQUEonE2OEByB1rKisrS7wliojKJl7rRGUfr3OighEEATk5OUhMTERycjIcHR3h4MDJToiI3hblx4/Hi1OHYXEvDJUSgKvffIXW2zvDzITpPWPjK0D52NnZwd3dHYmJiUhISEBsbKyxQ1IhCALS09NhZWVVapK+RGR4vNaJyj5e50SFY2FhgYoVK8LR0dHYoRARUQmSmJmh3g8/4V7vHrDIlKN5cBJOb1+EbmPnGzu0tx4TrKVEWFgYPD091a4bM2YMtm7dWqLxWFtbw9raGpUqVUJ2drY4Q2lpkJ2djfPnz6N9+/aQSqXGDoeIigmvdaKyj9c5UcGZmprCzMyMP0oQEb2lLNzdYTp9PBDwEwDAedUeJL4zGg5u6nNKVDKYYC1l+vXrh/79+6s8V6NGDSNFkzvxlbm5udG2r46pqSlycnJgaWnJf8aIyjBe60RlH69zIiIiooLzGvMJjp04CM8bL2GbJuC/6RPQds9xSDjkktEwwVrKNGzYECNHjjR2GEREREREREREVApJJBJ4LV2NiH6D4JgClL/9HBEXT6Jq+27GDu2txdR2KZSeno709HRjh0FERERERERERKVQtapeePhRV8TZAouGmOAnkwvGDumtxgRrKbNixQpx/NOaNWti9erVEATB2GEREREREREREVEp0m/kQnz+iSNuVTfB4SeHERIbYuyQ3lplMsGalpaGY8eO4dtvv0X//v3h7u4OiUQCiUSCBQsW6NVGcnIyFixYAG9vb9ja2sLBwQHNmjXDihUrkJWVZfCYTUxM0LlzZyxZsgSHDh3C+vXr4ejoiE8++QTTp083+PaIiIiIiIiIiOjN5WDhgA+afgQAcLJ0Qkx6jJEjenuVyTFYr169ih49ehS6fnh4ODp06ICwsDAAgLW1NTIzMxEcHIzg4GD88ssvOHPmDBwdHfPVFQQBmZmZem3HxMREnECqWrVqOH36tMr6Dz74AJ07d8aqVaswYcIE1KtXr9D7REREREREREREZcvQOkORI8/B4DqDYW1mjfjdu2FevQZsWjQ3dmhvlTLZgxUAHB0d0blzZ8yaNQu7d+9GpUqV9KqXk5ODXr16ISwsDJUrV8apU6eQmpqKtLQ0/Prrr7Czs8ONGzc0TkQVHh4OKysrvf58fX21xmJqaorPPvsMgiDg1KlTBT4GRERERERERERUdklNpXjf631YZgIREz/Cy4VfI/KzzyBLTDR2aG+VMtmDtV27doiLi1N5bs6cOXrV3bZtG27fvg0A2L9/P1q1agUgt7fpkCFDIJfLMXz4cPz55584c+YMOnfurFK/fPny2LJli17bUtcDNi93d3cAQEwMu3kTEREREREREVF+JtZWkGdkAAByXr7Ey6+/QZUVy40c1dujTCZYTU1NC11327ZtAICOHTuKyVVlQ4cOxeeff47Q0FBs3749X4LV1tYWY8eOLfT283r8+DEAoGLFigZrk4iIiIiIiIiIyg6JiQlcA5bgSa9eEFJSkXT0KGw7dIBDr57GDu2tUGaHCCiMtLQ0XLp0CQDQvXt3tWUkEgm6desGADh58qTBth0dHZ3vuYyMDHzzzTcwNTVF165dDbYtIiIiIiIiIiIqW85k3sKad3PExy+//hrZL14YMaK3R5nswVpY9+7dg1wuBwB4eXlpLKdY9/LlS8TFxcHJyanI254wYQJiY2PRsWNHuLm54eXLl9i+fTseP36M+fPno2bNmhrrZmZmqkyslfj/42zExcUhOzu7yLGVNtnZ2UhLS0NsbCykUqmxwyGiYsJrnajs43VO9HbgtU5U9vE6Lx1cJa644CmgVm05Wt8TgIQE3JsxA66rV0NShLu931bJyckAcie014UJViWRkZHicpUqVTSWU14XGRlpkARrz549sX37dvz000+Ii4uDjY0NfH19ERAQgAEDBmitu2TJEixcuDDf856enkWOi4iIiIiIiIiI3hzTlB88fgT89puRIikbkpOT4eDgoLUME6xKFJlpALC2ttZYTnmdcp2i+OCDD/DBBx8Uqu7cuXMxY8YM8bFcLkdcXBycnZ0hkUgMEl9pkpSUhKpVq+L58+ewt7c3djhEVEx4rROVfbzOid4OvNaJyj5e51QWCYKA5ORkuLq66izLBGsZYGFhAQsLC5XnypUrZ5xgSpC9vT3fuIneArzWico+XudEbwde60RlH69zKmt09VxV4CRXSuzs7MTltLQ0jeWU1ynXISIiIiIiIiIiorcLE6xKlLv8vtAyy5ryOn26CRMREREREREREVHZxASrknr16sHEJPeQ3LlzR2M5xbpKlSoZZIIrKhgLCwvMnz8/37AIRFS28FonKvt4nRO9HXitE5V9vM7pbScRBEEwdhAlwcPDA+Hh4Zg/fz4WLFigsVz79u1x4cIFdOrUCWfOnMm3XhAE1KxZE0+fPsXo0aOxbdu2YoyaiIiIiIiIiIiISjP2YM1jzJgxAICgoCBcuXIl3/q9e/fi6dOnAIDRo0eXaGxERERERERERERUupTZBGt8fDxiYmLEP7lcDiB3girl51NSUlTqjRkzBt7e3hAEAQMGDBB7scrlcuzduxcffvghAKB79+7o3Llzye4UERERERERERERlSpldogAxZAAuowZMwZbt25VeS4sLAwdO3ZEWFgYAMDa2hpyuRwZGRkAgMaNG+PMmTNwdHQ0dNhERERERERERET0BimzPViLwsPDA//99x+++uoreHl5QSKRQCqVokmTJli+fDn++ecfJleJiIiIiIiIiIio7PZgJSIiIiIiIiIiIipu7MFKb4zk5GQsWLAA3t7esLW1hYODA5o1a4YVK1YgKyvL2OERURGlpaXh2LFj+Pbbb9G/f3+4u7tDIpFAIpFgwYIFxg6PiAwgNjYWW7ZswciRI1G/fn3Y2NjAwsICbm5u6Nu3Lw4cOGDsEInIAK5fv46FCxeid+/eqFu3LpydnSGVSuHs7Iw2bdpg0aJFiIuLM3aYRFQMAgICxO/wEonE2OEQlRj2YKU3Qnh4ODp06KAyLq5MJkNmZiYAjotLVBacPXsWHTt2VLtu/vz5TLISlQFSqRQ5OTniY0tLS5iamiI1NVV8rnv37ti3bx+sra2NESIRGcDHH3+MwMBA8bGlpSWkUimSk5PF58qXL49Dhw6hVatWxgiRiIrBgwcP4OPjI85fAwBMOdHbgj1YqdTLyclBr169EBYWhsqVK+PUqVNITU1FWloafv31V9jZ2eHGjRsYOXKksUMloiJydHRE586dMWvWLOzevRuVKlUydkhEZEA5OTlo3rw51q5diydPniA9PR0pKSkIDQ3FBx98AAA4duwYJkyYYORIiagomjdvjmXLluHy5cuIj49Heno6kpKSkJycjG3btsHFxQUxMTHo27cvEhMTjR0uERmAXC6Hv78/MjIy+MMJvZXYg5VKvZ9//hnjxo0DAPz999/53qx3796N4cOHAwBOnz6Nzp07l3iMRFR0MpkMpqamKs95eHggPDycPViJyoigoCCNPdUBYOLEiVi/fj0A4NmzZ6hatWpJhUZEJejkyZPo2rUrAGDnzp0YMWKEkSMioqL68ccfMW3aNIwYMQI1a9bEwoULAbAHK7092IOVSr1t27YBADp27Kj2l7ChQ4fC09MTALB9+/YSjY2IDCdvcpWIyh5tyVUAYi9WAAgODi7ucIjISFq2bCkuR0REGDESIjKE0NBQfP7553B2dsb3339v7HCIjIIJVirV0tLScOnSJQC5Y7KpI5FI0K1bNwC5v4YTERHRm8nS0lJclslkRoyEiIrThQsXxOUaNWoYMRIiMoQPP/wQqampWLlyJVxcXIwdDpFRMMFKpdq9e/cgl8sBAF5eXhrLKda9fPmSM5ISERG9oc6ePSsue3t7Gy8QIjK4zMxMhIWFYc2aNRg1ahQAoGbNmujVq5eRIyOioti4cSPOnDmDd955B6NHjzZ2OERGY2bsAIi0iYyMFJerVKmisZzyusjISDg5ORVrXERERGRYCQkJWLJkCQCgXbt2qFOnjpEjIiJDsLS0RGZmZr7n27Rpg127dsHCwsIIURGRIbx48QKzZs2ClZWVOIY60duKCVYq1ZKTk8Vla2trjeWU1ynXISIiotJPLpdj1KhRiIqKgqWlJdasWWPskIjIQCpVqoSMjAykpKQgNTUVQO54zEuXLkW1atWMHB0RFcWECROQmJiI7777DtWrVzd2OERGxSECiIiIiMiopk6diiNHjgAAAgMD0bBhQyNHRESGEhYWhpcvXyIlJQWvXr3C8uXLcfPmTTRv3hxfffWVscMjokLauXMnjh49Ch8fH8yYMcPY4RAZHROsVKrZ2dmJy2lpaRrLKa9TrkNERESl26effir2WP3+++/h7+9v5IiIqLhUqFABM2fOxPHjxyGRSPDNN9+IP64Q0Zvj1atXmDZtGkxNTbFx40aYmfHmaCImWKlUc3V1FZdfvHihsZzyOuU6REREVHrNnj0bK1asAAAsX74c06ZNM25ARFQimjdvjrZt2wIANmzYYORoiKig5syZg9jYWIwfPx5169ZFSkqKyl9WVpZYVt1zRGURE6xUqtWrVw8mJrmn6Z07dzSWU6yrVKkSJ7giIiJ6A8yaNQvLli0DACxduhQzZ840ckREVJIUk9Q+fvzYyJEQUUGFhoYCANatWwc7O7t8f4pJKwGIz82ePdtY4RKVCCZYqVSztrZGmzZtAADHjx9XW0YQBJw4cQIA0KVLlxKLjYiIiArn008/xfLlywHkJldnzZpl5IiIqKQ9ffoUAIf3IiKisoEJVir1xowZAwAICgrClStX8q3fu3ev+AVt9OjRJRobERERFcynn36qMiwAk6tEZYtMJoMgCFrLnDlzBlevXgUAdOjQoQSiIiJDOnv2LARB0Pg3f/58saziuR9++MF4AROVACZYqdQbM2YMvL29IQgCBgwYgDNnzgAA5HI59u7diw8//BAA0L17d3Tu3NmYoRJREcXHxyMmJkb8k8vlAHInslN+PiUlxciRElFhKI+5unLlSg4LQFQGPX/+HI0bN8b69evx9OlTlWTr8+fPERAQgD59+kAQBDg5OWH69OlGjJaIiMgwJIKunxeJSoGwsDB07NgRYWFhAHKHDpDL5cjIyAAANG7cGGfOnIGjo6MRoySiovLw8EB4eLjOcmPGjMHWrVuLPyAiMphnz57B3d0dAGBiYgIXFxet5T/99FN8+umnJREaERlQWFgYPD09xcfm5uawt7dHeno6UlNTxec9PT2xf/9+NG7c2BhhElExWrBgARYuXAgAOnu0E5UVZsYOgEgfHh4e+O+//7B8+XL8/vvvCA0NhVQqRYMGDTBs2DBMmTIF5ubmxg6TiIiINFD0SFcsv3r1Smt59lQnejO5urpi7969OHv2LK5cuYLIyEjExMTA1NQU1apVQ6NGjdCnTx8MHz4cVlZWxg6XiIjIINiDlYiIiIiIiIiIiKiQOAYrERERERERERERUSExwUpERERERERERERUSEywEhERERERERERERUSE6xEREREREREREREhcQEKxEREREREREREVEhMcFKREREREREREREVEhMsBIREREREREREREVEhOsRERERERERERERIXEBCsRERERERERERFRITHBSkRERERERERERFRITLASERERERERERERFRITrEREREREZcylS5cgkUggkUiwYMECg7V79uxZsV0PDw+DtVsQycnJcHFxgUQiQdu2bY0SAxEREZEyJliJiIiIqED++ecfMclWmL8WLVoUe4y///67uL2ZM2fqvR8jR47Uextbt24V69na2kIulxsq/CKRy+X45JNPAADly5fXuP/FaezYsTrPA3Nzc7i4uKBp06b46KOPcPbsWQiCoLNtOzs7zJkzB0BuInnPnj3FvTtEREREWjHBSkREREQFcuPGjSLVb9asmYEi0ezw4cPics+ePdWWUbcfR48eRU5Ojl7bUK7fsGFDmJiUjq/Wu3fvxvXr1wEA06ZNg52dnZEjUi87OxsxMTH4999/8dNPP6Fjx47o2LEjQkNDddadNGkSypcvDwCYN2+e3q8ZERERUXEwM3YARERERPRmUU4sVqhQAY0bNy5Q/R49ehg6JBVyuRx//vknAKBcuXJo166d2nLqEqwJCQk4e/Ys3nnnHZ3bUa7v4+NTuGANTCaT4euvvwYA2NjY4KOPPjJyRICjoyOaN2+e7/m0tDQ8f/4cYWFh4nPnzp1D+/btcfnyZbi5uWls08rKCpMnT8bChQvx9OlTbNu2DR988EFxhE9ERESkExOsRERERFQgN2/eFJcHDRqENWvWGC8YNa5evYro6GgAQNeuXWFmpv4rr/J+2NraIiUlBQBw4MABnQlWQRBw69Yt8XFBk8zFZd++fXj48CEAYPjw4XBycjJyRLm9e48fP65x/aNHjzBr1iz88ccfAICIiAhMmzYN+/bt09ruRx99hEWLFiEnJwcBAQHw9/eHRCIxaOxERERE+igd9zERERER0RshJycHt2/fFh97e3sbMRr1lIcH6NWrl9oyefdj8uTJ4vKhQ4d0jgX69OlTJCUliY9LS4L1xx9/FJfflB6dtWrVwu+//44OHTqIzx08eBCxsbFa61WsWBHvvfceAODx48dir2UiIiKiksYEKxERERHp7f79+8jIyBAfl+YEq6mpKbp37662TN798Pf3R7Vq1QDk9qAMDg7Wug3l4QHMzMzg5eVV1LCL7M6dO7h8+TIAoEaNGiUymZihmJiYYNq0aeJjmUym8zUAcnvpKqxfv744QiMiIiLSiQlWIiIiItKb8m31AEpFYlHZs2fPxJ6prVu31niLfN7hAWrWrIk+ffqIzx04cEDrdpTr161bF5aWloUP2kB++eUXcblv374Frn/+/HmMGjUKnp6esLS0RKVKldC6dWv88MMPSEhIMFygGtStW1flsa4erEDueL4WFhYAgOPHj+tVh4iIiMjQmGAlIiIiIr0p99x0d3eHvb29EaPJT3l4gJ49e2osp7wfDRs2hImJiUqC9eDBg1q3UxonuNq/f7+43K1bN73r5eTkYMKECfDz88POnTsRFhaGzMxMvHr1CpcvX8b06dPh4+OD69evF0fYoqysLJXHtra2OuvY2tqiTZs2AIDs7GxxHFciIiKiksQEKxERERHpTbnnZmkcHuDIkSPisqbxVwHV/VAkSP38/ODo6AgAuHfvnjhZlK76pWH81dDQUDx69AhA7pAFrVq10queIAgYPXo0NmzYoPJ8/fr10aFDB9SqVQsAEB4ejnfffRcRERGGDVyJYngDBX17R/v5+YnLJ06cMGhMRERERPpggpWIiIiI9FaaE6ypqakICgoCAFSvXh316tXTWFZdgtTMzEycNAnQPExAdHQ0IiMj89U3pnPnzonL9evXh42NjV71Nm/ejN27d4uPO3TogIcPH+Lu3bsICgrCw4cPcfPmTfj6+iIuLg7Tp083eOwAEBMTg4CAAPFxy5YtUb16db3qNmvWTFw+e/asoUMjIiIi0okJViIiIiLSS3h4OOLi4sTHpW381ZMnTyIzMxOA9t6refdD+RZ/fYYJUB4eIG99Y/n333/F5QYNGuhVJyMjA3PmzBEft2nTBsePHxd7rSo0atQIQUFBqF+/PmJiYgwT8P9v/9GjR1i7di18fX0RGhoKALCxsUFgYKDe7Sgn+qOjo/H8+XODxUhERESkDyZYiYiIiEgveSe4GjFiBCQSid5/it6lxUV5eABt468q74eZmZlKorhbt27ihFVXrlxBVFSU1vru7u7isALGFBISIi7XqFFDrzr79+8XE6ampqbYuHGjOGFUXvb29li3bl2hYjt37pza88HKygq1a9fG5MmTxaRohw4dcPHiRfj6+urdvpubm0rcyseCiIiIqCQwwUpEREREesnbc7MgJBIJmjRpYsBoVAmCgKNHjwLITQYqj8uZl/J+1KlTR0yoArmTJnXu3FlsU92kSaVxgqvw8HBx2dXVVa86yj10O3bsqHVIBQBo3759sQ4L0b59e0yePBkNGzYscF3lfVY+FkREREQlwczYARARERHRm0G552bFihULlFysVKkS7O3t1a4bO3Ystm3bhk8//RTLli0rVGzXrl3Dq1evAABdunSBVCrVWFbXBFV9+/YVk7UHDx7ExIkTVdYrJ1hLw/irAFRu3de3R+21a9fE5a5du+pVp3v37rh9+3aBYnN0dETz5s3zPS+TyRAXF4f79+8jLS0N58+fx/nz59GsWTPs3bsX7u7uBdqGYoiB169fFyg+IiIioqJigpWIiIiI9KKcWBw+fDhWrlxp0HaLkqw8fPiwuKxt/FXl7QHqe6D26tULJiYmkMvlCAoKQlJSkpgcTk1NxePHj8WyumJ+/fo1AgICcOjQIURERMDGxga+vr6YNGkS+vbtq8ee6Sc1NVVctrKy0lk+OztbpaenvuPp6ju+q7KGDRvi+PHjWmM5ePAgZsyYgYiICFy7dg0dO3ZEcHAwnJyc9NqG8j4rHwsiIiKiksAhAoiIiIhIp7i4ODx79kx83KhRI4O0m5WVhXv37gEo2u32igSriYkJevToobFc3v1Qt82KFSuiZcuWYnyK3qwAcOvWLcjlcq31Fe7evQsvLy+sXLkSjx8/hlQqRUJCAk6dOoV+/fph6tSp+u5egQiCoLNMQkKCymNnZ2e92ta3XEFIpVIMGjQI58+fh52dHQAgNDRUZQIuXfTZZyIiIqLiwgQrEREREemUd4IrQyVY79y5g+zsbFhZWaFOnTqFauP58+e4desWAKBly5YoX768xrJ590NTD1Tl3qXKY5Uq13dyckK1atXU1s/MzETv3r0RHR0NLy8v3Lx5E0lJSUhKSsK3334LiUSCVatWYcuWLdp3Tk82NjbickZGhs7yWVlZKo/Nzc312o6mSbAMwdPTE++//774eMeOHUhJSdGrbnp6urisfCyIiIiISgITrERERESkk/Jt9VKpFPXr1zdIu4qEZcOGDWFqalqoNo4cOSIu9+zZU2tZ5f2oWrWqxlvQ+/TpIy4fO3YMmZmZ+eprGx5gw4YNePr0KaytrXH06FExIW1tbY3PP/8ckyZNAgB88cUXyM7O1hqzPpSTyvHx8TrL5x0PNzk5Wa/t6FuusNq2bSsuZ2RkIDg4WK96yvvs4uJi8LiIiIiItGGClYiIiIh0Uk4s1qtXT+8ej/q26+Pjg6ysLKxcuRK+vr6wsbGBk5MTBg0ahAcPHmhtQznBWtTxVxVq166NevXqAchNKp45c6ZA9Xfu3AkAGDZsmNperrNnz4ZEIkFkZCSCgoK0xqwP5QmhXrx4obO8nZ2dyrilYWFhem1HMZFUcSlXrpzK46ioKL3qRUZGissFmRyLiIiIyBCYYCUiIiIinZRvjTfU8ADK7VapUgXNmzfHzJkzERISgpycHMTHx2Pfvn1o2bKlxiRrWloa/vrrLwCAh4eHzsmalPdD1wRVysMEHDhwADk5Obh7967O+ikpKbh27RoAoFu3bmrLVKtWTUzgKpK3RaFoC4DKJFzaKCeIr169qlcdfcsVVt7et/pM2BUREaEy5IGhelcTERER6YsJViIiIiLSKiMjA/fv3xcfGyrBKggC/vvvPwDAihUrkJmZiZMnTyI9PR0pKSn49ddfYWtri4SEBEybNk1tG6dOnRLHHH3vvfcKtB+6JtVSHibg8OHDuHv3rsr4pprq37t3T5x0SVvCV7EuJCREaxz6aNKkibh8584dveq0a9dOXP7999+Rk5OjtXxKSorKhF/F4fz58yqP9emNevv2bXHZxcUFVatWNXhcRERERNowwUpEREREWt2+fRsymUx8rCsxqa8nT54gKSkJAODq6orLly/j3XffhUQigVQqxZAhQ/Dll18CAE6ePKl2/M+CDA9Q0P1o3rw5XF1dAQCvXr3C2rVrxXVWVlaoW7eu2nrKt7Ur6qujWKfvbfDatG/fXly+f/++XpNDjRo1Slx+8eIF1q1bp7X8kiVL9J50qjAeP36MrVu3io8rV66s17mmPE6rn59fMURGREREpB0TrERERESklfK4o4DherAq366/adOmfONvAsDAgQMBAHK5HE+ePFFZJwiC2KPS1tYWHTp00Lo95f0oV64cPD09tZaXSCTo3bu3+HjLli3isre3t8ZJuZSTkNbW1hrbV6wzxMRRNWrUQI0aNQAAMpkMly5d0lnHy8tLpdfvrFmzcOrUKbVld+/ejYCAgCLHqU52djZ+++03dOjQAampqeLzc+fOhUQi0Vn/3Llz4nLXrl2LJUYiIiIibcyMHQARERERlW7KiUkTExOMHDmyQPUbNmyIpUuXamy3bdu2aN26tdq6VapUEZflcrnKuuDgYLH357vvvgsLCwutcSjvh75J4j59+uCnn34CkJsIVDBUL15D6t+/P5YtWwYAOHHihF7JxjVr1uDChQtISkpCZmYmunXrhuHDh6NPnz6oUKECXrx4gb179+LAgQMAgCFDhmDPnj0Fiuu///5TOxatTCZDQkICQkJCkJaWprJuwIABmDRpks62U1NTxWSymZmZyrAORERERCWFCVYiIiIi0kq5p6lcLseJEycKVL9mzZpa21X0UlUnISFBXK5YsaLKOuXhAXr27KkzDuX90DdB2qlTJ9jb24tDGShomyDL1tZWXE5LS4O9vb3acoqkop2dnV6x6DJy5EgxwXrw4EGsXLlSZx0PDw8cOXIE3bp1Q1paGuRyOXbu3ImdO3fmKztq1Cj4+/sXOMEaHx+v9zljbm6OuXPn4vPPP9fYQ1jZsWPHxHFxu3TpAhcXlwLFRkRERGQIHCKAiIiIiDSSy+XiRFSFpTwBkzJFj1JN64H/zVpfoUKFfOOZHj58GEDurfy6JrjKux/6JljNzc3RvXv3fM9rq68cZ2RkpMZyinWVK1fWKxZdGjZsiBYtWgAAQkND8c8//+hVr127drh+/brGIRacnZ2xdOlSbN++3SBxKkgkEtjZ2cHT0xN9+vTB999/j+fPn2PBggWQSqV6tbFr1y5xecKECQaNj4iIiEhfEkExxSkRERERUQmJjo4We6TevXsX9evXV1tu7Nix2LZtG8aMGaMyAdKLFy/g5uYGIHcyqitXrhR7zPpKSUmBvb09BEHAvn37MGDAALXlvLy8cPfuXcyePRvfffedQbb966+/YtiwYQCAcePGYePGjQWq/+jRI1y6dAkvX76Eg4MDPD090alTJ5ibmxskPkOKjo6Gm5sbsrOzUb16dTx69AgmJuw/QkRERCWP30CIiIiIqMQp367/6tUrtWWeP3+O3377DQDw8ccfq6xTHh6gV69ehg+wCGxtbdG8eXMAwPHjx9WWiYiIQEhICACgc+fOBtv2oEGDUKtWLQC5E1PFxcUVqH6tWrUwduxYzJkzBx999BG6detWKpOrALB+/XpxXNzPPvuMyVUiIiIyGn4LISIiIqISpzzhlOJWf2U5OTkYN24c0tPT0b9/fzRt2lRlvXIdfcZfLWkjRowAkJvkfP78eb71S5cuhSAIcHV1RceOHQ22XVNTU3z11VcAcieAWrduncHaLk0yMjKwZs0aALnjyL7//vtGjoiIiIjeZkywEhEREVGJU/RgdXJyQmBgIH7++WexN+Lt27fRrVs3nDx5Eu7u7mpvc2/fvj3mz5+PRYsW6T2eakkaP348qlevjtTUVPTs2VMc/zU9PR0BAQFicvDbb7/Ve7xRfQ0fPlychOuHH35AcnKyQdsvDdatW4fo6GgAwOLFiw1+DImIiIgKgmOwEhEREVGJq1u3Lh48eIDt27djwYIFePr0KczNzWFpaYmkpCQAuT0Tjx8/jjp16hg52sK5e/cuOnXqJCYC7e3tkZqaCplMBgCYMmUKVq1aVSzbvnTpEtq2bQsAmD9/PhYsWFAs2zGG5ORkVK9eHTExMWjTpg0uXrxo7JCIiIjoLccEKxERERGVqLS0NNjZ2UEul+PRo0ews7PD3Llz8eeffyIxMRE1atTAkCFDMH36dNja2ho73CKJjo5GQEAADh8+jOfPn8PGxga+vr6YPHky+vbta+zwiIiIiMgAmGAlIiIiIiIiIiIiKiSOwUpERERERERERERUSEywEhERERERERERERUSE6xEREREREREREREhcQEKxEREREREREREVEhMcFKREREREREREREVEhMsBIREREREREREREVEhOsRERERERERERERIXEBCsRERERERERERFRITHBSkRERERERERERFRITLASERERERERERERFdL/Ad2IbM5FzPgMAAAAAElFTkSuQmCC", + "image/png": 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\n", 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" ] @@ -528,7 +539,234 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Please keep in mind that the decoding complexity differs significantly and should be also included in a fair comparison as shown in Section [Throughput and Decoding Complexity](#Throughput-and-Decoding-Complexity).\n", + "Please keep in mind that the decoding complexity differs significantly and should be also included in a fair comparison as shown in Section [Throughput and Decoding Complexity](#Throughput-and-Decoding-Complexity)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Performance under Optimal Decoding\n", + "\n", + "The achievable error-rate performance of a coding scheme depends on the strength of the code construction and the performance of the actual decoding algorithm.\n", + "We now approximate the maximum-likelihood performance of all previous coding schemes by using the ordered statistic decoder (OSD) [12]." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Running: 5G LDPC BP-20\n", + "Note: Required memory complexity is large for the given code parameters and t=4. Please consider small batch-sizes to keep the inference complexity small and activate XLA mode if possible.\n", + "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", + "---------------------------------------------------------------------------------------------------------------------------------------\n", + " 0.0 | 1.0525e-01 | 4.6233e-01 | 40416 | 384000 | 1387 | 3000 | 4.2 |reached target block errors\n", + " 0.5 | 5.5930e-02 | 2.5625e-01 | 28636 | 512000 | 1025 | 4000 | 2.0 |reached target block errors\n", + " 1.0 | 2.4980e-02 | 1.1889e-01 | 28777 | 1152000 | 1070 | 9000 | 4.6 |reached target block errors\n", + " 1.5 | 8.3019e-03 | 4.1040e-02 | 26566 | 3200000 | 1026 | 25000 | 12.7 |reached target block errors\n", + " 2.0 | 2.1109e-03 | 1.1055e-02 | 24588 | 11648000 | 1006 | 91000 | 46.7 |reached target block errors\n", + " 2.5 | 3.8392e-04 | 2.1874e-03 | 22556 | 58752000 | 1004 | 459000 | 236.2 |reached target block errors\n", + " 3.0 | 4.9438e-05 | 3.2400e-04 | 6328 | 128000000 | 324 | 1000000 | 512.5 |reached max iter \n", + " 3.5 | 5.0078e-06 | 3.9000e-05 | 641 | 128000000 | 39 | 1000000 | 511.6 |reached max iter \n", + " 4.0 | 3.6719e-07 | 4.0000e-06 | 47 | 128000000 | 4 | 1000000 | 512.2 |reached max iter \n", + " 4.5 | 0.0000e+00 | 0.0000e+00 | 0 | 128000000 | 0 | 1000000 | 512.5 |reached max iter \n", + "\n", + "Simulation stopped as no error occurred @ EbNo = 4.5 dB.\n", + "\n", + "\n", + "Running: 5G Polar+CRC SC\n", + "Note: Required memory complexity is large for the given code parameters and t=4. Please consider small batch-sizes to keep the inference complexity small and activate XLA mode if possible.\n", + "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", + "---------------------------------------------------------------------------------------------------------------------------------------\n", + " 0.0 | 1.0706e-01 | 4.4833e-01 | 41110 | 384000 | 1345 | 3000 | 4.4 |reached target block errors\n", + " 0.5 | 5.7684e-02 | 2.4560e-01 | 36918 | 640000 | 1228 | 5000 | 2.5 |reached target block errors\n", + " 1.0 | 2.5269e-02 | 1.0990e-01 | 32344 | 1280000 | 1099 | 10000 | 5.1 |reached target block errors\n", + " 1.5 | 7.8858e-03 | 3.5276e-02 | 29272 | 3712000 | 1023 | 29000 | 14.8 |reached target block errors\n", + " 2.0 | 1.7343e-03 | 7.8976e-03 | 28192 | 16256000 | 1003 | 127000 | 64.9 |reached target block errors\n", + " 2.5 | 2.6134e-04 | 1.2516e-03 | 26728 | 102272000 | 1000 | 799000 | 408.2 |reached target block errors\n", + " 3.0 | 2.6187e-05 | 1.3300e-04 | 3352 | 128000000 | 133 | 1000000 | 510.5 |reached max iter \n", + " 3.5 | 1.7031e-06 | 8.0000e-06 | 218 | 128000000 | 8 | 1000000 | 510.0 |reached max iter \n", + " 4.0 | 0.0000e+00 | 0.0000e+00 | 0 | 128000000 | 0 | 1000000 | 510.5 |reached max iter \n", + "\n", + "Simulation stopped as no error occurred @ EbNo = 4.0 dB.\n", + "\n", + "\n", + "Running: Reed Muller (RM) SCL-8\n", + "Note: Required memory complexity is large for the given code parameters and t=4. Please consider small batch-sizes to keep the inference complexity small and activate XLA mode if possible.\n", + "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", + "---------------------------------------------------------------------------------------------------------------------------------------\n", + " 0.0 | 9.9979e-02 | 4.8533e-01 | 38392 | 384000 | 1456 | 3000 | 4.3 |reached target block errors\n", + " 0.5 | 5.8141e-02 | 3.0425e-01 | 29768 | 512000 | 1217 | 4000 | 2.0 |reached target block errors\n", + " 1.0 | 2.5547e-02 | 1.4088e-01 | 26160 | 1024000 | 1127 | 8000 | 4.1 |reached target block errors\n", + " 1.5 | 9.7431e-03 | 5.8222e-02 | 22448 | 2304000 | 1048 | 18000 | 9.2 |reached target block errors\n", + " 2.0 | 2.8170e-03 | 1.8182e-02 | 19832 | 7040000 | 1000 | 55000 | 28.1 |reached target block errors\n", + " 2.5 | 5.9362e-04 | 4.0732e-03 | 18692 | 31488000 | 1002 | 246000 | 125.7 |reached target block errors\n", + " 3.0 | 1.0056e-04 | 7.4500e-04 | 12872 | 128000000 | 745 | 1000000 | 510.3 |reached max iter \n", + " 3.5 | 1.3063e-05 | 9.8000e-05 | 1672 | 128000000 | 98 | 1000000 | 510.3 |reached max iter \n", + " 4.0 | 6.2500e-07 | 5.0000e-06 | 80 | 128000000 | 5 | 1000000 | 510.2 |reached max iter \n", + " 4.5 | 1.2500e-07 | 1.0000e-06 | 16 | 128000000 | 1 | 1000000 | 510.1 |reached max iter \n", + "\n", + "Running: Conv. Code Viterbi (constraint length 8)\n", + "Note: Required memory complexity is large for the given code parameters and t=4. Please consider small batch-sizes to keep the inference complexity small and activate XLA mode if possible.\n", + "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", + "---------------------------------------------------------------------------------------------------------------------------------------\n", + " 0.0 | 9.7660e-02 | 7.0150e-01 | 25001 | 256000 | 1403 | 2000 | 2.9 |reached target block errors\n", + " 0.5 | 6.5164e-02 | 5.5900e-01 | 16682 | 256000 | 1118 | 2000 | 1.0 |reached target block errors\n", + " 1.0 | 3.6641e-02 | 4.1567e-01 | 14070 | 384000 | 1247 | 3000 | 1.5 |reached target block errors\n", + " 1.5 | 1.9215e-02 | 2.7100e-01 | 9838 | 512000 | 1084 | 4000 | 2.0 |reached target block errors\n", + " 2.0 | 1.0513e-02 | 1.8833e-01 | 8074 | 768000 | 1130 | 6000 | 3.1 |reached target block errors\n", + " 2.5 | 5.0686e-03 | 1.1822e-01 | 5839 | 1152000 | 1064 | 9000 | 4.6 |reached target block errors\n", + " 3.0 | 2.7242e-03 | 7.8538e-02 | 4533 | 1664000 | 1021 | 13000 | 6.6 |reached target block errors\n", + " 3.5 | 1.4941e-03 | 5.1800e-02 | 3825 | 2560000 | 1036 | 20000 | 10.2 |reached target block errors\n", + " 4.0 | 7.7959e-04 | 3.0545e-02 | 3293 | 4224000 | 1008 | 33000 | 16.8 |reached target block errors\n", + " 4.5 | 4.3529e-04 | 1.8887e-02 | 2953 | 6784000 | 1001 | 53000 | 27.1 |reached target block errors\n", + "\n", + "Running: Turbo Code (constraint length 4)\n", + "Note: Required memory complexity is large for the given code parameters and t=4. Please consider small batch-sizes to keep the inference complexity small and activate XLA mode if possible.\n", + "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", + "---------------------------------------------------------------------------------------------------------------------------------------\n", + " 0.0 | 1.0087e-01 | 5.0400e-01 | 25823 | 256000 | 1008 | 2000 | 3.1 |reached target block errors\n", + " 0.5 | 6.4128e-02 | 3.4400e-01 | 24625 | 384000 | 1032 | 3000 | 1.5 |reached target block errors\n", + " 1.0 | 3.0613e-02 | 1.7683e-01 | 23511 | 768000 | 1061 | 6000 | 3.1 |reached target block errors\n", + " 1.5 | 1.2736e-02 | 8.1692e-02 | 21193 | 1664000 | 1062 | 13000 | 6.7 |reached target block errors\n", + " 2.0 | 3.9779e-03 | 2.9500e-02 | 17312 | 4352000 | 1003 | 34000 | 17.4 |reached target block errors\n", + " 2.5 | 1.0436e-03 | 1.0192e-02 | 13225 | 12672000 | 1009 | 99000 | 50.7 |reached target block errors\n", + " 3.0 | 2.3167e-04 | 3.0895e-03 | 9608 | 41472000 | 1001 | 324000 | 165.9 |reached target block errors\n", + " 3.5 | 7.3588e-05 | 1.2706e-03 | 7413 | 100736000 | 1000 | 787000 | 402.9 |reached target block errors\n", + " 4.0 | 2.3914e-05 | 4.7400e-04 | 3061 | 128000000 | 474 | 1000000 | 511.9 |reached max iter \n", + " 4.5 | 7.0391e-06 | 1.5300e-04 | 901 | 128000000 | 153 | 1000000 | 512.1 |reached max iter \n" + ] + } + ], + "source": [ + "# overwrite existing legend entries for OSD simulations\n", + "legends = [\"5G LDPC\", \"5G Polar+CRC\", \"5G Polar+CRC\", \"RM\", \"Conv. Code\", \"Turbo Code\"]\n", + "\n", + "# run ber simulations for each code we have added to the list\n", + "for idx, code in enumerate(codes_under_test):\n", + "\n", + " if idx==2: # skip second polar code (same code only different decoder)\n", + " continue \n", + "\n", + " print(\"\\nRunning: \" + code[2])\n", + " \n", + " # initialize encoder\n", + " encoder = code[0]\n", + " # encode dummy bits to init conv encoders (otherwise k is not defined)\n", + " encoder(tf.zeros((1, k))) \n", + "\n", + " # OSD can be directly associated to an encoder\n", + " decoder = OSDecoder(encoder=encoder, t=4) \n", + "\n", + " # generate a new model with the given encoder/decoder\n", + " model = System_Model(k=k,\n", + " n=n,\n", + " num_bits_per_symbol=num_bits_per_symbol,\n", + " encoder=encoder,\n", + " decoder=decoder, \n", + " cw_estimates=True) # OSD returns codeword estimates and not info bit estimates\n", + " \n", + " # the first argument must be a callable (function) that yields u and u_hat for batch_size and ebno\n", + " ber_plot128.simulate(tf.function(model, jit_compile=True), \n", + " ebno_dbs=ebno_db, # SNR to simulate\n", + " legend=legends[idx]+f\" OSD-{decoder.t} \", # legend string for plotting\n", + " max_mc_iter=1000, # run 100 Monte Carlo runs per SNR point\n", + " num_target_block_errors=1000, # continue with next SNR point after 1000 bit errors\n", + " batch_size=1000, # batch-size per Monte Carlo run\n", + " soft_estimates=False, # the model returns hard-estimates\n", + " early_stop=True, # stop simulation if no error has been detected at current SNR point\n", + " show_fig=False, # we show the figure after all results are simulated\n", + " add_bler=True, # in case BLER is also interesting\n", + " forward_keyboard_interrupt=True); # should be True in a loop\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And let's plot the results. \n", + "\n", + "*Remark*: we define a custom plotting function to enable a nicer visualization of OSD vs. non-OSD results." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# for simplicity, we only plot a subset of the simulated curves \n", + "# focus on BLER\n", + "plots_to_show = ['5G LDPC BP-20 (BLER)', '5G LDPC OSD-4 (BLER)', '5G Polar+CRC SCL-8 (BLER)', '5G Polar+CRC OSD-4 (BLER)', 'Reed Muller (RM) SCL-8 (BLER)', 'RM OSD-4 (BLER)', 'Conv. Code Viterbi (constraint length 8) (BLER)', 'Conv. Code OSD-4 (BLER)', 'Turbo Code (constraint length 4) (BLER)', 'Turbo Code OSD-4 (BLER)']\n", + "\n", + "# find indices of relevant curves\n", + "idx = []\n", + "for p in plots_to_show:\n", + " for i,l in enumerate(ber_plot128._legends):\n", + " if p==l:\n", + " idx.append(i)\n", + "\n", + "# generate new figure\n", + "fig, ax = plt.subplots(figsize=(16,12))\n", + "plt.xticks(fontsize=18)\n", + "plt.yticks(fontsize=18)\n", + "plt.title(f\"Performance under Ordered Statistic Decoding (k={k},n={n})\", fontsize=25)\n", + "plt.grid(which=\"both\")\n", + "plt.xlabel(r\"$E_b/N_0$ (dB)\", fontsize=25)\n", + "plt.ylabel(r\"BLER\", fontsize=25)\n", + "\n", + "# plot pairs of BLER curves (non-osd vs. osd)\n", + "for i in range(int(len(idx)/2)):\n", + "\n", + " # non-OSD\n", + " plt.semilogy(ebno_db,\n", + " ber_plot128._bers[idx[2*i]],\n", + " c='C%d'%(i),\n", + " label=ber_plot128._legends[idx[2*i]].replace(\" (BLER)\", \"\"), #remove \"(BLER)\" from label\n", + " linewidth=2)\n", + " # OSD\n", + " plt.semilogy(ebno_db,\n", + " ber_plot128._bers[idx[2*i+1]],\n", + " c='C%d'%(i),\n", + " label= ber_plot128._legends[idx[2*i+1]].replace(\" (BLER)\", \"\"), #remove \"(BLER)\" from label\n", + " linestyle = \"--\",\n", + " linewidth=2)\n", + "\n", + "plt.legend(fontsize=20)\n", + "plt.xlim([0, 4.5])\n", + "plt.ylim([1e-4, 1]);\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As can be seen, the performance of Polar and Convolutional codes is in practice close to their ML performance.\n", + "For other codes such as LDPC codes, there is a practical performance gap under BP decoding which tends to be smaller for longer codes." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Performance of Longer LDPC Codes\n", "\n", "Now, let us have a look at the performance gains due to longer codewords. \n", "For this, we scale the length of the LDPC code and compare the results (same rate, same decoder, same channel)." @@ -1525,7 +1763,9 @@ "\n", "[10] S. Cammerer, B. Leible, M. Stahl, J. Hoydis, and S ten Brink, \"Combining Belief Propagation and Successive Cancellation List Decoding of Polar Codes on a GPU Platform,\" IEEE ICASSP, 2017.\n", "\n", - "[11] V. Bioglio, F. Gabry, I. Land, \"Low-complexity puncturing and shortening of polar codes,\" IEEE Wireless Communications and Networking Conference Workshops (WCNCW), 2017." + "[11] V. Bioglio, F. Gabry, I. Land, \"Low-complexity puncturing and shortening of polar codes,\" IEEE Wireless Communications and Networking Conference Workshops (WCNCW), 2017.\n", + "\n", + "[12] M. Fossorier, S. Lin, \"Soft-Decision Decoding of Linear Block Codes Based on Ordered Statistics\", IEEE Transactions on Information Theory, vol. 41, no. 5, 1995." ] } ], diff --git a/examples/OFDM_MIMO_Detection.ipynb b/examples/OFDM_MIMO_Detection.ipynb new file mode 100644 index 00000000..e248f07e --- /dev/null +++ b/examples/OFDM_MIMO_Detection.ipynb @@ -0,0 +1,1557 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c0222cc3-b667-4784-9b43-ca3129793c96", + "metadata": {}, + "source": [ + "# OFDM MIMO Channel Estimation and Detection" + ] + }, + { + "cell_type": "markdown", + "id": "220ef1ff-de24-451a-83ce-28cf0608de9d", + "metadata": {}, + "source": [ + "In this notebook, we will evaluate some of the OFDM channel estimation and MIMO detection algorithms available in Sionna.\n", + "\n", + "We will start by evaluating the mean square error (MSE) preformance of various channel estimation and interpolation methods.\n", + "\n", + "Then, we will compare some of the MIMO detection algorithms under both perfect and imperfect channel state information (CSI) in terms of uncoded symbol error rate (SER) and coded bit error rate (BER).\n", + "\n", + "The developed end-to-end Keras models in this notebook are a great tool for benchmarking of MIMO receivers under realistic conditions. They can be easily extended to new channel estimation methods or MIMO detection algorithms." + ] + }, + { + "cell_type": "markdown", + "id": "d4a374f2-c8f3-45b7-9fe5-89123decb076", + "metadata": {}, + "source": [ + "For MSE evaluations, the block diagram of the system looks as follows:" + ] + }, + { + "cell_type": "markdown", + "id": "1151e856-875a-4c6a-8291-e1c00313eb25", + "metadata": {}, + "source": [ + "![SER](data:image/png;base64,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)" + ] + }, + { + "cell_type": "markdown", + "id": "7158bf5c-afb3-42f6-a5db-c928cb3b0422", + "metadata": {}, + "source": [ + "where the channel estimation module is highlighted as it is the focus of this evaluation. The channel covariance matrices are required for linear minimum mean square error (LMMSE) channel interpolation." + ] + }, + { + "cell_type": "markdown", + "id": "627b6480-7c04-42ad-98eb-2c4fc14d369e", + "metadata": {}, + "source": [ + "For uncoded SER evaluations, the block diagram of the system looks as follows:" + ] + }, + { + "cell_type": "markdown", + "id": "c9d2cbb9-499d-4615-b34c-13cad61228b1", + "metadata": {}, + "source": [ + 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)" + ] + }, + { + "cell_type": "markdown", + "id": "53fc4e24-26e0-4812-a45f-b55f3b68042f", + "metadata": {}, + "source": [ + "where the channel estimation and detection modules are highlighted as they are the focus of this evaluation." + ] + }, + { + "cell_type": "markdown", + "id": "f622bbb9-9078-4881-8462-2eb69c1122d6", + "metadata": {}, + "source": [ + "Finally, for coded BER evaluations, the block diagram of the system looks as follows:" + ] + }, + { + "cell_type": "markdown", + "id": "4ff3e463-d95d-4816-9ae9-c0c185bf6b18", + "metadata": {}, + "source": [ + "![BER](data:image/png;base64,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)" + ] + }, + { + "cell_type": "markdown", + "id": "8123dc2f-f759-4615-a137-f10a1c5ef062", + "metadata": {}, + "source": [ + "## Table of Contents\n", + "* [GPU Configuration and Imports](#GPU-Configuration-and-Imports)\n", + "* [Simulations parameters](#Simulation-parameters)\n", + "* [Estimation of the channel time, frequency, and spatial covariance matrices](#Estimation-of-the-channel-time,-frequency,-and-spatial-covariance-matrices)\n", + "* [Loading the channel covariance matrices](#Loading-the-channel-covariance-matrices)\n", + "* [Comparison of OFDM estimators](#Comparison-of-OFDM-estimators)\n", + "* [Comparison of MIMO detectors](#Comparison-of-MIMO-detectors)" + ] + }, + { + "cell_type": "markdown", + "id": "6fd7faab-7720-4039-9a16-65d53233dbb1", + "metadata": {}, + "source": [ + "## GPU Configuration and Imports " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "ffb6a229", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of GPUs available : 2\n", + "Only GPU number 0 used.\n" + ] + } + ], + "source": [ + "# Configure the notebook to use only a single GPU and allocate only as much memory as needed\n", + "# For more details, see https://www.tensorflow.org/guide/gpu\n", + "import tensorflow as tf\n", + "gpus = tf.config.list_physical_devices('GPU')\n", + "print('Number of GPUs available :', len(gpus))\n", + "if gpus:\n", + " gpu_num = 0 # Number of the GPU to be used\n", + " try:\n", + " tf.config.set_visible_devices(gpus[gpu_num], 'GPU')\n", + " print('Only GPU number', gpu_num, 'used.')\n", + " tf.config.experimental.set_memory_growth(gpus[gpu_num], True)\n", + " except RuntimeError as e:\n", + " print(e)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "9d3ff139", + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pickle\n", + "\n", + "from tensorflow.keras import Model\n", + "\n", + "# Import Sionna\n", + "try:\n", + " import sionna\n", + "except ImportError as e:\n", + " # Install Sionna if package is not already installed\n", + " import os\n", + " os.system(\"pip install sionna\")\n", + " import sionna\n", + "\n", + "from sionna.mimo import StreamManagement\n", + "from sionna.utils import QAMSource, compute_ser, BinarySource, sim_ber, ebnodb2no, QAMSource\n", + "from sionna.mapping import Mapper\n", + "from sionna.ofdm import ResourceGrid, ResourceGridMapper, LSChannelEstimator, LMMSEInterpolator, LinearDetector, KBestDetector, EPDetector, MMSEPICDetector\n", + "from sionna.channel import GenerateOFDMChannel, OFDMChannel, gen_single_sector_topology\n", + "from sionna.channel.tr38901 import UMi, Antenna, PanelArray\n", + "from sionna.fec.ldpc import LDPC5GEncoder\n", + "from sionna.fec.ldpc import LDPC5GDecoder" + ] + }, + { + "cell_type": "markdown", + "id": "81ead318-3f42-48b0-b140-62dcbbb7baff", + "metadata": {}, + "source": [ + "## Simulation parameters" + ] + }, + { + "cell_type": "markdown", + "id": "575c8802-bc4c-4db7-bb96-67c897735d00", + "metadata": {}, + "source": [ + "The next cell defines the simulation parameters used throughout this notebook.\n", + "\n", + "This includes the OFDM waveform parameters, [antennas geometries and patterns](https://nvlabs.github.io/sionna/api/channel.wireless.html#sionna.channel.tr38901.PanelArray), and the [3GPP UMi channel model](https://nvlabs.github.io/sionna/api/channel.wireless.html#sionna.channel.tr38901.UMi)." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c770ffeb-5396-4c2f-af31-4b4d82a805ae", + "metadata": {}, + "outputs": [], + "source": [ + "NUM_OFDM_SYMBOLS = 14\n", + "FFT_SIZE = 12*4 # 4 PRBs\n", + "SUBCARRIER_SPACING = 30e3 # Hz\n", + "CARRIER_FREQUENCY = 3.5e9 # Hz\n", + "SPEED = 3. # m/s\n", + "\n", + "# The user terminals (UTs) are equipped with a single antenna\n", + "# with vertial polarization.\n", + "UT_ANTENNA = Antenna(polarization='single',\n", + " polarization_type='V',\n", + " antenna_pattern='omni', # Omnidirectional antenna pattern\n", + " carrier_frequency=CARRIER_FREQUENCY)\n", + "\n", + "# The base station is equipped with an antenna\n", + "# array of 8 cross-polarized antennas,\n", + "# resulting in a total of 16 antenna elements.\n", + "NUM_RX_ANT = 16\n", + "BS_ARRAY = PanelArray(num_rows_per_panel=4,\n", + " num_cols_per_panel=2,\n", + " polarization='dual',\n", + " polarization_type='cross',\n", + " antenna_pattern='38.901', # 3GPP 38.901 antenna pattern\n", + " carrier_frequency=CARRIER_FREQUENCY)\n", + "\n", + "# 3GPP UMi channel model is considered\n", + "CHANNEL_MODEL = UMi(carrier_frequency=CARRIER_FREQUENCY,\n", + " o2i_model='low',\n", + " ut_array=UT_ANTENNA,\n", + " bs_array=BS_ARRAY,\n", + " direction='uplink',\n", + " enable_shadow_fading=False,\n", + " enable_pathloss=False)" + ] + }, + { + "cell_type": "markdown", + "id": "a07a86d6-73e8-457d-b44c-98753023ca52", + "metadata": {}, + "source": [ + "## Estimation of the channel time, frequency, and spatial covariance matrices" + ] + }, + { + "cell_type": "markdown", + "id": "4682a1c0-4c2f-41b0-b244-d178d06c1264", + "metadata": {}, + "source": [ + "The linear minimum mean square (LMMSE) interpolation method requires knowledge of the time (i.e., across OFDM symbols), frequency (i.e., across sub-carriers), and spatial (i.e., across receive antennas) covariance matrices of the channel frequency response.\n", + "\n", + "These are estimated in this section using Monte Carlo sampling.\n", + "\n", + "We explain below how this is achieved for the frequency covariance matrix. The same approach is used for the time and spatial covariance matrices.\n", + "\n", + "Let $N$ be the number of sub-carriers.\n", + "The first step for estimating the frequency covariance matrix is to sample the channel model in order to build a set of frequency-domain channel realizations $\\left\\{ \\mathbf{h}_k \\right\\}, 1 \\leq k \\leq K$, where $K$ is the number of samples and $\\mathbf{h}_k \\in \\mathbb{C}^{N}$ are complex-valued samples of the channel frequency response.\n", + "\n", + "The frequency covariance matrix $\\mathbf{R}^{(f)} \\in \\mathbb{C}^{N \\times N}$ is then estimated by\n", + "\n", + "\\begin{equation}\n", + "\\mathbf{R}^{(f)} \\approx \\frac{1}{K} \\sum_{k = 1}^K \\mathbf{h}_k \\mathbf{h}_k^{\\mathrm{H}}\n", + "\\end{equation}\n", + "\n", + "where we assume that the frequency-domain channel response has zero mean.\n", + "\n", + "The following cells implement this process for all three dimensions (frequency, time, and space).\n" + ] + }, + { + "cell_type": "markdown", + "id": "0c6fdb83-e5b9-4521-a15a-59bfe337f041", + "metadata": {}, + "source": [ + "The next cell defines a [resource grid](https://nvlabs.github.io/sionna/api/ofdm.html#sionna.ofdm.ResourceGrid) and an [OFDM channel generator](https://nvlabs.github.io/sionna/api/channel.wireless.html#sionna.channel.GenerateOFDMChannel) for sampling the channel in the frequency domain." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "0ae51854-9597-409d-8ecf-a1d85e02b3cd", + "metadata": {}, + "outputs": [], + "source": [ + "rg = ResourceGrid(num_ofdm_symbols=NUM_OFDM_SYMBOLS,\n", + " fft_size=FFT_SIZE,\n", + " subcarrier_spacing=SUBCARRIER_SPACING)\n", + "channel_sampler = GenerateOFDMChannel(CHANNEL_MODEL, rg)" + ] + }, + { + "cell_type": "markdown", + "id": "65802f06-150a-4e91-a68b-05fa44aa3eaf", + "metadata": {}, + "source": [ + "Then, a function that samples the channel is defined.\n", + "It randomly samples a network topology for every batch and for every batch example using the [appropriate utility function](https://nvlabs.github.io/sionna/api/channel.wireless.html#sionna.channel.gen_single_sector_topology)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5a71816c-bd1e-4789-9ea1-db6f5cf1525c", + "metadata": {}, + "outputs": [], + "source": [ + "def sample_channel(batch_size):\n", + " # Sample random topologies\n", + " topology = gen_single_sector_topology(batch_size, 1, 'umi', min_ut_velocity=SPEED, max_ut_velocity=SPEED)\n", + " CHANNEL_MODEL.set_topology(*topology)\n", + " \n", + " # Sample channel frequency responses\n", + " # [batch size, 1, num_rx_ant, 1, 1, num_ofdm_symbols, fft_size]\n", + " h_freq = channel_sampler(batch_size)\n", + " # [batch size, num_rx_ant, num_ofdm_symbols, fft_size]\n", + " h_freq = h_freq[:,0,:,0,0]\n", + " \n", + " return h_freq" + ] + }, + { + "cell_type": "markdown", + "id": "2bd07c16-595e-4880-b2aa-e162e75f9d55", + "metadata": {}, + "source": [ + "We now define a function that estimates the frequency, time, and spatial covariance matrcies using Monte Carlo sampling." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "1cc6d3e1-9abb-45f4-ad66-27fa3ff5654a", + "metadata": {}, + "outputs": [], + "source": [ + "@tf.function(jit_compile=True) # Use XLA for speed-up \n", + "def estimate_covariance_matrices(num_it, batch_size):\n", + " freq_cov_mat = tf.zeros([FFT_SIZE, FFT_SIZE], tf.complex64)\n", + " time_cov_mat = tf.zeros([NUM_OFDM_SYMBOLS, NUM_OFDM_SYMBOLS], tf.complex64)\n", + " space_cov_mat = tf.zeros([NUM_RX_ANT, NUM_RX_ANT], tf.complex64)\n", + " for _ in tf.range(num_it):\n", + " # [batch size, num_rx_ant, num_ofdm_symbols, fft_size]\n", + " h_samples = sample_channel(batch_size)\n", + " \n", + " #################################\n", + " # Estimate frequency covariance\n", + " #################################\n", + " # [batch size, num_rx_ant, fft_size, num_ofdm_symbols]\n", + " h_samples_ = tf.transpose(h_samples, [0,1,3,2])\n", + " # [batch size, num_rx_ant, fft_size, fft_size]\n", + " freq_cov_mat_ = tf.matmul(h_samples_, h_samples_, adjoint_b=True)\n", + " # [fft_size, fft_size]\n", + " freq_cov_mat_ = tf.reduce_mean(freq_cov_mat_, axis=(0,1))\n", + " # [fft_size, fft_size]\n", + " freq_cov_mat += freq_cov_mat_ \n", + " \n", + " ################################\n", + " # Estimate time covariance\n", + " ################################\n", + " # [batch size, num_rx_ant, num_ofdm_symbols, fft_size]\n", + " time_cov_mat_ = tf.matmul(h_samples, h_samples, adjoint_b=True)\n", + " # [num_ofdm_symbols, num_ofdm_symbols]\n", + " time_cov_mat_ = tf.reduce_mean(time_cov_mat_, axis=(0,1))\n", + " # [num_ofdm_symbols, num_ofdm_symbols]\n", + " time_cov_mat += time_cov_mat_\n", + " \n", + " ###############################\n", + " # Estimate spatial covariance\n", + " ###############################\n", + " # [batch size, num_ofdm_symbols, num_rx_ant, fft_size]\n", + " h_samples_ = tf.transpose(h_samples, [0,2,1,3])\n", + " # [batch size, num_ofdm_symbols, num_rx_ant, num_rx_ant]\n", + " space_cov_mat_ = tf.matmul(h_samples_, h_samples_, adjoint_b=True)\n", + " # [num_rx_ant, num_rx_ant]\n", + " space_cov_mat_ = tf.reduce_mean(space_cov_mat_, axis=(0,1))\n", + " # [num_rx_ant, num_rx_ant]\n", + " space_cov_mat += space_cov_mat_\n", + " \n", + " freq_cov_mat /= tf.complex(tf.cast(NUM_OFDM_SYMBOLS*num_it, tf.float32), 0.0)\n", + " time_cov_mat /= tf.complex(tf.cast(FFT_SIZE*num_it, tf.float32), 0.0)\n", + " space_cov_mat /= tf.complex(tf.cast(FFT_SIZE*num_it, tf.float32), 0.0)\n", + "\n", + " return freq_cov_mat, time_cov_mat, space_cov_mat" + ] + }, + { + "cell_type": "markdown", + "id": "d5e444cb-58d7-42c7-9e49-cdb6f9eb4aaa", + "metadata": {}, + "source": [ + "We then compute the estimates by executing the function defined in the previous cell.\n", + "\n", + "The batch size and number of iterations determine the total number of samples, i.e.,\n", + "\n", + "```\n", + "number of samples = batch_size x num_iterations\n", + "```\n", + "\n", + "and hence control the tradeoff between the accuracy of the estimates and the time needed for their computation." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "8ea94466-bb28-4ed5-a7cb-6e1cf82f86c0", + "metadata": {}, + "outputs": [], + "source": [ + "batch_size = 1000\n", + "num_iterations = 100\n", + "\n", + "sionna.Config.xla_compat = True # Enable Sionna's support of XLA\n", + "FREQ_COV_MAT, TIME_COV_MAT, SPACE_COV_MAT = estimate_covariance_matrices(batch_size, num_iterations)\n", + "sionna.Config.xla_compat = False # Disable Sionna's support of XLA" + ] + }, + { + "cell_type": "markdown", + "id": "0ddaf455-cae8-4248-8979-a9428c4298d6", + "metadata": {}, + "source": [ + "Finally, the estimated matrices are saved (as numpy arrays) for future use." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "6048932c-486a-419f-81c2-62b1bc6c9ff5", + "metadata": {}, + "outputs": [], + "source": [ + "# FREQ_COV_MAT : [fft_size, fft_size]\n", + "# TIME_COV_MAT : [num_ofdm_symbols, num_ofdm_symbols]\n", + "# SPACE_COV_MAT : [num_rx_ant, num_rx_ant]\n", + "\n", + "np.save('freq_cov_mat', FREQ_COV_MAT.numpy())\n", + "np.save('time_cov_mat', TIME_COV_MAT.numpy())\n", + "np.save('space_cov_mat', SPACE_COV_MAT.numpy())" + ] + }, + { + "cell_type": "markdown", + "id": "2770ca3d-19aa-4337-ab2b-8b184f2a0ddd", + "metadata": {}, + "source": [ + "## Loading the channel covariance matrices" + ] + }, + { + "cell_type": "markdown", + "id": "d4bab810-fc78-4a0c-bac4-f1da9f2bc07d", + "metadata": {}, + "source": [ + "The next cell loads saved estimates of the time, frequency, and space covariance matrices." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "b20bce1a-9a6c-4842-aaad-3b659931cb6e", + "metadata": {}, + "outputs": [], + "source": [ + "FREQ_COV_MAT = np.load('freq_cov_mat.npy')\n", + "TIME_COV_MAT = np.load('time_cov_mat.npy')\n", + "SPACE_COV_MAT = np.load('space_cov_mat.npy')" + ] + }, + { + "cell_type": "markdown", + "id": "c1ee48c1-6817-4c40-9b7f-447c5a34c9b8", + "metadata": {}, + "source": [ + "We then visualize the loaded matrices.\n", + "\n", + "As one can see, the frequency correlation slowly decays with increasing spectral distance.\n", + "\n", + "The time-correlation is much stronger as the mobility low. The covariance matrix is hence very badly conditioned with rank almost equal to one.\n", + "\n", + "The spatial covariance matrix has a regular structure which is determined by the array geometry and polarization of its elements." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "7e316b0f-bbfd-4c75-9204-9a0cd49aa63b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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6bWhWvRM4o3n+883yfD0MOJRe79fDgUOa9bO9T5I8q2/45mbgQc17kCbRSowN/b7d9/w24Pqq2tO3DL04QpInJvlUkhubc/Ak7o4Nx7PvOet/Lk0cG2eLqz+gXgv8QV+gXl9VB1fV+fSGDDYkSV/5e87zGNfTGzq4zzTbvkkv8APQ7H8TvW/IAO8FTkmyEfgZWgbg6nkP8Eng5c3qGd9nkhOAvwReCBxdVeuBL9MbxpBWkomODW0lOQB4P/BHwLFNbLiQu2PDdmBj30s2LWZ9pOVm42zp/CXw/CQPT88hSZ6c5DB6jZvdwK8nWZvkvwAnz2enzTfec4HXJjk+yeokP94Eu/cAT07y2CRrgRcDd9AbFqGqvkMvH+Wv6OV+XL7A9/Ya4JeT/OAc7/MQev+UvgO9BGB6PWfSSjbJsWG+1tHLhfsOsDvJE4En9G1/D/CcJD+c5GB6w7LSxLJxtkSqaiu9BNg3ADfRS759drNtF/BfmuUb6Q0V/s3e1ya5Z3r3SJrpG/NLgC8Bn2le/3+AVVX1VeCZwJ/R+xb9VHqX9u/qe+07gcfR9804ye8m+ccW7+1LwMeB357jfV4G/DG9fzjfBn6U3pWe0oo1ybFhvqrqFuDX6TXCbqI3lHpB3/Z/BP4U+Ci98/OpZtMdo66L1AXZN5VBXZHkPOC6qvofy10XSd1hbIDmth9fpnch0u7lro80avacSZI6L8nPJDkgyZH0egD/zoaZJpWNM0nSOPgVeveD+xqwB/jV5a2OtHgc1pQkSeqQoXrOkpya5KtJrkxy9qgqJWkyGCMkqb0F95wlWQ38B727R19H72qgM5or8qZ19FGratOmmef5HazL1MD2qYHbYe23XLNv3zPQFp2qgeWW+2v7+tpvO3Nsn/317Pf6geX9Xj/79kH7fTLmKt/6ozT7+5m7QiMuv5+W9ZvDrmuvu76q7jHcXsbHQmLEujUH10Hr1t+1XKsHPsMZ+J0MfL2sVYPlByvF7NsHzfX6ucrPxTv8TZYhY8Tt315ZMUIzm7mlNLeTgSur6iqAJO8CTgNmDLybNq3hogtnvhn8nQP/3W8f+KDvrNX7LN9a+1Z/59QBA9vX7bN8y56D9t0+UH7w9Tun1s2+vGff5dsGlu+YWjOwfe0+y7um9n0/u/bsW/72geU7B8rvmVo1sH1gec9g+X3/E+we2D7YGNyzZ6DxOfD6weUaOP5+jcOB1vZ+jcP99jfHf8K22wft11rddzFz7X+/5X3LZ2D71S96yXzv7D4pWseIg9at5xEP+OW7lvccsu/f1O6DBj7TBw4uD3yG1w58ZtcObt/3+AMhhqk1szcGB/4kp2ksDiwPfiTn2j7XR3jUjbth97fEWTKDf2Nzall+5PufY/tlf/hbKy1GaAbDDGtuYN8pNK7j7qk/7pLkrCRbk2y94YbBvjBJE6x1jNi1e+eSVU6SumrRr9asqi1VtbmqNh99tBeHStpXf4xYt+bg5a6OJC27YYY1t7Hv/GYbuXtetgVZO5hPsl8f8B5mNdj2G+yoGxyCWHEGT8C+53NwmJPVgydw9sb11OAJHxjmZNXgMOa+v9/BIaD9Pg2Dw4yrBj4fbbe3zTea08D7MaGofYyYmiK33n7X4v5/suv2W7Ovtn/kg3mp+1o18DsdHOacK+Ts9wkYHMYceMFgCBz8mxjc4eCw28iHOduaK4SP2OD7nXMYsmX9Rr7/JT4/Gl/DdGV9Bjgxyb2TrANOp2+6DUkrnjFCkhZgwT1nVbU7yQuBD9H7unpuVV06sppJGmvGCElamGGGNamqC4ELR1QXSRPGGCFJ7Q3VOGurqva5Xcb+OWb7MgdtsZmDNtsBaqD8frfWmJMJJa1NFdl5d87ZYBLWxOegDZYfvP3MYuegzZUj1ZY5aLPvf7lzBNVZXj4pSZLUITbOJEmSOsTGmSRJUocsac7ZFINTMu07AG8O2nIzB222Awyfg6Y5TU1RO2+7a3GuM7zictDmmE/XHLSB3Y9bDprUsOdMkiSpQ2ycSZIkdYiNM0mSpA5Z4pyzsLP6cz4GMzjMQesWc9BmO8BgDtrgx8+ctAWYmqJuu23Gzfud0dr3pE98DtrgXJxz5KiZgzaw+znqu1/O2Fzvb47z2ToHTWrYcyZJktQhNs4kSZI6xMaZJElShyx5ztmtNdshzUHrNnPQZj3A4PEGX685VRVTt989t+Zc3x67dh+0qf1+5Uucg7ZfXuTsOxx5Dtqgtvtb5vuAjfq+Za33JzXsOZMkSeoQG2eSJEkdYuNMkiSpQ5Y256zCzqkD7l4xZ9PQHLRuW+E5aHPdVEpD688/gw7koM2ZM9Tuxlg1sPtVgzlsc+19rrk4B3aw6Dlog4a9L5o5aFqh7DmTJEnqkCXtOZMkSZPtp3/qkLrhxjlGppbQZ794x4eq6tTlrkcbNs4kSdLI3HDjHv79Q/dc7mrcZfVxVxyz3HVoaxnuc7auf8W+FjkHbfXA9rUDrz94heegrRpIgLij9cdjheWg7WfUEw9q0NA5aAMxYKnvgzZo8C9g8DNkDlq3LHYOmrSXPWeSJGlkimm+TKsVLwiQJEnqEHvOJEnSCBV7BucWUytL2jjbwypu2XPQ3SsG0zdGnIM2mGM2uLt1c9wH7cAVnoM2NXRChDloWlyduw/anMxBm71Cc5Rf5vueDRp1Dpq0lz1nkiRpZHo5Z7Y8h2HOmSRJUofYOJMkSeqQJZ5bcxW39s+tOWjEOWiD9zEbzDFrm4PmXJzDWuk5aFps5qDNsXdz0BbV0DloE8RbaQzHnDNJkrSiJTkXeAqwo6oeNM323wZ+oVlcA/wwcI+qujHJ1cAt9HocdlfV5mHrY+NMkiSNTFHsqbG7IOA84A3A26bbWFV/CPwhQJKnAr9ZVTf2Ffmpqrp+VJWZc6AwyblJdiT5ct+6o5JcnOSK5ueRo6qQpPFjnJA0zqrq48CNcxbsOQM4fxGrM6+es/PYvzV5NvDhqnpNkrOb5d+Za0dThJ2z5ZwNGjIHbf+5MruVg7Zn1Uq/HsMctAlyHiOKE6PUOgdtrvl591tjDto+i+ag7Xv4tjlo6rwkBwOnAi/sW13ARUkK+Iuq2jLsceZsHczQmjwNeGvz/K3A04etiKTxZZyQ1G+K6swDOCbJ1r7HWUO8tacC/zowpPmoqnoY8ETgBUl+coj9AwvPOTu2qrY3z78FHDtTweYknAVw5HEHLvBwksbQvOJEf4w4kIOXqGqSVpDrR5Gk3zidgSHNqtrW/NyR5APAycDHhznI0ONqVVXM0nlcVVuqanNVbT70qLXDHk7SGJotTvTHiLW0SHuQ1EkF7KE68xiVJEcAjwY+2LfukCSH7X0OPAH48vR7mL+F9px9O8lxVbU9yXHAjvm8aKrCzqm5cjRmMWQO2v5zZS5zDpoGTHgO2sqzoDixmLwP2hx7NwdtUZmD1l1JzgdOoTcEeh3wCmAtQFW9qSn2M8BFVXVr30uPBT7Q5KuuAd5ZVf80bH0W2ji7ADgTeE3z84OzF5e0AhknpBVq3ObWrKoz5lHmPHoXP/Wvuwp4yKjrM59baZwPfBK4f5LrkjyXXrB9fJIrgMc1y5JWKOOEJI3OnD1ns7QmHzviukgaU8YJSRqdpZ1bkyFzzgaNeC5Oc9C6ZrgctP3vWbXvCW+dgzZwx+u50l/2y0FT55iDNsfe58pBG8wpG3y9OWizGvr9d1TBOM4Q0Ck2DyRJkjrEuTUlSdJI7d+LqzbsOZMkSeqQpc05q1Xs3DPCnLNBE56DtnrwJkMrTssctP3O5+wfiDlz0AYTVla1y0FT95mDNsfeB3PQBl6QOXLUzEFbGWrEN39diew5kyRJ6hAbZ5IkSR3iBQGSJGl0CvY4qjmUJb/P2W2LmXM2aNJy0Abr3zY9ZZlNjTwLa7xy0DR+zEGbY+/moEmLwp4zSZI0MoW30hiWOWeSJEkdYs+ZJEkaobDHmwkNZYnvcxbumFrG9mDHc9BWrbActNHrdg6axl/rHLQ55hfsWg7aYI7XXCFn7HPQ5ji+OWhaLg5rSpIkdYjDmpIkaWQKmLKXcSj2nEmSJHXIkvacFeG2PWuX8pCz63gO2sHmoA2pYzlomjjjfh+0/XPM9o1BU2tmj2Fjn4M2aK77opmDNm9eEDAce84kSZI6xMaZJElSh3hBgCRJGpnCYc1hLfF9zmDXVHcTofasmqMj0Ry0MbfcOWiadOOegzYYYcxBm/345qBpsdhzJkmSRmpq5C3llcWv9pIkSR1iz5kkSRoZc86Gt+T3Odu1Z4Lag3OmqJmD1m1LnIOmFccctNn2vv8LzEGTehzWlCRJ6pAJ6saSJEnLrQh77PsZimdPkiSpQ5b4Pmfh9knKORs0R1N3/xyx4XLQVu+X0DDeOWjLf+n14uagSZ3LQZvzT84ctFmZgzaj5Y/n483/HpIkSR0ywd1YkiRpqXkrjeHN2XOWZFOSjya5LMmlSV7UrD8qycVJrmh+Hrn41ZXUNcYISRqt+fSc7QZeXFWfS3IY8NkkFwPPBj5cVa9JcjZwNvA7s+2oCHd2eG7NtlZncC7FgQKLnIPGwPYDJywHbfmNOgdtYo0sRqw0nctBm5M5aLOaK6dsBeegqZ05G2dVtR3Y3jy/JcnlwAbgNOCUpthbgY9h4JVWHGOEpH2FPWVK+zBanb0k9wIeCnwaOLYJygDfAo6d4TVnJdmaZOudN+8cpq6SOm7oGMEdS1NRSeqweV8QkORQ4P3Ab1TV99I3pFZVlQx2GN+1bQuwBeCw+/+gnbjShBpFjDg8RxkjpDFXwJQ3gxjKvBpnSdbSC7rvqKq/aVZ/O8lxVbU9yXHAjjl3VLBnaoJ/YYPpG0PmoO13H7M5ctBWzTEXpzlowxo2B21yjSxGrHArLQdtMGTNdduwsctBm4sXNGoG87laM8BbgMur6rV9my4Azmyenwl8cPTVk9R1xghJg/aQzjzG0Xx6zh4J/CLwpSSXNOt+F3gN8J4kzwWuAf7botRQUtcZIyRphOZzteYnmLnz9bGjrY6kcWOMkKTRWtIZAgq4c5JzzgYNmYM2mMExeB+z/XPM9rXWHLRF1jIHTWpp4nPQ5qjxxOWgzfmGJkOVt9IYlmdPkiSpQ5xbU5IkjdRcvaKanT1nkiRJHbK0OWcV7lzJeTgjzkEbTGDYP8eMObabg9bG1GACyurB8zV7Dpo0LHPQZts745+DJjUc1pQkSSNTwB4H5obi2ZMkSeoQe84kSdIIeSuNYS35fc72TPUP2k94ktJczEEba6vmvGffhJ8ALbuhc9Bq37/5Zc9BywrPQZMa9pxJkqSRKWDKrKmhePYkSZI6xMaZJElShyz5fc72nW9wrvtErTDmoEkagvdBm23vmIO2hPZ4MoZiz5kkSVKHeEGAJEkamSLehHZInj1JkqQOWfKes/75CXfvN8/maHPQBu+ZM3bMQVtUY//5kOZgDtpse8cctEU05U1oh+LZkyRJ6hAbZ5IkSR3iBQGSJGlkCrwgYEhLP7fmnr5f2Op9B/hHnYN2x8Dbmxr3BIAlzkFbvV9CxeD+JisHTZp05qDNtndGnoMmLZRNW0mSNDJF2FPdecxHknOT7Ejy5Rm2n5Lku0kuaR4v79t2apKvJrkyydmjOIc2ziRJ0kp3HnDqHGX+X1Wd1DxeBZBkNXAO8ETggcAZSR44bGXMOZMkSSM1NWZ9P1X18ST3WsBLTwaurKqrAJK8CzgNuGyY+ixt46zC1FR/F+PAL2+Rc9AmziLnoA3mT5iDNrvB/Bep6wZz0FL7foYH/0TNQRsoP0cO2n53ijQnbdz9eJIvAN8EXlJVlwIbgGv7ylwHPHzYA9lzJkmSJtkxSbb2LW+pqi0t9/E54ISq+n6SJwF/C5w4qgoOsnEmSZJGpgr2dGuGgOuravMwO6iq7/U9vzDJnyc5BtgGbOorurFZN5ROnT1JkqSuSfKDSe9+U0lOptd+ugH4DHBiknsnWQecDlww7PGWfm7NqdkG3c1BG4o5aJKGUHfcsc/yXCHEHLSB8uagNTLnue6aJOcDp9AbAr0OeAWwFqCq3gQ8A/jVJLuB24DTq6qA3UleCHyI3gf43CYXbSgOa0qSpBWtqs6YY/sbgDfMsO1C4MJR1sdhTUmSpA6Zs+csyYHAx4EDmvLvq6pXJLk38C7gaOCzwC9W1a7FrKyk7jFGSOpXdO6CgLEzn2HNO4DHNJePrgU+keQfgd8CXldV70ryJuC5wBtn21EV1NTdv7Cp/TIaBpmDNpQVloO2KnN9nrRIRhYj1C3moM22d0aegybtNee/5+r5frO4tnkU8Bjgfc36twJPX4wKSuo2Y4SkQXtY1ZnHOJpXrZOsTnIJsAO4GPgacHNV7W6KXEfvLrnTvfasJFuTbJ265dYRVFlS14wqRtzJHdMVkaQVZV5Xa1bVHuCkJOuBDwAPmO8BmrvwbgE44Ic2OL+NNIFGFSMOz1HGCGnMFWFqcu8TsiRa3Uqjqm5O8lHgx4H1SdY034znfUfc6v+FTe3bcWcO2iKb9By08ey9niijiBHqronLQatu5aBJe8357yzJPZpvwyQ5CHg8cDnwUXo3ZQM4E/jgItVRUocZIyRptObTc3Yc8NYkq+k15t5TVX+f5DLgXUleDXweeMsi1lNSdxkjJO1jXBPxu2LOxllVfRF46DTrrwJOXoxKSRofxghJGq0ln75pnzH4VQMj9mOegzZuc4mZgyZpGBOXg7bM90GbFAVMeRPaoXj2JEmSOsSJzyVJ0giFPeM2ktQx9pxJkiR1yBL3nGXgPmcDI/YTloM2dsxBkzQEc9Bm2ztz5qBJezmsKUmSRsYLAobn2ZMkSeoQe84kSdJIeUHAcJa2cVbA1N2/sP16Pc1B28eyTxxrDpqkIQydg5Z910x6Dpq0lz1nkiRpZKpiztmQPHuSJEkdYuNMkiSpQ5Zhbs27R90Hx9/NQes4c9AkDWEwB60ye87WirsP2gTZ47DmUDx7kiRJHeIFAZIkaWSKuXsRNTt7ziRJkjpk6e9z1jfo3p9/BuagjR1z0CQNYer22/dZnutPbtJz0KS9HNaUJEkjFC8IGJJnT5IkqUPsOZMkSSNTdGD6wTG39I2z/jyzVfuOuJuDNubMQZM0hInLQUu7HDRpL3vOJEnSSO3x2+xQPHuSJEkdYuNMkiSpQ5Z+WLN/CH4gx8wctPG2KgPnc+gcLnPQpJVs6By02vdvftlz0FreB21cFfGCgCH570WSJKlDvCBAkiSN1JR9P0Px7EmSJHXI0s+tOct9zrqeg5YM1m/fRXPQ5mAOmqQhTNx90CZ0ds0q2GPO2VD8dyJJktQh826cJVmd5PNJ/r5ZvneSTye5Msm7k8z1lUPSBDNGSNJotOk5exFwed/y/wFeV1X3BW4CnjvKikkaO8YISUBvbs2uPMbRvHLOkmwEngz8AfBb6SVfPQb4+abIW4FXAm9sdfQ5csy6l4M2R9KROWjtmIM2MRYtRkizmLQcNGmv+V4Q8HrgpcBhzfLRwM1VtbtZvg7YMNqqSRojr8cYIYm9N6FdQd9OF8GcZy/JU4AdVfXZhRwgyVlJtibZuuf7ty5kF5I6bJQx4k7uGHHtJGn8zKfn7JHA05I8CTgQOBz4E2B9kjXNN+ONwLbpXlxVW4AtAAecsHEyrxuWVraRxYjDc5QxQpoAeyZ0aqqlMmfjrKpeBrwMIMkpwEuq6heSvBd4BvAu4Ezgg/M6Yn/oHfzdmYPGclqVZf6/OOY5aKv3+7zOWnxijDxGSAs07jlo0l7DDAr/Dr3E3yvp5Ze8ZTRVkjQhjBGStACtZgioqo8BH2ueXwWcPPoqSRpXxghJBWN7C4uu8HIKSZKkDlnauTVh/zSefuagDSx7H7R9dDwHbb958lbtm/8iaWmNXw7apPBWGsPy7EmSJHWIjTNJkqQOWfphTUmSNNGmvE3IUJa4cRbSnwc28LurwZwyc9AGlldq/kKj8zlogxXajaTuMAdN48KeM0mSNDJVsMdbaQzFnDNJkqQOsedMkiSNlLfSGM7SNs6KWe9zloGcsM7loNVg5budgzbxd2geuxw0SV1iDpq6yqatJElShzisKUmSRqbI5I/cLDJ7ziRJkjqkW3NrDmidgzbXvofNQRvc32B9upaDtnpg7sepCW+Ldz4HTVKXLX8O2uTwJrTDmfD/1pIkSbNLcm6SHUm+PMP2X0jyxSRfSvJvSR7St+3qZv0lSbaOoj7mnEmSpJEpxvJuAecBbwDeNsP2rwOPrqqbkjwR2AI8vG/7T1XV9aOqjI0zSZK0olXVx5Pca5bt/9a3+Clg42LWZxlyzvpb0y1zcuYs3jIJbaXloA1p1bjlUM1xOlcP/L72f3+Dv59hc9AkjZPBHLTUYMzYlzloK8ZzgX/sWy7goiQF/EVVbRn2APacSZKkkerYDAHHDOSCbVloAyrJT9FrnD2qb/Wjqmpbkh8ALk7ylar6+BD1tXEmSZIm2vVVtXnYnSR5MPBm4IlVdcPe9VW1rfm5I8kHgJMBG2eSJKkjavJuQpvknsDfAL9YVf/Rt/4QYFVV3dI8fwLwqmGPt+SNs/QN2VfLHLE573u2/yta7d8ctAk3533Qbh9YsXuOF7TMQZM01uqOO/ZZnitit89B03JJcj5wCr0h0OuAVwBrAarqTcDLgaOBP08CsLvpjTsW+ECzbg3wzqr6p2HrY8+ZJEkamWL8bkJbVWfMsf15wPOmWX8V8JD9XzGcTmXsSZIkrXQ2ziRJkjpkSYc1d1173fVXv+gl1wDHACO7k+4isH7DsX7TO2EZjjlWbuGm6/+53meMGJ71G8786jeYpjq4fGPr405MjJi0CwKW2pI2zqrqHgBJto7istbFYv2GY/20UMaI0bB+w+l6/TT5vCBAkiSNzJjOrdkp5pxJkiR1yHI1zoaed2qRWb/hWD8Nq+u/I+s3HOsnzSJVYzaZtSRJ6qz1D/iB+s9/+XPLXY27/P1PvuGz45ZD6LCmJElShyxp4yzJqUm+muTKJGcv5bFnkuTcJDuSfLlv3VFJLk5yRfPzyGWq26YkH01yWZJLk7yoY/U7MMm/J/lCU7//1ay/d5JPN7/ndydZtxz166vn6iSfT/L3Xayf9tW1OGGMGKp+xogVqOjNrdmVxzhassZZktXAOcATgQcCZyR54FIdfxbnAacOrDsb+HBVnQh8uFleDruBF1fVA4FHAC9ozllX6ncH8JiqeghwEnBqkkcA/wd4XVXdF7gJeO4y1W+vFwGX9y13rX5qdDROnIcxYqGMEdICLGXP2cnAlVV1VVXtAt4FnLaEx59WVX2c/W8VeBrw1ub5W4GnL2Wd9qqq7VX1ueb5LfSCx4YO1a+q6vvN4trmUcBjgPc165etfgBJNgJPBt7cLIcO1U/76VycMEYMVT9jxAo1RTrzGEdL2TjbAFzbt3xds66Ljq2q7c3zb9GbdX5ZJbkX8FDg03Sofs1wwCXADuBi4GvAzVW1uymy3L/n1wMvBaaa5aPpVv20r3GJE535G9zLGLFgr8cYoY7xgoA5VO9y1mW9pDXJocD7gd+oqu/1b1vu+lXVnqo6CdhIr9fjActVl0FJngLsqKrPLnddNLmW+28QjBELZYxQVy3lDAHbgE19yxubdV307STHVdX2JMfR+8a3LJKspRd031FVf9O1+u1VVTcn+Sjw48D6JGuab57L+Xt+JPC0JE8CDgQOB/6kQ/XT/sYlTnTmb9AYMRRjxGIoZwgY1lL2nH0GOLG5CmYdcDpwwRIev40LgDOb52cCH1yOSjS5D28BLq+q1/Zt6kr97pFkffP8IODx9HJePgo8Y7nrV1Uvq6qNVXUvep+3j1TVL3SlfprWuMSJrvwNGiOGYIxQVy1Zz1lV7U7yQuBDwGrg3Kq6dKmOP5Mk5wOnAMckuQ54BfAa4D1JngtcA/y3ZareI4FfBL7U5GwA/C7dqd9xwFubK+xWAe+pqr9PchnwriSvBj5P759Hl/wO3a7fitXFOGGMGIoxYgVybs3hOUOAJEkamcPvf2w9/C9+frmrcZd//qnXj90MAUuZcyZJklYAe86G49WakiRJHWLjTJIkqUMc1pQkSSOzd25NLZw9Z5IkSR1iz5kkSRqpsudsKPacSZIkdYiNs0WU5NIkpyx3PcZFklOam3xKE83YoEk3RTrzGEcOaw4hyff7Fg8G7gD2NMu/UlU/svS1WnxJrgaOpfdevw/8E/DCqvr+bK+TVooVHhueV1X/vNx1kcaZPWdDqKpD9z6AbwBP7Vv3juWu3yJ7avO+TwIeCrxseasjdccKjw2ShmTjbBEluTrJ45rnr0zy3iRvT3JLki8luV+SlyXZkeTaJE/oe+0RSd6SZHuSbUle3cxPN91xVif53SRfa/b92SSbmm0/keQzSb7b/PyJZv3PJdk6sJ/fTNJqkumq+ha9eRBP6tvPI5L8W5Kbk3yhf/gmyXOSXN7U86okv9LmeNIkWAmxIcmzk/xrktc1seCq5pjPbt7TjiRn9pV/cpLPJ/les/2VA/t7VpJrktyQ5H/2n0N1S1VvhoCuPMaRjbOl9VTgr4Ej6U2m+yF6v4MNwKuAv+grex6wG7gvvZ6pJwDPm2G/vwWcATwJOBz4JWBnkqOAfwD+FDgaeC3wD0mOBv4OuH+SE/v28/PAO9u8oSQbgScCVzbLG5pjvho4CngJ8P4k92hesgN4SlPP5wCvS/KwNseUJtDExYbGw4EvNsd4J/Au4Meauj8TeEOSQ5uytwLPAtYDTwZ+NcnTAZI8EPhz4BfoTaZ+BL1zI00kG2dL6/9V1YeqajfwXuAewGuq6k56QeteSdYnOZZeMP2Nqrq1qnYArwNOn2G/zwP+R1V9tXq+UFU30AtwV1TVX1fV7qo6H/gKvSGWncAH6QVumkD8AGC+347/NsktwLX0GlyvaNY/E7iwqi6sqqmquhjY2rwfquofquprTT3/BbgI+M/zPKY0qSYpNvT7elX9VVXtAd4NbAJeVVV3VNVFwC56DTWq6mNV9aUmbnwROB94dLOfZwB/V1WfqKpdwMuBWkB9tESq0pnHOLJxtrS+3ff8NuD6JmjtXQY4FDgBWAtsb4YDbqb3zfkHZtjvJuBr06w/HrhmYN013P2N8500AZjeN+O/bQLzfDy9qg4DTqEXuI9p1p8A/Ozeejd1fxS9b7skeWKSTyW5sdn2pL7XSivVJMWGfoPvi6oaXHcoQJKHJ/loku8k+S7wfO6ODcfT+yJIs4+dwA0LqI80FmycddO19K7uOqaq1jePw2e5wuta4D7TrP8mvWDe757Atub5xcA9kpxELxC3HrZoer/OA/6ory5/3Vfv9VV1SFW9JskBwPubssdW1XrgQhjTa52lpTc2sWEB3kmvd25TVR0BvIm7Y8N2YOPegkkOojdUqk5a/jwzc840clW1nd5w3x8nOTzJqiT3SfLoGV7yZuD3k5yYngc3uSMXAvdL8vNJ1iT5OeCBwN83x7mT3hDKH9LLD7t4gVV+PfD4JA8B3g48NclPN8nIB6Z3/7KNwDrgAOA7wO4kT6SXLyNpHsYwNrRxGHBjVd2e5GR6PXZ7vY9eXPmJJOuAV+KXOk0wG2fd9Sx6jZnLgJvoBafjZij7WuA99IL294C3AAc1uSVPAV5MbwjgpcBTqur6vte+E3gc8N4m34Ukv5Dk0vlWtKq+A7wNeHlVXQucBvwuvUbYtcBvA6uq6hbg15u63kQv+C4kj0VaycYmNrT034FXNbmsL2/qDUBVXQr8Gr38u+307q+4g14vojRxUmVOpSRpfDRXeN4MnFhVX1/m6mjAofc7rh70Z89e7mrc5dOnvuazVbV5uevRhj1nkqTOS/LUJAcnOYRe3uqXgKuXt1bS4nD6JknSODiN3r3gQu/2PKeXQz+dVDC2ifhdYeNMktR5VfU8Zr7ZrjRRHNaUJEnqEHvOJEnS6FRvfk0t3JI2zo4+alVt2mR7UCvTF7545/VVdY+5S65c69YcXAetW7/c1ZCWxfdu226MELDEjbNNm9Zw0YXO1KOV6diN2weny9GAg9at5xEP+OXlroa0LC76/KsmJkZMeY/goZhzJkmS1CFDNc6SnJrkq0muTHL2qColaTIYI6SVp4CqdOYxjhbcOEuyGjgHeCK9OdnOSPLAUVVM0ngzRkjSwgzTc3YycGVVXVVVu+jNeXbaaKolaQIYIyRpAYa5IGADvUmt97oOePhw1ZE0QYwR0ooUZwgY0qJfEJDkrCRbk2y94YapxT6cpDHTHyN27d653NWRpGU3TONsG7Cpb3ljs24fVbWlqjZX1eajj/biUGkFaR0j1q05eMkqJ2nxVHXnMY6GaS19Bjgxyb2TrANOBy4YTbUkTQBjhCQtwIJzzqpqd5IXAh8CVgPnVtWlI6uZpLFmjJBWrnG9hUVXDDVDQFVdCFw4orpImjDGCElqzyQwSZKkDnEWckmSNDK9RHyHNYdhz5kkSVKH2HMmSZJGypvQDseeM0mSpA6x50ySJI3UuN78tSvsOZMkSeoQG2eSJEkd4rCmJEkaKW+lMRx7ziRJkjrExpkkSRqZIlR15zEfSc5NsiPJl2fYniR/muTKJF9M8rC+bWcmuaJ5nDmKc2jjTJIkrXTnAafOsv2JwInN4yzgjQBJjgJeATwcOBl4RZIjh62MjTNJkrSiVdXHgRtnKXIa8Lbq+RSwPslxwE8DF1fVjVV1E3Axszfy5sULAiRJ0khN4G3ONgDX9i1f16ybaf1QbJxJkqRJdkySrX3LW6pqy7LVZh5snEmSpNGpzt1K4/qq2jzkPrYBm/qWNzbrtgGnDKz/2JDHMudMkiRpDhcAz2qu2nwE8N2q2g58CHhCkiObCwGe0Kwbij1nkiRptMYs6SzJ+fR6wI5Jch29KzDXAlTVm4ALgScBVwI7gec0225M8vvAZ5pdvaqqZruwYF4W3DhLsgl4G3AsvV/Dlqr6k2ErJGkyGCMkjYuqOmOO7QW8YIZt5wLnjrI+w/Sc7QZeXFWfS3IY8NkkF1fVZSOqm6TxZoyQpAVYcOOsGWvd3jy/Jcnl9C4fNfBKMkZIK1jHLggYOyO5ICDJvYCHAp8exf4kTRZjhCTN39AXBCQ5FHg/8BtV9b1ptp9Fb6oDNm5YPezhJI2ZNjHiwLVHLHHtJC2GGrMLArpmqJ6zJGvpBd13VNXfTFemqrZU1eaq2nz00d65Q1pJ2saIdWsOXtoKSlIHDXO1ZoC3AJdX1WtHVyVJk8AYIa1MhTlnwxqmK+uRwC8Cj0lySfN40ojqJWn8GSMkaQGGuVrzE4BNY0nTMkZI0sI4Q4AkSRqdAhzWHIoZ+pIkSR1iz5kkSRopb6UxHHvOJEmSOsSeM0mSNFr2nA3FnjNJkqQOsXEmSZLUIQ5rSpKkEYozBAzJnjNJkqQOsedMkiSNlhcEDMWeM0mSpA6xcSZJktQhDmtKkqTRKbwgYEj2nEmSJHWIPWeSJGm0vCBgKPacSZIkdYg9Z5IkacTMORuGPWeSJEkdMnTjLMnqJJ9P8vejqJCkyWKMkKR2RjGs+SLgcuDwEexL0uQxRkgrjRcEDGWonrMkG4EnA28eTXUkTRJjhCS1N2zP2euBlwKHDV8VSRPo9RgjpJXHnrOhLLjnLMlTgB1V9dk5yp2VZGuSrTfcMLXQw0kaMwuJEbt271yi2klSdw0zrPlI4GlJrgbeBTwmydsHC1XVlqraXFWbjz7ai0OlFaR1jFi35uClrqOkUSug0p3HGFpwa6mqXlZVG6vqXsDpwEeq6pkjq5mksWaMkKSFsStLkiSpQ0YyQ0BVfQz42Cj2JWnyGCOklaW8IGAo9pxJkiR1iHNrSpKk0bLnbCj2nEmSJHWIPWeSJGm0xvQWFl1hz5kkSVKH2DiTJEnqEIc1JUnSSMULAoZiz5kkSVKH2HMmSZJGp/BWGkOy50ySJKlDbJxJkiR1iMOakiRphOJ9zoZkz5kkSVKH2HMmSZJGywsChmLPmSRJUofYcyZJkkbLnrOh2HMmSZLUITbOJEmSOsRhTUmSNFoOaw5lqJ6zJOuTvC/JV5JcnuTHR1UxSePPGCFJ7Q3bc/YnwD9V1TOSrAMOHkGdJE0OY4S00hTehHZIC26cJTkC+Eng2QBVtQvYNZpqSRp3xghJWphhhjXvDXwH+Kskn0/y5iSHjKheksafMUJaoVLdeYyjYRpna4CHAW+sqocCtwJnDxZKclaSrUm23nDD1BCHkzRmWseIXbt3LnUdJalzhmmcXQdcV1WfbpbfRy8Q76OqtlTV5qrafPTR3rlDWkFax4h1a0xJk6QFt5aq6lvAtUnu36x6LHDZSGolaewZI6QVrDr0GEPDXq35a8A7mquwrgKeM3yVJE0QY4QktTRU46yqLgE2j6YqkiaNMUKS2jMJTJIkrWhJTk3y1SRXJpnuwqXXJbmkefxHkpv7tu3p23bBKOrj9E2SJGmkxukWFklWA+cAj6d3IdNnklxQVXflyFbVb/aV/zXgoX27uK2qThplnew5kyRJK9nJwJVVdVVzs+x3AafNUv4M4PzFrJCNM0mStJJtAK7tW76uWbefJCfQu8H2R/pWH9jcq/FTSZ4+igo5rClJkkarW3NrHpNka9/ylqrassB9nQ68r6r29K07oaq2Jfkh4CNJvlRVX1twbbFxJkmSJtv1VTXbVePbgE19yxubddM5HXhB/4qq2tb8vCrJx+jlow3VOHNYU5Ikjc5y33S2/U1oPwOcmOTezT0ZTwf2u+oyyQOAI4FP9q07MskBzfNjgEcygptt23MmSZJWrKraneSFwIeA1cC5VXVpklcBW6tqb0PtdOBdVdXf5Pth4C+STNHr8HpN/1WeC2XjTJIkrWhVdSFw4cC6lw8sv3Ka1/0b8KOjro+NM0mSNFpjdJ+zLjLnTJIkqUPsOZMkSSM1TjMEdJE9Z5IkSR1iz5kkSRote86GYs+ZJElSh9g4kyRJ6hCHNSVJ0mg5rDkUe84kSZI6ZKjGWZLfTHJpki8nOT/JgaOqmKTxZ4yQVp5Utx7jaMGNsyQbgF8HNlfVg+jNR3X6qComabwZIyRpYYbNOVsDHJTkTuBg4JvDV0nSBDFGSCtRZblrMNYW3HNWVduAPwK+AWwHvltVF42qYpLGmzFCkhZmmGHNI4HTgHsDxwOHJHnmNOXOSrI1ydYbbphaeE0ljZWFxIhdu3cudTUlqXOGuSDgccDXq+o7VXUn8DfATwwWqqotVbW5qjYffbQXh0orSOsYsW7NwUteSUmLoDr0GEPDtJa+ATwiycFJAjwWuHw01ZI0AYwRkrQAC74goKo+neR9wOeA3cDngS2jqpik8WaMkFaucb2FRVcMdbVmVb0CeMWI6iJpwhgjJKk9p2+SJEmjZc/ZUMzQlyRJ6hAbZ5IkSR3isKYkSRqdMZ7TsivsOZMkSeoQe84kSdJo2XM2FHvOJEmSOsTGmSRJUoc4rClJkkbLYc2h2HMmSZLUIfacSZKkkfJWGsOx50ySJKlDbJxJkiR1iI0zSZKkDrFxJkmS1CFeECBJkkbLCwKGYs+ZJElSh9hzJkmSRqe8lcaw7DmTJEnqkDkbZ0nOTbIjyZf71h2V5OIkVzQ/j1zcakrqMuOEpH1Uhx5jaD49Z+cBpw6sOxv4cFWdCHy4WZa0cp2HcUKSRmLOxllVfRy4cWD1acBbm+dvBZ4+2mpJGifGCUkanYVeEHBsVW1vnn8LOHZE9ZE0OYwT0ko1psOJXTH0BQFVNeuobpKzkmxNsvWGG6aGPZykMTRbnOiPEbt271zimklS9yy0cfbtJMcBND93zFSwqrZU1eaq2nz00V4cKq0g84oT/TFi3ZqDl7SCkkYv9G6l0ZXHOFpoa+kC4Mzm+ZnAB0dTHUkTxDghSQswn1tpnA98Erh/kuuSPBd4DfD4JFcAj2uWJa1QxglJ+1ju22eM+a005rwgoKrOmGHTY0dcF0ljyjghSaNjEpgkSVKHOLemJEkanTFOxO8Ke84kSZI6xJ4zSZI0WvacDcWeM0mSpA6xcSZJktQhDmtKkqTRclhzKPacSZIkdYg9Z5IkaaS8lcZw7DmTJEnqEHvOJEnSaNlzNhR7ziRJkjrExpkkSVKHOKwpSZJGp3BYc0j2nEmSJHWIPWeSJGmkvJXGcOw5kyRJ6hAbZ5IkabSqQ495SHJqkq8muTLJ2dNsf3aS7yS5pHk8r2/bmUmuaB5nzvcUzWbOxlmSc5PsSPLlvnV/mOQrSb6Y5ANJ1o+iMpLGjzFC0jhLsho4B3gi8EDgjCQPnKbou6vqpObx5ua1RwGvAB4OnAy8IsmRw9ZpPj1n5wGnDqy7GHhQVT0Y+A/gZcNWRNLYOg9jhKTxdTJwZVVdVVW7gHcBp83ztT8NXFxVN1bVTfRi32A8bG3OxllVfRy4cWDdRVW1u1n8FLBx2IpIGk/GCEmDUt15zMMG4Nq+5euadYP+azMa8L4km1q+tpVR5Jz9EvCPI9iPpMlkjJC0nI5JsrXvcdYC9vF3wL2a0YCLgbeOtor7GupWGkl+D9gNvGOWMmcBZwFs3LB6mMNJGjNtY8SBa49YoppJWlTdupXG9VW1eZbt24BNfcsbm3V3qaob+hbfDPzfvteeMvDajy20onstuOcsybOBpwC/UFUz/hqqaktVba6qzUcf7cWh0kqxkBixbs3BS1Y/SWp8Bjgxyb2TrANOBy7oL5DkuL7FpwGXN88/BDwhyZHNhQBPaNYNZUE9Z0lOBV4KPLqqdg5bCUmTxRghrWBjNn1TVe1O8kJ6jarVwLlVdWmSVwFbq+oC4NeTPI3eSMCNwLOb196Y5PfpNfAAXlVVN+53kJbmbJwlOZ9el90xSa6jd8noy4ADgIuTAHyqqp4/bGUkjR9jhKRxV1UXAhcOrHt53/OXMcNV51V1LnDuKOszZ+Osqs6YZvVbRlkJSePLGCFJo+XcmpIkaWTSPLRwZuhLkiR1iD1nkiRptMbogoAusudMkiSpQ+w5kyRJIzXPaZM0A3vOJEmSOsTGmSRJUoc4rClJkkbLYc2h2HMmSZLUIfacSZKk0bLnbCj2nEmSJHWIjTNJkqQOcVhTkiSNTnmfs2HZcyZJktQh9pxJkqTRsudsKPacSZIkdciS9px94Yt3Xn/sxu3XzLD5GOD6pazPDKzH/rpSl3Gvxwmjrsik+d5t26+/6POvMkbMT1fqAd2py7jXY2JihDlnw1nSxllV3WOmbUm2VtXmpayP9ZifrtTFekw+Y8T41QO6UxfroUnhsKYkSVKHeEGAJEkaLYc1h9KlnrMty12BhvXYX1fqYj1Wtq6cd+uxv67UxXpoIqTK5q0kSRqNg39gUz3gv/7WclfjLp9/0299dtxyALvUcyZJkrTiLXnjLMmpSb6a5MokZ0+z/YAk7262fzrJvRahDpuSfDTJZUkuTfKiacqckuS7SS5pHi8fdT2a41yd5EvNMbZOsz1J/rQ5H19M8rBFqMP9+97nJUm+l+Q3Bsos2vlIcm6SHUm+3LfuqCQXJ7mi+XnkDK89sylzRZIzF6Eef5jkK825/0CS9TO8dtbfo+bPGLHfcYwRxojxUh17jKElbZwlWQ2cAzwReCBwRpIHDhR7LnBTVd0XeB3wfxahKruBF1fVA4FHAC+Yph4A/6+qTmoer1qEeuz1U80xput2fSJwYvM4C3jjqA9eVV/d+z6B/wTsBD4wTdHFOh/nAacOrDsb+HBVnQh8uFneR5KjgFcADwdOBl4xU4Aeoh4XAw+qqgcD/wG8bJbXz/Z71DwYI2ZkjDBGaAVZ6p6zk4Erq+qqqtoFvAs4baDMacBbm+fvAx6bJKOsRFVtr6rPNc9vAS4HNozyGCN0GvC26vkUsD7JcYt4vMcCX6uqmW4EOnJV9XHgxoHV/Z+DtwJPn+alPw1cXFU3VtVN9ILkYOAcqh5VdVFV7W4WPwVsXOj+NS/GiPaMEcYITZilbpxtAK7tW76O/QPeXWWaD/x3gaMXq0LNkMhDgU9Ps/nHk3whyT8m+ZFFqkIBFyX5bJKzptk+n3M2SqcD58+wbSnOx17HVtX25vm3gGOnKbPU5+aXgH+cYdtcv0fNjzFif8aI6Rkjumy5hzLHfFhzRd/nLMmhwPuB36iq7w1s/hxwQlV9P8mTgL+lN2wwao+qqm1JfgC4OMlXmm9nSy7JOuBpTN8tv1TnYz9VVcnyTgaS5PfoDXW9Y4Yinfk9anSMEfsyRszMGKFRWuqes23Apr7ljc26acskWQMcAdww6ookWUsv6L6jqv5mcHtVfa+qvt88vxBYm+SYUdejqrY1P3fQy+E4eaDIfM7ZqDwR+FxVfXuaei7J+ejz7b1DM83PHdOUWZJzk+TZwFOAX6gZ7j0zj9+j5scYsf9xjBHTM0Z0VOjNrdmVxzha6sbZZ4ATk9y7+QZ2OnDBQJkLgDOb588APjLTh32hmvyUtwCXV9VrZyjzg3vzWJKcTO9cjfQfQJJDkhy29znwBODLA8UuAJ6VnkcA3+3ryh+1M5hhuGIpzseA/s/BmcAHpynzIeAJSY5sknyf0KwbmSSnAi8FnlZVO2coM5/fo+bHGLHvMYwRMzNGaGIt9cTnu5O8kN4fx2rg3Kq6NMmrgK1VdQG9gPjXSa6kl3h5+iJU5ZHALwJfSnJJs+53gXs29XwTvaD/q0l2A7cBp4/6HwC9HIkPNPFsDfDOqvqnJM/vq8eFwJOAK+ldIfWcEdcBuCtgPB74lb51/fVYtPOR5HzgFOCYJNfRu7rqNcB7kjwXuAb4b03ZzcDzq+p5VXVjkt+n9w8d4FVVNZg0PGw9XgYcQG8YAuBTVfX8JMcDb66qJzHD73Gh9VjJjBH7MUZgjBhLY9pj1RXOECBJkkbmkHtsqgc8vTszBHzuzc4QIEmSpCGs6Ks1JUnS6MVRuaHYcyZJktQh9pxJkqTRGeObv3aFPWeSJEkdYuNMkiSpQ2ycqbOSnNLcS0iSNEaWe1YAZwhYgZI8Ksm/JflukhuT/GuSH1vuei2XJFcnuS3J95N8K8l56c1JKK0oxoZ9NbHhcctdD2nc2DhrKcnhwN8DfwYcBWwA/hdwx3LWqwOeWlWHAicBD2X6iZGliWVskPpUhx5jyMZZe/cDqKrzq2pPVd1WVRdV1RehNwFu8235Dc23568keezeFyd5TpLLk9yS5Kokv9K/8ySnJbkkyfeSfK2Zt40kRyR5S5LtSbYleXWS1dNVMMnqJL/bvP6WJJ9Nsnei6J9I8pmmbp9J8hPN+p9LsnVgP7+ZZHBew1lV1bfoTb1zUt9+HtH0Jtyc5AtJTpnv+ZDGiLFhFn3v/3VNLLiqOeazk1ybZEeSM/vKPznJ55v3e22SVw7s71lJrklyQ5L/aS+dJomNs/b+A9iT5K1JnpjeZLqDHg58DTiG3txrf5PkqGbbDuApwOH05sB7XZKHwV2TBb8N+G1gPfCTwNXN684DdgP3pdcz9QTgeTPU8bfoTVD8pOY4vwTsbOrwD8CfAkcDrwX+IcnRwN8B909yYt9+fh5453xOyl5JNgJPpDfPH0k2NMd8Nb3ehJcA709yj7nOhzRmjA1zezjwxeYY7wTeBfxYU/dnAm/I3SkRtwLPat7vk+nN2/l0gCQPBP4c+AXgOOAIej2V6ojlzjMz52yFqarvAY+i11n6l8B3klyQ5Ni+YjuA11fVnVX1buCr9IILVfUPVfW16vkX4CLgPzevey69iZ4vrqqpqtpWVV9p9v0k4Deq6taq2gG8jpknfH4e8D+q6qvNcb5QVTc0dbiiqv66qnZX1fnAV+gNSe4EPkgvcNME4gcA8/12/LdJbgGubd7/K5r1zwQurKoLm/d0MbC1eT9znQ9pbBgb5uXrVfVXVbUHeDewid5k5HdU1UXALnoNNarqY1X1peb9fhE4H3h0s59nAH9XVZ+oql3AyxnbASxpfzbOFqCqLq+qZ1fVRuBBwPHA6/uKbKt9Z5S/pilD8436U+klC99ML7Ae05TbRO9b9aATgLXA9mY44GbgL4AfmKGKM+3n+KYu/a7h7m+c76QJwPS+Gf9tE5jn4+lVdRhwCr3Avfc9nQD87N56N3V/FL1vu3OdD2msGBvm9O2+57cBVNXgukMBkjw8yUeTfCfJd4Hnc/f5OJ7eF0GafewEblhAfaROsnE2pKr6Cr1hhQf1rd6QJH3L9wS+meQA4P3AHwHHVtV64EJgb9lrgftMc5hr6SUVH1NV65vH4VX1IzNUa6b9fJNeMO93T2Bb8/xi4B5JTqIXiFsPWzTf+M+j9x731uWv++q9vqoOqarXzON8SGPL2DC0d9LrndtUVUcAb+Lu87Ed2Li3YJKD6A2VqiuW+yIALwhYWZI8IMmLm9wqmmTaM4BP9RX7AeDXk6xN8rPAD9MLtOuAA4DvALuTPJFefshebwGek+SxSVYl2ZDkAVW1nd4Qxx8nObzZdp8kj2Z6bwZ+P8mJ6XlwkztyIXC/JD+fZE2SnwMeSO8KM6rqTuC9wB/Syw+7eIGn6fXA45M8BHg78NQkP51eMvKB6d2/bOM8zoc0NowNI3cYcGNV3d7k3P1837b30YsrP5FkHfBK/FKnCWLjrL1b6CW1fjrJrfQC75eBF/eV+TRwInA98AfAM6rqhqq6Bfh14D3ATfSCzV15G1X17zSJwMB3gX/h7m+zz6IXwC9rXvs+mqHBaby2OcZFwPfoBfaDmtySpzR1vQF4KfCUqrq+77XvBB4HvLeqdgMk+YUkl873BFXVd+glL7+8qq4FTgN+l94/nmvpJTWvmut8SGPG2DBa/x14VZPL+vKm3gBU1aXAr9G7oGA78H16+XzetqQLOnARwLhfEJB90x80rCTPBp5XVY9a7rpI6g5jw+JprvC8GTixqr6+zNVZ8Q45elM96Mm/udzVuMu///WLP1tVm5e7Hm3YcyZJGjtJnprk4CSH0MvV+xJ3315Ey22588zMOZMkacmdRu9Chm/SGyo+vRwK0oSwcTZiVXWewxaSBhkbRquqntdcnXpEVT22qr663HWSRmXNcldAkiRNjjC+ifhdYc+ZJElShyxpz9nRR62qTZvmf8idU+3ajjvuPLxV+dt2HtCqfNvEwrbfHLK7XfnVu1qWv+X2di9oe9ugVS3Lp2O3JVrk+nzv9m9dX1X3mLvkyrVuzcF10Lr18y6/6/Bp5/ee0dRB7f4o161r90e56pp2n6GpA9vVf8+6dvuvdrtn3Xf3tHtB2xSvRS5fa9q94da9O4uc0jZRMcL0v6EsaeNs06Y1XHTh/Gfm+cKudo2tc7Y9plX5S77wQ63Kr9rVLjCuuqNd+YO+0678odumWpU/4iNXtCrP6paBbt26VuXrgLWtyrfWtrG1qmVHcst/BB/68h8MTo+jAQetW88jHvDL8y6/7THrW+3/+w9udxusEzZcP3ehPge9oF1IvfV+7W5q//0N7T5zd6xv9zew6UM3tyqfO+5c1PLsaddY3HNMu/8ZubPd/rO7Xcxt20D50GX/2xghYMhhzSSnJvlqkiuTnD2qSkmaDMYIaWVa7hvPjvtNaBfcOEuyGjgHeCK9aT7OSPLAUVVM0ngzRkjSwgzTc3YycGVVXVVVu+hNo3HaaKolaQIYIyRpAYZpnG2gN0/iXtc16yQJjBHSyrTcMwI4Q8DckpyVZGuSrTfc0DKZUtLE648Ru3bvXO7qSNKyG+ZqzW3Apr7ljc26fVTVFmALwEkPWTembVhJC9A6Rhxx8PHGCGkCxL6YoQzTc/YZ4MQk906yDjgduGA01ZI0AYwRksbCXFeWJ/mtJJcl+WKSDyc5oW/bniSXNI+RxLgF95xV1e4kLwQ+BKwGzq2qS0dRKUnjzxghaRz0XVn+eHq5sZ9JckFVXdZX7PPA5qrameRXgf8L/Fyz7baqOmmUdRrqJrRVdSFw4YjqImnCGCOkFWq8EhTuurIcIMneK8vvapxV1Uf7yn8KeOZiVsi5NSVJ0krW9sry5wL/2Ld8YHNR06eSPH0UFVrS6Zt2Tq1qNSXTQ9Z9r9X+X7DhI63Kn9OqNFz6L/dtVX7Pwe2+OtzWeka1dm3rI1pOx9R26pTa1W6yz7SdN+/gA1uVbz2321TLDNaWc6FqbnXb7UxdctncBRsbaHdP222sb1X+GuY/3RzAif/x2VblD2lVGqDddE+90eT5mzqw3b+Exf52n3azbVGru9XfkD3j1X00Sh27M/8xSbb2LW9pLkRqLckzgc3Ao/tWn1BV25L8EPCRJF+qqq8NUd+lbZxJkiQtseuravMs2+d1ZXmSxwG/Bzy6qu766lBV25qfVyX5GPBQYKjGWbe+ZkiSpPFW9EYuuvKY25xXlid5KPAXwNOqakff+iOTHNA8PwZ4JH25agtlz5kkSVqxZrqyPMmrgK1VdQHwh8ChwHuTAHyjqp4G/DDwF0mm6HV4vWbgKs8FsXEmSZJWtOmuLK+ql/c9f9wMr/s34EdHXR8bZ5IkaaQ6dkHA2DHnTJIkqUPsOZMkSaNlz9lQ7DmTJEnqEHvOJEnSyARzzoZlz5kkSVKH2DiTJEnqkCUd1txx5+Gcs+0x8y7fdq7MxZ6L8yXbT2xVfudxrYov+lycWbu2VfnWvdJt5+Jsu//VB7crv6flXJmLPRenRq7NPJyw+HNxtrXnP9rN8LLYc3FOrW05/25Li90b0LW5NaFdTJwY878zv2bQtU+yJEnSirbgxlmSTUk+muSyJJcmedEoKyZpvBkjpJUr1Z3HOBpmWHM38OKq+lySw4DPJrl4FHNKSZoIxghJWoAF95xV1faq+lzz/BbgcmDDqComabwZI6QVrDr0GEMjyTlLci/gocCnR7E/SZPFGCFJ8zd04yzJocD7gd+oqv0ul0xyVpKtSbbuuvm2YQ8nacy0iRF3csfSV1CSOmaoW2kkWUsv6L6jqv5mujJVtQXYArD+AT8wph2MkhaibYw4PEcZI6QJMK6J+F0xzNWaAd4CXF5Vrx1dlSRNAmOEJC3MMD1njwR+EfhSkkuadb9bVRcOXStJk8AYIa1EBUzZdTaMBTfOquoT9OY3laT9GCMkaWGcIUCSJKlDlnRuzdt2HsAlX/iheZc/p+X+F3suzsO/sbtV+band7Hn4mRdu7k123Z5LPpcnC3n/eulPLWwu+U8eM4dN3YWey7OxZ5tdbHn4ty14YhW5afo2FycLf/kuzcX5wQxPA7FT6YkSVKHLGnPmSRJmnzeSmM49pxJkiR1iD1nkiRptMzJHYo9Z5IkSR1i40ySJKlDHNaUJEkj5QUBw7HnTJIkqUPsOZMkSaNTeBPaIdlzJkmS1CH2nEmSpJEJEG+lMZSlbZwVrNo1/8nPLv2X+7ba/Uu2n9iqfNu5Mv/2ja9vVf5zuw5rVf6cbY9pVb7NPKUAtarlxHMHHdCu/GEHtypeq1vOlbn9hlblafl+s7bd3KNt5yrVPBxyEHXSQ+Zd/Lv3afeZ23lsu8/E7kNbFedeLefi3PaY9a3Kf//Bd7Qqf8KG61uVP/Alt7Uqn90tZxNd5PlrV7WcT3fVHXe2Ks+uduVzZ9v5mKUehzUlSZI6ZOiesySrga3Atqp6yvBVkjRJjBHSCtSyU1X7GkXP2YuAy0ewH0mTyRghSS0M1ThLshF4MvDm0VRH0iQxRkgrU6o68xhHw/acvR54KXZgSpre6zFGSFIrC26cJXkKsKOqPjtHubOSbE2ydc+tty70cJLGzEJixJ27jRHS2KuOPcbQMD1njwSeluRq4F3AY5K8fbBQVW2pqs1VtXn1IYcMcThJY6Z1jFi7xhghSQtunFXVy6pqY1XdCzgd+EhVPXNkNZM01owRkrQwzhAgSZJGqFrfQFj7GknjrKo+BnxsFPuSNHmMEZI0f/acSZKkkYodZ0NZ0sZZClbdMf+5z/Yc3O63u/O4tjVq9/bbzpX5sHW3tCr/gg0faVX+nFal4fa0q3/rbuk97e6W0HKmT7K6XYpktaxP3dly3rxWpTUfuX0Xay7/xrzLH8E9Wx6h5VycLX/LU5dc1qr8hrZzcbK+VflrOKZV+futub1V+bZa/820nItz6sDF/ZfWNknb9okWyrk1JUmSOsRhTUmSNFpeEDAUe84kSZI6xJ4zSZI0OgVxwrah2HMmSZLUIfacSZKk0TLnbCj2nEmSJHWIjTNJkqQOcVhTkiSNlqOaQ7HnTJIkqUPsOZMkSSMVLwgYytLOrbkbDvrO/GdXu+0e7fa/2HNxnrPtMa3Kt50rs+1cnL++4cOtyv/fVf+lVXmmWt6opm35ttaubVU8tJsrc7Hn4tTcas8e9tx007zLr7m83f4Xey7Otro2F2et3tWq/GJr+w++1rQbDOraXJzSXvacSZKk0bLnbChDNeyTrE/yviRfSXJ5kh8fVcUkjT9jhCS1N2zP2Z8A/1RVz0iyjsUeA5A0bowRktTSghtnSY4AfhJ4NkBV7QK6lbAgadkYI6QVqgDn1hzKMMOa9wa+A/xVks8neXOSQ0ZUL0njzxghSQswTONsDfAw4I1V9VDgVuDswUJJzkqyNcnWPTtvHeJwksZM6xhxJ3csdR0ljVgoUt15jKNhGmfXAddV1aeb5ffRC8T7qKotVbW5qjavPtgvzdIK0jpGrOWAJa2gJHXRghtnVfUt4Nok929WPRZod9MeSRPLGCGtYFXdecxDklOTfDXJlUmm6+E/IMm7m+2fTnKvvm0va9Z/NclPj+L0DXu15q8B72iuwroKeM7wVZI0QYwRkjotyWrgHODx9Hr8P5Pkgqrq/zL5XOCmqrpvktOB/wP8XJIHAqcDPwIcD/xzkvtV1Z5h6jRU46yqLgE2D7MPSZPLGCFpDJwMXFlVVwEkeRdwGvv29J8GvLJ5/j7gDUnSrH9XVd0BfD3Jlc3+PjlMhZwhQJIkjdZ4JeJvAK7tW74OePhMZapqd5LvAkc36z818NoNw1ZoSRtnq3fBodva3PykXUrcYs/FeckXfqhV+XNalW4/V+ZD1n2/3QHWrG5Xfne74q3n1mw7b94BbefWbGex5+LU6LWZhxOWYi7OxbXYc3HuOajd31jX1Jq2f/Xt/sfUAe1iaI1XA2WSHZNka9/ylqrasmy1mQd7ziRJ0uh07ya011fVbOkV24BNfcsbm3XTlbkuyRrgCOCGeb62taHm1pQkSRpznwFOTHLv5uKl04ELBspcAJzZPH8G8JHqdY1eAJzeXM15b+BE4N+HrZA9Z5IkacVqcsheCHwIWA2cW1WXJnkVsLWqLgDeAvx1k/B/I70GHE2599C7eGA38IJhr9QEG2eSJGnExu3O/FV1IXDhwLqX9z2/HfjZGV77B8AfjLI+DmtKkiR1iD1nkiRptMas56xr7DmTJEnqEHvOJEnSCM1/TktNz54zSZKkDrFxJkmS1CEOa0qSpNEpHNYc0tLOrXnL7RzxkSvmXf6I1e3mMcvalvPCrWtXvla1m7ft9hzWqvz/XfVfWpVvO1fm2/7p3Fblv7Dr8Fblz9n2mFbl285VuuqOdud/1Z3tyh94fbvy7eaJBc5vV3wlykEHsuoB858PMne0mw+1bfm1225sVX7PQ3+kVfnsaXevyuxu+Zm7bVer4i1n32XbY9a3Kv/9B9/Rqvy9N36nVfkDn9tuvuGdP/yDrcrfsvHAVuXvOLLlXJ+fb1dck8ueM0mSNFrdmltz7AyVc5bkN5NcmuTLSc5P0u5rhaSJZoyQpPYW3DhLsgH4dWBzVT2IXo/46aOqmKTxZoyQVq5UdeYxjoa9WnMNcFCSNcDBwDeHr5KkCWKMkKSWFtw4q6ptwB8B3wC2A9+tqotGVTFJ480YIUkLM8yw5pHAacC9geOBQ5I8c5pyZyXZmmTrrqnbF15TSWNlQTFi986lrqakxVDVnccYGmZY83HA16vqO1V1J/A3wE8MFqqqLVW1uao2r1tlLrC0grSPEWsOXvJKSlLXDHMrjW8Aj0hyMHAb8Fhg60hqJWkSGCOklaiAqfHsseqKYXLOPg28D/gc8KVmX1tGVC9JY84YIUkLM9RNaKvqFcArRlQXSRPGGCGtROOb69UVTnwuSZLUIUs8fVOgzXyZLeeda9tObznrGRx0QLvybb85TLWc72J3u+Jt58p8yLrvtSr/gg0faVX+nFal4fKPnNiq/J2Htjuftx/Tqjh+txm9WhWmDpx/WFrs30DaTQVJre3WZ6JtjJu65LJW5Tcw/3lQAbaxvlX5r3OPVuVPvOazrcofnLZn6NiW5Z0hUQvjJ0eSJI2Ww5pD6dbXPEmSpBXOnjNJkjRa9pwNxZ4zSZKkDrHnTJIkjY43oR2aPWeSJEkdYuNMkiSpQxzWlCRJI1RQLe/bqX3YcyZJktQh9pxJkqTR8lYaQ7HnTJIkqUOWtudsVci6dfMuXrt2tdv/Is/FyWEHtyu/p+WYe9u5NVuWP2fbY1qVbztX5mLPxfnSb963Vfmdx7f77rH4c3FqToGptS3m321psb+N1uqV9X23a3NxtrX76m+0Kt/yPwDt5+KUehzWlCRJo+N9zoY259e8JOcm2ZHky33rjkpycZIrmp9HLm41JXWZcUKSRmc+ffDnAacOrDsb+HBVnQh8uFmWtHKdh3FC0l5V3XmMoTkbZ1X1ceDGgdWnAW9tnr8VePpoqyVpnBgnJGl0FppzdmxVbW+efwuzHiXtzzghrVRj2mPVFUNfWlRVxSwXPiY5K8nWJFt37blt2MNJGkOzxYl9YsSdty5xzSSpexbaOPt2kuMAmp87ZipYVVuqanNVbV63+qAFHk7SGJpXnNgnRqw9ZEkrKEldtNDG2QXAmc3zM4EPjqY6kiaIcUJakTpwEcCkXxCQ5Hzgk8D9k1yX5LnAa4DHJ7kCeFyzLGmFMk5I0ujMeUFAVZ0xw6bHjrguksaUcULSXYr2M95oHytrrhFJkqSOW9rpmxLqgLXzL95yrLj1yHLbuThXt5vzL61KL0DL83PJF36oVflzWpVe/Lk4D7/mzlblYf6fNVj8uTg1Dwm1Zv5/OVMs3jycsIBvry3/6J2Lc3Zt5+Jc7L/IxZ+Lc4KMaa5XV6ysyCBJktRxNs4kSZI6ZGmHNSVJ0uRzWHMo9pxJkiR1iD1nkiRphAqm7Dkbhj1nkiRJHWLPmSRJGp2CKm81NAx7ziRJkjrExpkkSVKHOKwpSZJGywsChmLPmSRJUod0uuesDj6w3QtWt5vJrNa2nCtz+w3tyredN29tu7kg28xTCrDqjnYT/13+kRNblX/pN+/bqnzbuTLf++Y/aVX+C7sOb1X+z657XKvyX/zivVqV19yye4q1N942//J3tpsflz0tk5Rb3kiz7UyfuW1Xu/J37m53gJbzB9/xUw9rVf679z6gVfnbfqBdDNp9cLvzf++Wc3Fue8z6VuVv+dE7WpW/18brW5WnXQjqNm9COxR7ziRJkjpkzsZZknOT7Ejy5b51f5jkK0m+mOQDSdYvai0ldZYxQpJGaz49Z+cBpw6suxh4UFU9GPgP4GUjrpek8XEexghJe1XB1FR3HmNozsZZVX0cuHFg3UVVtTf54VPAxkWom6QxYIyQpNEaxQUBvwS8ewT7kTSZjBHSSuMFAUMZ6oKAJL8H7AbeMUuZs5JsTbJ11575X4Ulafy1jhG7dy5d5SSpoxbcc5bk2cBTgMdWzdxErqotwBaAIw78QZvS0gqxoBhx8PHGCGkC1JjmenXFgnrOkpwKvBR4WlX5VVfSPowRkiZFkqOSXJzkiubnkdOUOSnJJ5Nc2lyl/nN9285L8vUklzSPk+Y65nxupXE+8Eng/kmuS/Jc4A3AYcDFzYHe1OaNSpocxghJE+5s4MNVdSLw4WZ50E7gWVX1I/SuXn/9wC2EfruqTmoel8x1wDmHNavqjGlWv2Wu10laGYwRkvZVk3ZBwGnAKc3ztwIfA36nv0BV/Uff828m2QHcA7h5IQd0hgBJkqSZHVtV25vn3wKOna1wkpOBdcDX+lb/QTPc+bokc857tvRza6bF3GptW94t581Lm7oArGpXvtrWh3ZzTbasPavubPeKOw9tV/+dx7dt67ebG7TtXJkPWfe9VuV/beM/tyr/Zy0nwrumVemVqQK1psXnqGWMaPs30zamTB3YLqS2/nbcMgal3VSQHHD5tlblj2BDuwPU4s7FOXXJZa3Kb2g7FyfrW5W/mmNalZ8YBUx1qufsmCRb+5a3NBci3SXJPwM/OM1rf69/oaoqyYxvLslxwF8DZ1bV3gDyMnqNunX0Ln76HeBVs1W40xOfS5IkDen6qto8W4GqmvHbdpJvJzmuqrY3ja8dM5Q7HPgH4Peq6lN9+97b63ZHkr8CXjJXhR3WlCRJo1VT3XkM7wLgzOb5mcAHBwskWQd8AHhbVb1vYNtxzc8ATwe+PPj6QTbOJEmSZvYa4PFJrgAe1yyTZHOSNzdl/hvwk8Czp7llxjuSfAn4EnAM8Oq5DuiwpiRJ0gyq6gbgsdOs3wo8r3n+duDtM7z+MW2PaeNMkiSNTAHVrQsCxo7DmpIkSR1iz5kkSRqdqlEl4q9Y9pxJkiR1iD1nkiRppMw5G449Z5IkSR1i40ySJKlDlnZYM4FVLdqDUy0TCtvOxbl7T6viWdtuLsi6s91cmYs9F+eB17ebp+72ltPCLfZcnH92Xbu5LNvOlbnYc3H+XavSK1RCre7Od8a2c3FOtZkXFGCx5+Jsac9VV7cqv65lzD2iNrYqD+3m4myra3NxThQvCBhKd6KgJEmS5u45S3Iu8BRgR1U9aGDbi4E/Au5RVdcvThUldZ1xQtJet3DTh/653tdy7GVRjV3cmU+f+nnAG4C39a9Msgl4AvCN0VdL0pg5D+OEJKCqTl3uOoy7OYc1q+rjwI3TbHod8FJ6MzVIWsGME5I0OgvKOUtyGrCtqr4w4vpImhDGCUlamNZXayY5GPhdekMV8yl/FnAWwIFrDm97OEljqE2c2CdGrDtikWsmSd23kJ6z+wD3Br6Q5GpgI/C5JD84XeGq2lJVm6tq87o1By+8ppLGybzjRH+MWLv2kCWupiR1T+ues6r6EvADe5ebwLvZq7Ak7WWckKSFm7PnLMn5wCeB+ye5LslzF79aksaJcUKSRmfOnrOqOmOO7fcaWW0kjSXjhCSNjjMESJIkdcjSzq0JsGb1/MvubrnvxZ6Lc127uTXbzsu32HNxHrqt7Vxn7druiz0X5xe/eK9W5f+Mbs3FqXlaNf+/nFrs75dp91dca9vVp/Xsg12bi/PbO1qVX9dy/0fQdi7OxbXYc3Fe2qq0Jpk9Z5IkSR1i40ySJKlDbJxJkiR1iI0zSZKkDrFxJkmS1CE2ziRJkjrExpkkSVKH2DiTJEnqEBtnkiRJHWLjTJIkqUNsnEmSJHVIqu38ksMcLPkOcM00m44Brl+yiiw/3+9km+n9nlBV91jqyowTY8RdfL+TzRihWS1p42zGSiRbq2rzctdjqfh+J9tKe79LYaWdU9/vZFtp71ftOawpSZLUITbOJEmSOqQrjbMty12BJeb7nWwr7f0uhZV2Tn2/k22lvV+11ImcM0mSJPV0pedMkiRJLHPjLMmpSb6a5MokZy9nXZZKkquTfCnJJUm2Lnd9Ri3JuUl2JPly37qjklyc5Irm55HLWcdRmuH9vjLJtuZ3fEmSJy1nHcfdSosTxghjhLRsjbMkq4FzgCcCDwTOSPLA5arPEvupqjppQi+lPg84dWDd2cCHq+pE4MPN8qQ4j/3fL8Drmt/xSVV14RLXaWKs4DhhjJgc52GMUEvL2XN2MnBlVV1VVbuAdwGnLWN9NAJV9XHgxoHVpwFvbZ6/FXj6UtZpMc3wfjU6xokJY4yQ5racjbMNwLV9y9c16yZdARcl+WySs5a7Mkvk2Kra3jz/FnDsclZmibwwyRebIY2JGaJZBisxThgjjBFa4bwgYOk9qqoeRm+Y5gVJfnK5K7SUqnd58KRfIvxG4D7AScB24I+XtTYaN8YIY4RWuOVsnG0DNvUtb2zWTbSq2tb83AF8gN6wzaT7dpLjAJqfO5a5Pouqqr5dVXuqagr4S1bG73ixrLg4YYwwRkjL2Tj7DHBiknsnWQecDlywjPVZdEkOSXLY3ufAE4Avz/6qiXABcGbz/Ezgg8tYl0W3959M42dYGb/jxbKi4oQxAjBGSKxZrgNX1e4kLwQ+BKwGzq2qS5erPkvkWOADSaB37t9ZVf+0vFUarSTnA6cAxyS5DngF8BrgPUmeC1wD/Lflq+FozfB+T0lyEr2hmauBX1mu+o27FRgnjBHGCMkZAiRJkrrECwIkSZI6xMaZJElSh9g4kyRJ6hAbZ5IkSR1i40ySJKlDbJxJkiR1iI0zSZKkDrFxJkmS1CH/P6TEssOYPWQQAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(3,2, figsize=(10,12))\n", + "fig.suptitle(\"Time and frequency channel covariance matrices\")\n", + "\n", + "ax[0,0].set_title(\"Freq. cov. Real\")\n", + "im = ax[0,0].imshow(FREQ_COV_MAT.real, vmin=-0.3, vmax=1.8)\n", + "ax[0,1].set_title(\"Freq. cov. Imag\")\n", + "im = ax[0,1].imshow(FREQ_COV_MAT.imag, vmin=-0.3, vmax=1.8)\n", + "\n", + "ax[1,0].set_title(\"Time cov. Real\")\n", + "im = ax[1,0].imshow(TIME_COV_MAT.real, vmin=-0.3, vmax=1.8)\n", + "ax[1,1].set_title(\"Time cov. Imag\")\n", + "im = ax[1,1].imshow(TIME_COV_MAT.imag, vmin=-0.3, vmax=1.8)\n", + "\n", + "ax[2,0].set_title(\"Space cov. Real\")\n", + "im = ax[2,0].imshow(SPACE_COV_MAT.real, vmin=-0.3, vmax=1.8)\n", + "ax[2,1].set_title(\"Space cov. Imag\")\n", + "im = ax[2,1].imshow(SPACE_COV_MAT.imag, vmin=-0.3, vmax=1.8)\n", + "\n", + "fig.subplots_adjust(right=0.8)\n", + "cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])\n", + "fig.colorbar(im, cax=cbar_ax);" + ] + }, + { + "cell_type": "markdown", + "id": "b5867869-195c-4daf-9b25-f226ea34aca2", + "metadata": {}, + "source": [ + "## Comparison of OFDM estimators" + ] + }, + { + "cell_type": "markdown", + "id": "0dabc5a8-37a4-4609-9966-ca3374602cf7", + "metadata": {}, + "source": [ + "This section focuses on comparing the available OFDM channel estimators in Sionna for the considered setup." + ] + }, + { + "cell_type": "markdown", + "id": "1bf539fc-04fd-48be-99a4-7afc25ae0b2f", + "metadata": {}, + "source": [ + "OFDM channel estimation consists of two steps:\n", + "\n", + "1. Channel estimation at pilot-carrying resource elements using [least-squares (LS)](https://nvlabs.github.io/sionna/api/ofdm.html#sionna.ofdm.LSChannelEstimator).\n", + "\n", + "2. Interpolation for data-carrying resource elements, for which three methods are available in Sionna:\n", + "\n", + "- [Nearest-neighbor](https://nvlabs.github.io/sionna/api/ofdm.html#sionna.ofdm.NearestNeighborInterpolator), which uses the channel estimate of the nearest pilot\n", + "- [Linear](https://nvlabs.github.io/sionna/api/ofdm.html#sionna.ofdm.LinearInterpolator), with optional averaging over the OFDM symbols (time dimension) for low mobility scenarios\n", + "- [LMMSE](https://nvlabs.github.io/sionna/api/ofdm.html#sionna.ofdm.LMMSEInterpolator), which requires knowledge of the time and frequency covariance matrices\n", + "\n", + "The LMMSE interpolator also features optional spatial smoothin, which requires the spatial covarance matrix. The [API documentation](https://nvlabs.github.io/sionna/api/ofdm.html#sionna.ofdm.LMMSEInterpolator) explains in more detail how this interpolator operates." + ] + }, + { + "cell_type": "markdown", + "id": "93b9fb03-97cd-4d9b-adb6-e0b00be506fb", + "metadata": {}, + "source": [ + "### End-to-end model" + ] + }, + { + "cell_type": "markdown", + "id": "b1568b07-702f-40a8-afb8-66115a5c4ea0", + "metadata": {}, + "source": [ + "In the next cell, we will create a Keras model which uses the interpolation method specified at initialization.\n", + "\n", + "It computes the mean square error (MSE) for a specified batch size and signal-to-noise ratio (SNR) (in dB).\n", + "\n", + "The following interpolation methods are available (set through the `int_method` parameter):\n", + "\n", + "- `\"nn\"` : Nearest-neighbor interpolation\n", + "- `\"lin\"` : Linear interpolation\n", + "- `\"lmmse\"` : LMMSE interpolation\n", + "\n", + "When LMMSE interpolation is used, it is required to specified the order in which interpolation and optional spatial smoothing is performed.\n", + "This is achieved using the `lmmse_order` parameter. For example, setting this parameter to `\"f-t\"` leads to frequency interpolation being performed first followed by time interpolation, and no spatial smoothing.\n", + "Setting it to `\"t-f-s\"` leads to time interpolation being performed first, followed by frequency interpolation, and finally spatial smoothing. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "e83d1d4a-7153-4856-b1ab-79177ad846a1", + "metadata": {}, + "outputs": [], + "source": [ + "class MIMOOFDMLink(Model):\n", + "\n", + " def __init__(self, int_method, lmmse_order=None, **kwargs):\n", + " super().__init__(kwargs)\n", + "\n", + " assert int_method in ('nn', 'lin', 'lmmse')\n", + "\n", + "\n", + " # Configure the resource grid\n", + " rg = ResourceGrid(num_ofdm_symbols=NUM_OFDM_SYMBOLS,\n", + " fft_size=FFT_SIZE,\n", + " subcarrier_spacing=SUBCARRIER_SPACING,\n", + " num_tx=1,\n", + " pilot_pattern=\"kronecker\",\n", + " pilot_ofdm_symbol_indices=[2,11])\n", + " self.rg = rg\n", + "\n", + " # Stream management\n", + " # Only a sinlge UT is considered for channel estimation\n", + " sm = StreamManagement([[1]], 1)\n", + "\n", + " ##################################\n", + " # Transmitter\n", + " ##################################\n", + "\n", + " self.qam_source = QAMSource(num_bits_per_symbol=2) # Modulation order does not impact the channel estimation. Set to QPSK\n", + " self.rg_mapper = ResourceGridMapper(rg)\n", + "\n", + " ##################################\n", + " # Channel\n", + " ##################################\n", + "\n", + " self.channel = OFDMChannel(CHANNEL_MODEL, rg, return_channel=True)\n", + "\n", + " ###################################\n", + " # Receiver\n", + " ###################################\n", + "\n", + " # Channel estimation\n", + " freq_cov_mat = tf.constant(FREQ_COV_MAT, tf.complex64)\n", + " time_cov_mat = tf.constant(TIME_COV_MAT, tf.complex64)\n", + " space_cov_mat = tf.constant(SPACE_COV_MAT, tf.complex64)\n", + " if int_method == 'nn':\n", + " self.channel_estimator = LSChannelEstimator(rg, interpolation_type='nn')\n", + " elif int_method == 'lin':\n", + " self.channel_estimator = LSChannelEstimator(rg, interpolation_type='lin')\n", + " elif int_method == 'lmmse':\n", + " lmmse_int_freq_first = LMMSEInterpolator(rg.pilot_pattern, time_cov_mat, freq_cov_mat, space_cov_mat, order=lmmse_order)\n", + " self.channel_estimator = LSChannelEstimator(rg, interpolator=lmmse_int_freq_first)\n", + "\n", + " @tf.function\n", + " def call(self, batch_size, snr_db):\n", + "\n", + "\n", + " ##################################\n", + " # Transmitter\n", + " ##################################\n", + "\n", + " x = self.qam_source([batch_size, 1, 1, self.rg.num_data_symbols])\n", + " x_rg = self.rg_mapper(x)\n", + "\n", + " ##################################\n", + " # Channel\n", + " ##################################\n", + "\n", + " no = tf.pow(10.0, -snr_db/10.0)\n", + " topology = gen_single_sector_topology(batch_size, 1, 'umi', min_ut_velocity=SPEED, max_ut_velocity=SPEED)\n", + " CHANNEL_MODEL.set_topology(*topology)\n", + " y_rg, h_freq = self.channel((x_rg, no))\n", + "\n", + " ###################################\n", + " # Channel estimation\n", + " ###################################\n", + "\n", + " h_hat,_ = self.channel_estimator((y_rg,no))\n", + "\n", + " ###################################\n", + " # MSE\n", + " ###################################\n", + "\n", + " mse = tf.reduce_mean(tf.square(tf.abs(h_freq-h_hat)))\n", + "\n", + " return mse" + ] + }, + { + "cell_type": "markdown", + "id": "e20e3285", + "metadata": {}, + "source": [ + "The next cell defines a function for evaluating the mean square error (MSE) of a `model` over a range of SNRs (`snr_dbs`).\n", + "\n", + "The `batch_size` and `num_it` parameters control the number of samples used to compute the MSE for each SNR value." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "2e89a42b-ca66-4448-b6da-ccf98d0ca3e1", + "metadata": {}, + "outputs": [], + "source": [ + "def evaluate_mse(model, snr_dbs, batch_size, num_it):\n", + "\n", + " # Casting model inputs to TensorFlow types to avoid\n", + " # re-building of the graph\n", + " snr_dbs = tf.cast(snr_dbs, tf.float32)\n", + " batch_size = tf.cast(batch_size, tf.int32)\n", + "\n", + " mses = []\n", + " for snr_db in snr_dbs:\n", + "\n", + " mse_ = 0.0\n", + " for _ in range(num_it):\n", + " mse_ += model(batch_size, snr_db).numpy()\n", + " # Averaging over the number of iterations\n", + " mse_ /= float(num_it)\n", + " mses.append(mse_)\n", + "\n", + " return mses" + ] + }, + { + "cell_type": "markdown", + "id": "dc14c206", + "metadata": {}, + "source": [ + "The next cell defines the evaluation parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "d8de113a-07ba-40e3-83ce-03c2fe0fd640", + "metadata": {}, + "outputs": [], + "source": [ + "# Range of SNR (in dB)\n", + "SNR_DBs = np.linspace(-10.0, 20.0, 20)\n", + "\n", + "# Number of iterations and batch size.\n", + "# These parameters control the number of samples used to compute each SNR value.\n", + "# The higher the number of samples is, the more accurate the MSE estimation is, at\n", + "# the cost of longer compute time.\n", + "BATCH_SIZE = 512\n", + "NUM_IT = 10\n", + "\n", + "# Interpolation/filtering order for the LMMSE interpolator.\n", + "# All valid configurations are listed.\n", + "# Some are commented to speed-up simulations.\n", + "# Uncomment configurations to evaluate them!\n", + "ORDERS = ['s-t-f', # Space - time - frequency\n", + " #'s-f-t', # Space - frequency - time\n", + " #'t-s-f', # Time - space - frequency\n", + " 't-f-s', # Time - frequency - space\n", + " #'f-t-s', # Frequency - time - space\n", + " #'f-s-t', # Frequency - space- time\n", + " #'f-t', # Frequency - time (no spatial smoothing)\n", + " 't-f' # Time - frequency (no spatial smoothing)\n", + " ]" + ] + }, + { + "cell_type": "markdown", + "id": "cb63359c", + "metadata": {}, + "source": [ + "The next cell evaluates the nearest-neighbor, linear, and LMMSE interpolator.\n", + "For the LMMSE interpolator, we loop through the configuration listed in `ORDERS`." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "28b4a2dd-eb4e-449f-958c-9d3985296d03", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /home/faycal/.local/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py:1176: calling gather (from tensorflow.python.ops.array_ops) with validate_indices is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "The `validate_indices` argument has no effect. Indices are always validated on CPU and never validated on GPU.\n" + ] + } + ], + "source": [ + "MSES = {}\n", + "\n", + "# Nearest-neighbor interpolation\n", + "e2e = MIMOOFDMLink(\"nn\")\n", + "MSES['nn'] = evaluate_mse(e2e, SNR_DBs, BATCH_SIZE, NUM_IT)\n", + "\n", + "# Linear interpolation\n", + "e2e = MIMOOFDMLink(\"lin\")\n", + "MSES['lin'] = evaluate_mse(e2e, SNR_DBs, BATCH_SIZE, NUM_IT)\n", + "\n", + "# LMMSE\n", + "for order in ORDERS:\n", + " e2e = MIMOOFDMLink(\"lmmse\", order)\n", + " MSES[f\"lmmse: {order}\"] = evaluate_mse(e2e, SNR_DBs, BATCH_SIZE, NUM_IT)\n" + ] + }, + { + "cell_type": "markdown", + "id": "cf1d55b0", + "metadata": {}, + "source": [ + "Finally, we plot the MSE." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "8a83a603-88ed-41d6-9eb6-927f73efe384", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,6))\n", + "\n", + "for est_label in MSES:\n", + " plt.semilogy(SNR_DBs, MSES[est_label], label=est_label)\n", + "\n", + "plt.xlabel(r\"SNR (dB)\")\n", + "plt.ylabel(\"MSE\")\n", + "plt.legend()\n", + "plt.grid(True)" + ] + }, + { + "cell_type": "markdown", + "id": "e4e29531-1b22-4c01-a46a-91bc8834ea96", + "metadata": {}, + "source": [ + "Unsurprisingly, the LMMSE interpolator leads to more accurate estimates compared to the two other methods, as it leverages knowledge of the the channel statistics.\n", + "Moreover, the order in which the LMMSE interpolation steps are performed strongly impacts the accuracy of the estimator. This is because the LMMSE interpolation operates in one dimension at a time which is not equivalent to full-blown LMMSE estimation across all dimensions at one.\n", + "\n", + "Also note that the order that leads to the best accuracy depends on the channel statistics. As a rule of thumb, it might be good to start with the dimension that is most strongly correlated (i.e., time in our example)." + ] + }, + { + "cell_type": "markdown", + "id": "b4556af5-ecaa-4770-86ae-165238ef0e21", + "metadata": {}, + "source": [ + "## Comparison of MIMO detectors" + ] + }, + { + "cell_type": "markdown", + "id": "07870ad0-b59f-4227-ab15-fba5331a89a8", + "metadata": {}, + "source": [ + "An OFDM MIMO receiver consists of two stages: **OFDM channel estimation** and **MIMO detection**.\n", + "\n", + "While the previous section focused on OFDM channel estimation, this section focuses now on MIMO detection.\n", + "\n", + "The following MIMO detection algorithms, all available out-of-the-box in Sionna, are considered:\n", + "\n", + "- [LMMSE equalization followed by APP demapping](https://nvlabs.github.io/sionna/api/mimo.html#sionna.mimo.LinearDetector)\n", + "- [K-Best detection](https://nvlabs.github.io/sionna/api/mimo.html#sionna.mimo.KBestDetector)\n", + "- [EP detection](https://nvlabs.github.io/sionna/api/mimo.html#sionna.mimo.EPDetector)\n", + "- [MMSE-PIC detection](https://nvlabs.github.io/sionna/api/mimo.html#sionna.mimo.MMSEPICDetector)\n", + "\n", + "Both perfect and imperfect channel state information is considered in the simulations.\n", + "LS estimation combined with LMMSE interpolation is used, with time-frequency-space smoothing (in this order, i.e., `order='t-f-s'`)." + ] + }, + { + "cell_type": "markdown", + "id": "5cda3dc1-1317-4b46-bcb0-8b3ef8a02e97", + "metadata": {}, + "source": [ + "### End-to-end model" + ] + }, + { + "cell_type": "markdown", + "id": "1bad6adf-eff6-451a-bef4-fe24731b1769", + "metadata": {}, + "source": [ + "A Keras model is created in the next cell, which uses the detection method specified at initialization.\n", + "\n", + "It computes either the coded bit error rate (BER) or the uncoded symbol error rate (SER), for a specified batch size, $E_b/N_0$ (in dB), and QAM modulation with a specified modulation order.\n", + "When computing the BER, a 5G LDPC code is used with the specified coderate.\n", + "\n", + "The following MIMO detection methods are considered (set through the `det_param` parameter):\n", + "\n", + "- `\"lmmse\"` : No parameter needed\n", + "- `\"k-best\"` : List size `k`, defaults to 64\n", + "- `\"ep\"` : Number of iterations `l`, defaults to 10\n", + "- `\"mmse-pic\"` : Number of self-iterations `num_it`, defaults to 4\n", + "\n", + "The `det_param` parameter corresponds to either `k`, `l`, or `num_it`, for K-Best, EP, or MMSE-PIC, respectively. If set to `None`, a default value is used according to the selected detector.\n", + "\n", + "The `perf_csi` parameter controls whether perfect CSI is assumed or not. If set to `False`, then LS combined with LMMSE interpolation is used to estimate the channel.\n", + "\n", + "You can easily add your own MIMO detector and channel estimator to this model for a fair and realistic benchmark." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "de827343-04d6-4a52-8ffe-f26c76bd8a6d", + "metadata": {}, + "outputs": [], + "source": [ + "class MIMOOFDMLink(Model):\n", + " \n", + " def __init__(self, output, det_method, perf_csi, num_tx, num_bits_per_symbol, det_param=None, coderate=0.5, **kwargs):\n", + " super().__init__(kwargs)\n", + " \n", + " assert det_method in ('lmmse', 'k-best', 'ep', 'mmse-pic'), \"Unknown detection method\"\n", + "\n", + " self._output = output\n", + " self.num_tx = num_tx\n", + " self.num_bits_per_symbol = num_bits_per_symbol\n", + " self.coderate = coderate\n", + " self.det_method = det_method\n", + " self.perf_csi = perf_csi\n", + " \n", + " # Configure the resource grid\n", + " rg = ResourceGrid(num_ofdm_symbols=NUM_OFDM_SYMBOLS,\n", + " fft_size=FFT_SIZE,\n", + " subcarrier_spacing=SUBCARRIER_SPACING,\n", + " num_tx=num_tx,\n", + " pilot_pattern=\"kronecker\",\n", + " pilot_ofdm_symbol_indices=[2,11])\n", + " self.rg = rg\n", + " \n", + " # Stream management\n", + " sm = StreamManagement(np.ones([1,num_tx], int), 1)\n", + " \n", + " # Codeword length and number of information bits per codeword\n", + " n = int(rg.num_data_symbols*num_bits_per_symbol)\n", + " k = int(coderate*n)\n", + " self.n = n\n", + " self.k = k\n", + " \n", + " # If output is symbol, then no FEC is used and hard decision are output\n", + " hard_out = (output == \"symbol\")\n", + " coded = (output == \"bit\")\n", + " self.hard_out = hard_out\n", + " self.coded = coded\n", + "\n", + " ##################################\n", + " # Transmitter\n", + " ##################################\n", + "\n", + " self.binary_source = BinarySource()\n", + " self.mapper = Mapper(constellation_type=\"qam\", num_bits_per_symbol=num_bits_per_symbol, return_indices=True)\n", + " self.rg_mapper = ResourceGridMapper(rg)\n", + " if coded:\n", + " self.encoder = LDPC5GEncoder(k, n, num_bits_per_symbol=num_bits_per_symbol)\n", + " \n", + " ##################################\n", + " # Channel\n", + " ##################################\n", + "\n", + " self.channel = OFDMChannel(CHANNEL_MODEL, rg, return_channel=True)\n", + " \n", + " ###################################\n", + " # Receiver\n", + " ###################################\n", + "\n", + " # Channel estimation\n", + " if not self.perf_csi:\n", + " freq_cov_mat = tf.constant(FREQ_COV_MAT, tf.complex64)\n", + " time_cov_mat = tf.constant(TIME_COV_MAT, tf.complex64)\n", + " space_cov_mat = tf.constant(SPACE_COV_MAT, tf.complex64)\n", + " lmmse_int_time_first = LMMSEInterpolator(rg.pilot_pattern, time_cov_mat, freq_cov_mat, space_cov_mat, order='t-f-s')\n", + " self.channel_estimator = LSChannelEstimator(rg, interpolator=lmmse_int_time_first)\n", + "\n", + " # Detection\n", + " if det_method == \"lmmse\":\n", + " self.detector = LinearDetector(\"lmmse\", output, \"app\", rg, sm, constellation_type=\"qam\", num_bits_per_symbol=num_bits_per_symbol, hard_out=hard_out)\n", + " elif det_method == 'k-best':\n", + " if det_param is None:\n", + " k = 64\n", + " else:\n", + " k = det_param\n", + " self.detector = KBestDetector(output, num_tx, k, rg, sm, constellation_type=\"qam\", num_bits_per_symbol=num_bits_per_symbol, hard_out=hard_out)\n", + " elif det_method == \"ep\":\n", + " if det_param is None:\n", + " l = 10\n", + " else:\n", + " l = det_param\n", + " self.detector = EPDetector(output, rg, sm, num_bits_per_symbol, l=l, hard_out=hard_out)\n", + " elif det_method == 'mmse-pic':\n", + " if det_param is None:\n", + " l = 4\n", + " else:\n", + " l = det_param\n", + " self.detector = MMSEPICDetector(output, rg, sm, 'app', num_iter=l, constellation_type=\"qam\", num_bits_per_symbol=num_bits_per_symbol, hard_out=hard_out)\n", + " \n", + " if coded:\n", + " self.decoder = LDPC5GDecoder(self.encoder, hard_out=False)\n", + " \n", + " @tf.function\n", + " def call(self, batch_size, ebno_db):\n", + " \n", + " \n", + " ##################################\n", + " # Transmitter\n", + " ##################################\n", + "\n", + " if self.coded:\n", + " b = self.binary_source([batch_size, self.num_tx, 1, self.k])\n", + " c = self.encoder(b)\n", + " else:\n", + " c = self.binary_source([batch_size, self.num_tx, 1, self.n])\n", + " bits_shape = tf.shape(c)\n", + " x,x_ind = self.mapper(c)\n", + " x_rg = self.rg_mapper(x)\n", + "\n", + " ##################################\n", + " # Channel\n", + " ##################################\n", + "\n", + " no = ebnodb2no(ebno_db, self.num_bits_per_symbol, self.coderate, resource_grid=self.rg)\n", + " topology = gen_single_sector_topology(batch_size, self.num_tx, 'umi', min_ut_velocity=SPEED, max_ut_velocity=SPEED)\n", + " CHANNEL_MODEL.set_topology(*topology)\n", + " y_rg, h_freq = self.channel((x_rg, no))\n", + " \n", + " ###################################\n", + " # Receiver\n", + " ###################################\n", + " \n", + " # Channel estimation\n", + " if self.perf_csi:\n", + " h_hat = h_freq\n", + " err_var = 0.0\n", + " else:\n", + " h_hat,err_var = self.channel_estimator((y_rg,no))\n", + " \n", + " # Detection\n", + " if self.det_method == \"mmse-pic\":\n", + " if self._output == \"bit\":\n", + " prior_shape = bits_shape\n", + " elif self._output == \"symbol\":\n", + " prior_shape = tf.concat([tf.shape(x), [self.num_bits_per_symbol]], axis=0)\n", + " prior = tf.zeros(prior_shape)\n", + " det_out = self.detector((y_rg,h_hat,prior,err_var,no))\n", + " else:\n", + " det_out = self.detector((y_rg,h_hat,err_var,no))\n", + " \n", + " # (Decoding) and output\n", + " if self._output == \"bit\":\n", + " llr = tf.reshape(det_out, bits_shape)\n", + " b_hat = self.decoder(llr)\n", + " return b, b_hat\n", + " elif self._output == \"symbol\":\n", + " x_hat = tf.reshape(det_out, tf.shape(x_ind))\n", + " return x_ind, x_hat" + ] + }, + { + "cell_type": "markdown", + "id": "cce1c898-f431-4613-b6fc-ee702e932ecf", + "metadata": {}, + "source": [ + "The following function is used to evaluate all of the considered detectors for a given setup: It instantiates the end-to-end systems, runs the simulations, and returns the BER or SER." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "990cefa6-abed-42c6-969c-75bdfdab7946", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def run_sim(num_tx, num_bits_per_symbol, output, ebno_dbs, perf_csi, det_param=None):\n", + "\n", + " lmmse = MIMOOFDMLink(output, \"lmmse\", perf_csi, num_tx, num_bits_per_symbol, det_param)\n", + " k_best = MIMOOFDMLink(output, \"k-best\", perf_csi, num_tx, num_bits_per_symbol, det_param)\n", + " ep = MIMOOFDMLink(output, \"ep\", perf_csi, num_tx, num_bits_per_symbol, det_param)\n", + " mmse_pic = MIMOOFDMLink(output, \"mmse-pic\", perf_csi, num_tx, num_bits_per_symbol, det_param)\n", + " \n", + " if output == \"symbol\":\n", + " soft_estimates = False\n", + " ylabel = \"Uncoded SER\"\n", + " else:\n", + " soft_estimates = True\n", + " ylabel = \"Coded BER\"\n", + " \n", + " er_lmmse,_ = sim_ber(lmmse,\n", + " ebno_dbs,\n", + " batch_size=64,\n", + " max_mc_iter=200,\n", + " num_target_block_errors=200,\n", + " soft_estimates=soft_estimates);\n", + "\n", + " er_ep,_ = sim_ber(ep,\n", + " ebno_dbs,\n", + " batch_size=64,\n", + " max_mc_iter=200,\n", + " num_target_block_errors=200,\n", + " soft_estimates=soft_estimates);\n", + " \n", + " er_kbest,_ = sim_ber(k_best,\n", + " ebno_dbs,\n", + " batch_size=64,\n", + " max_mc_iter=200,\n", + " num_target_block_errors=200,\n", + " soft_estimates=soft_estimates);\n", + " \n", + " er_mmse_pic,_ = sim_ber(mmse_pic,\n", + " ebno_dbs,\n", + " batch_size=64,\n", + " max_mc_iter=200,\n", + " num_target_block_errors=200,\n", + " soft_estimates=soft_estimates);\n", + " \n", + " return er_lmmse, er_ep, er_kbest, er_mmse_pic" + ] + }, + { + "cell_type": "markdown", + "id": "f5b2eb9f-685c-4481-a5c1-a2ce7d132db1", + "metadata": {}, + "source": [ + "The next cell defines the simulation parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "386ef947-c7db-48be-be7e-ddaade014537", + "metadata": {}, + "outputs": [], + "source": [ + "# Range of SNR (dB)\n", + "EBN0_DBs = np.linspace(-10., 20.0, 10)\n", + "\n", + "# Number of transmitters\n", + "NUM_TX = 4\n", + "\n", + "# Modulation order (number of bits per symbol)\n", + "NUM_BITS_PER_SYMBOL = 4 # 16-QAM" + ] + }, + { + "cell_type": "markdown", + "id": "c0f7deac-a797-4ae9-a1e1-de32cb4b4ab2", + "metadata": {}, + "source": [ + "We start by evaluating the uncoded SER. The next cell runs the simulations with perfect CSI and channel estimation. Results are stored in the `SER` dictionnary." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "d8fc9f89-dff2-4a46-b712-3b032aff67bd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", + "---------------------------------------------------------------------------------------------------------------------------------------\n", + " -10.0 | 6.3274e-01 | 1.0000e+00 | 93302 | 147456 | 256 | 256 | 4.7 |reached target block errors\n", + " -6.667 | 5.0724e-01 | 1.0000e+00 | 74796 | 147456 | 256 | 256 | 0.1 |reached target block errors\n", + " -3.333 | 3.7246e-01 | 9.9609e-01 | 54922 | 147456 | 255 | 256 | 0.1 |reached target block errors\n", + " 0.0 | 2.3949e-01 | 9.7656e-01 | 35314 | 147456 | 250 | 256 | 0.1 |reached target block errors\n", + " 3.333 | 1.2375e-01 | 8.4766e-01 | 18247 | 147456 | 217 | 256 | 0.1 |reached target block errors\n", + " 6.667 | 5.7034e-02 | 6.6211e-01 | 16820 | 294912 | 339 | 512 | 0.1 |reached target block errors\n", + " 10.0 | 2.5584e-02 | 4.6680e-01 | 7545 | 294912 | 239 | 512 | 0.1 |reached target block errors\n", + " 13.333 | 6.7546e-03 | 2.6302e-01 | 2988 | 442368 | 202 | 768 | 0.2 |reached target block errors\n", + " 16.667 | 2.0913e-03 | 1.0840e-01 | 2467 | 1179648 | 222 | 2048 | 0.5 |reached target block errors\n", + " 20.0 | 5.6708e-04 | 3.9621e-02 | 1756 | 3096576 | 213 | 5376 | 1.4 |reached target block errors\n", + "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", + "---------------------------------------------------------------------------------------------------------------------------------------\n", + " -10.0 | 6.1009e-01 | 1.0000e+00 | 89961 | 147456 | 256 | 256 | 5.3 |reached target block errors\n", + " -6.667 | 4.8094e-01 | 1.0000e+00 | 70918 | 147456 | 256 | 256 | 0.1 |reached target block errors\n", + " -3.333 | 2.9869e-01 | 9.9609e-01 | 44044 | 147456 | 255 | 256 | 0.1 |reached target block errors\n", + " 0.0 | 1.4774e-01 | 9.7656e-01 | 21785 | 147456 | 250 | 256 | 0.1 |reached target block errors\n", + " 3.333 | 6.1442e-02 | 7.9688e-01 | 9060 | 147456 | 204 | 256 | 0.1 |reached target block errors\n", + " 6.667 | 2.0511e-02 | 4.3750e-01 | 6049 | 294912 | 224 | 512 | 0.2 |reached target block errors\n", + " 10.0 | 4.6556e-03 | 1.4453e-01 | 4119 | 884736 | 222 | 1536 | 0.5 |reached target block errors\n", + " 13.333 | 8.7167e-04 | 5.3385e-02 | 1928 | 2211840 | 205 | 3840 | 1.2 |reached target block errors\n", + " 16.667 | 1.0502e-04 | 1.1217e-02 | 1084 | 10321920 | 201 | 17920 | 5.6 |reached target block errors\n", + " 20.0 | 2.3600e-05 | 2.9688e-03 | 696 | 29491200 | 152 | 51200 | 15.8 |reached max iter \n", + "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", + "---------------------------------------------------------------------------------------------------------------------------------------\n", + " -10.0 | 6.1452e-01 | 1.0000e+00 | 90615 | 147456 | 256 | 256 | 5.6 |reached target block errors\n", + " -6.667 | 4.8480e-01 | 1.0000e+00 | 71487 | 147456 | 256 | 256 | 0.5 |reached target block errors\n", + " -3.333 | 3.0013e-01 | 9.8828e-01 | 44256 | 147456 | 253 | 256 | 0.5 |reached target block errors\n", + " 0.0 | 1.2075e-01 | 9.4141e-01 | 17806 | 147456 | 241 | 256 | 0.5 |reached target block errors\n", + " 3.333 | 4.2379e-02 | 7.3242e-01 | 12498 | 294912 | 375 | 512 | 0.9 |reached target block errors\n", + " 6.667 | 1.5837e-02 | 3.4635e-01 | 7006 | 442368 | 266 | 768 | 1.4 |reached target block errors\n", + " 10.0 | 4.0855e-03 | 1.1775e-01 | 4217 | 1032192 | 211 | 1792 | 3.3 |reached target block errors\n", + " 13.333 | 7.5164e-04 | 3.3040e-02 | 2660 | 3538944 | 203 | 6144 | 11.1 |reached target block errors\n", + " 16.667 | 9.1727e-05 | 1.0116e-02 | 1055 | 11501568 | 202 | 19968 | 36.2 |reached target block errors\n", + " 20.0 | 2.4482e-05 | 2.5000e-03 | 722 | 29491200 | 128 | 51200 | 92.6 |reached max iter \n", + "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", + "---------------------------------------------------------------------------------------------------------------------------------------\n", + " -10.0 | 6.0616e-01 | 1.0000e+00 | 89382 | 147456 | 256 | 256 | 5.0 |reached target block errors\n", + " -6.667 | 4.9781e-01 | 1.0000e+00 | 73405 | 147456 | 256 | 256 | 0.1 |reached target block errors\n", + " -3.333 | 2.9688e-01 | 1.0000e+00 | 43777 | 147456 | 256 | 256 | 0.1 |reached target block errors\n", + " 0.0 | 1.3372e-01 | 9.5703e-01 | 19718 | 147456 | 245 | 256 | 0.1 |reached target block errors\n", + " 3.333 | 4.9093e-02 | 8.1250e-01 | 7239 | 147456 | 208 | 256 | 0.1 |reached target block errors\n", + " 6.667 | 1.7320e-02 | 4.4531e-01 | 5108 | 294912 | 228 | 512 | 0.2 |reached target block errors\n", + " 10.0 | 4.3996e-03 | 2.3438e-01 | 2595 | 589824 | 240 | 1024 | 0.3 |reached target block errors\n", + " 13.333 | 7.8729e-04 | 7.3509e-02 | 1277 | 1622016 | 207 | 2816 | 0.9 |reached target block errors\n", + " 16.667 | 1.5014e-04 | 1.8714e-02 | 952 | 6340608 | 206 | 11008 | 3.6 |reached target block errors\n", + " 20.0 | 2.7364e-05 | 3.7695e-03 | 807 | 29491200 | 193 | 51200 | 16.9 |reached max iter \n", + "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", + "---------------------------------------------------------------------------------------------------------------------------------------\n", + " -10.0 | 6.5757e-01 | 1.0000e+00 | 96962 | 147456 | 256 | 256 | 4.5 |reached target block errors\n", + " -6.667 | 5.3936e-01 | 1.0000e+00 | 79532 | 147456 | 256 | 256 | 0.2 |reached target block errors\n", + " -3.333 | 4.2834e-01 | 1.0000e+00 | 63161 | 147456 | 256 | 256 | 0.2 |reached target block errors\n", + " 0.0 | 3.2353e-01 | 9.8828e-01 | 47706 | 147456 | 253 | 256 | 0.2 |reached target block errors\n", + " 3.333 | 1.8555e-01 | 9.4141e-01 | 27360 | 147456 | 241 | 256 | 0.2 |reached target block errors\n", + " 6.667 | 1.0126e-01 | 7.9297e-01 | 14931 | 147456 | 203 | 256 | 0.2 |reached target block errors\n", + " 10.0 | 3.7248e-02 | 5.4492e-01 | 10985 | 294912 | 279 | 512 | 0.4 |reached target block errors\n", + " 13.333 | 2.3170e-02 | 4.2773e-01 | 6833 | 294912 | 219 | 512 | 0.4 |reached target block errors\n", + " 16.667 | 6.8410e-03 | 2.1777e-01 | 4035 | 589824 | 223 | 1024 | 0.9 |reached target block errors\n", + " 20.0 | 4.8977e-03 | 1.7188e-01 | 3611 | 737280 | 220 | 1280 | 1.1 |reached target block errors\n", + "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", + "---------------------------------------------------------------------------------------------------------------------------------------\n", + " -10.0 | 6.5626e-01 | 1.0000e+00 | 96770 | 147456 | 256 | 256 | 5.3 |reached target block errors\n", + " -6.667 | 5.3429e-01 | 1.0000e+00 | 78785 | 147456 | 256 | 256 | 0.2 |reached target block errors\n", + " -3.333 | 3.4984e-01 | 1.0000e+00 | 51586 | 147456 | 256 | 256 | 0.2 |reached target block errors\n", + " 0.0 | 2.3107e-01 | 9.8828e-01 | 34072 | 147456 | 253 | 256 | 0.2 |reached target block errors\n", + " 3.333 | 9.8416e-02 | 8.3203e-01 | 14512 | 147456 | 213 | 256 | 0.2 |reached target block errors\n", + " 6.667 | 3.5495e-02 | 6.2305e-01 | 10468 | 294912 | 319 | 512 | 0.5 |reached target block errors\n", + " 10.0 | 1.1027e-02 | 3.7370e-01 | 4878 | 442368 | 287 | 768 | 0.7 |reached target block errors\n", + " 13.333 | 4.2103e-03 | 1.7057e-01 | 3725 | 884736 | 262 | 1536 | 1.4 |reached target block errors\n", + " 16.667 | 1.5082e-03 | 7.8125e-02 | 2224 | 1474560 | 200 | 2560 | 2.4 |reached target block errors\n", + " 20.0 | 1.9312e-03 | 6.3101e-02 | 3702 | 1916928 | 210 | 3328 | 3.0 |reached target block errors\n", + "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", + "---------------------------------------------------------------------------------------------------------------------------------------\n", + " -10.0 | 6.5530e-01 | 1.0000e+00 | 96628 | 147456 | 256 | 256 | 5.9 |reached target block errors\n", + " -6.667 | 5.4480e-01 | 1.0000e+00 | 80334 | 147456 | 256 | 256 | 0.6 |reached target block errors\n", + " -3.333 | 3.8673e-01 | 9.9219e-01 | 57026 | 147456 | 254 | 256 | 0.6 |reached target block errors\n", + " 0.0 | 2.1018e-01 | 9.8438e-01 | 30993 | 147456 | 252 | 256 | 0.6 |reached target block errors\n", + " 3.333 | 8.1733e-02 | 8.1250e-01 | 12052 | 147456 | 208 | 256 | 0.6 |reached target block errors\n", + " 6.667 | 3.1857e-02 | 5.5859e-01 | 9395 | 294912 | 286 | 512 | 1.2 |reached target block errors\n", + " 10.0 | 9.6594e-03 | 2.7995e-01 | 4273 | 442368 | 215 | 768 | 1.9 |reached target block errors\n", + " 13.333 | 3.6594e-03 | 1.5937e-01 | 2698 | 737280 | 204 | 1280 | 3.1 |reached target block errors\n", + " 16.667 | 2.2942e-03 | 8.4375e-02 | 3383 | 1474560 | 216 | 2560 | 6.2 |reached target block errors\n", + " 20.0 | 1.4678e-03 | 5.6920e-02 | 3030 | 2064384 | 204 | 3584 | 8.7 |reached target block errors\n", + "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", + "---------------------------------------------------------------------------------------------------------------------------------------\n", + " -10.0 | 6.5718e-01 | 1.0000e+00 | 96905 | 147456 | 256 | 256 | 5.3 |reached target block errors\n", + " -6.667 | 5.3097e-01 | 1.0000e+00 | 78294 | 147456 | 256 | 256 | 0.2 |reached target block errors\n", + " -3.333 | 3.6694e-01 | 1.0000e+00 | 54108 | 147456 | 256 | 256 | 0.2 |reached target block errors\n", + " 0.0 | 2.3520e-01 | 9.7656e-01 | 34682 | 147456 | 250 | 256 | 0.2 |reached target block errors\n", + " 3.333 | 9.2502e-02 | 8.5156e-01 | 13640 | 147456 | 218 | 256 | 0.2 |reached target block errors\n", + " 6.667 | 3.4912e-02 | 6.6211e-01 | 10296 | 294912 | 339 | 512 | 0.5 |reached target block errors\n", + " 10.0 | 1.3431e-02 | 4.2383e-01 | 3961 | 294912 | 217 | 512 | 0.5 |reached target block errors\n", + " 13.333 | 5.7865e-03 | 2.5098e-01 | 3413 | 589824 | 257 | 1024 | 0.9 |reached target block errors\n", + " 16.667 | 2.7466e-03 | 1.1279e-01 | 3240 | 1179648 | 231 | 2048 | 1.9 |reached target block errors\n", + " 20.0 | 1.2919e-03 | 6.6732e-02 | 2286 | 1769472 | 205 | 3072 | 2.9 |reached target block errors\n" + ] + } + ], + "source": [ + "SER = {} # Store the results\n", + "\n", + "# Perfect CSI\n", + "ser_lmmse, ser_ep, ser_kbest, ser_mmse_pic = run_sim(NUM_TX, NUM_BITS_PER_SYMBOL, \"symbol\", EBN0_DBs, True)\n", + "SER['Perf. CSI / LMMSE'] = ser_lmmse\n", + "SER['Perf. CSI / EP'] = ser_ep\n", + "SER['Perf. CSI / K-Best'] = ser_kbest\n", + "SER['Perf. CSI / MMSE-PIC'] = ser_mmse_pic\n", + "\n", + "# Imperfect CSI\n", + "ser_lmmse, ser_ep, ser_kbest, ser_mmse_pic = run_sim(NUM_TX, NUM_BITS_PER_SYMBOL, \"symbol\", EBN0_DBs, False)\n", + "SER['Ch. Est. / LMMSE'] = ser_lmmse\n", + "SER['Ch. Est. / EP'] = ser_ep\n", + "SER['Ch. Est. / K-Best'] = ser_kbest\n", + "SER['Ch. Est. / MMSE-PIC'] = ser_mmse_pic" + ] + }, + { + "cell_type": "markdown", + "id": "a7b436e3-1fc4-4c6a-b2b3-338cf7158851", + "metadata": {}, + "source": [ + "Next, we evaluate the coded BER. The cell below runs the simulations with perfect CSI and channel estimation. Results are stored in the `BER` dictionnary." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "56dda021-51c5-4c23-8675-e9e08e556c4f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", + "---------------------------------------------------------------------------------------------------------------------------------------\n", + " -10.0 | 1.8888e-01 | 8.5547e-01 | 55703 | 294912 | 219 | 256 | 5.8 |reached target block errors\n", + " -6.667 | 1.1261e-01 | 5.8984e-01 | 66421 | 589824 | 302 | 512 | 0.2 |reached target block errors\n", + " -3.333 | 5.7696e-02 | 3.1641e-01 | 51046 | 884736 | 243 | 768 | 0.3 |reached target block errors\n", + " 0.0 | 2.5274e-02 | 1.5039e-01 | 44721 | 1769472 | 231 | 1536 | 0.7 |reached target block errors\n", + " 3.333 | 1.0029e-02 | 6.6732e-02 | 35491 | 3538944 | 205 | 3072 | 1.4 |reached target block errors\n", + " 6.667 | 2.6471e-03 | 1.9627e-02 | 32007 | 12091392 | 206 | 10496 | 4.6 |reached target block errors\n", + " 10.0 | 5.2647e-04 | 4.3645e-03 | 27792 | 52789248 | 200 | 45824 | 20.0 |reached target block errors\n", + " 13.333 | 8.6721e-05 | 5.6641e-04 | 5115 | 58982400 | 29 | 51200 | 22.2 |reached max iter \n", + " 16.667 | 1.6174e-05 | 9.7656e-05 | 954 | 58982400 | 5 | 51200 | 22.2 |reached max iter \n", + " 20.0 | 1.5428e-06 | 1.9531e-05 | 91 | 58982400 | 1 | 51200 | 22.2 |reached max iter \n", + "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", + "---------------------------------------------------------------------------------------------------------------------------------------\n", + " -10.0 | 1.6863e-01 | 7.8125e-01 | 49731 | 294912 | 200 | 256 | 5.6 |reached target block errors\n", + " -6.667 | 9.9314e-02 | 5.1172e-01 | 58578 | 589824 | 262 | 512 | 0.3 |reached target block errors\n", + " -3.333 | 4.7239e-02 | 2.7474e-01 | 41794 | 884736 | 211 | 768 | 0.4 |reached target block errors\n", + " 0.0 | 1.4008e-02 | 8.8108e-02 | 37181 | 2654208 | 203 | 2304 | 1.2 |reached target block errors\n", + " 3.333 | 2.2594e-03 | 1.6276e-02 | 31983 | 14155776 | 200 | 12288 | 6.1 |reached target block errors\n", + " 6.667 | 3.9112e-04 | 2.9883e-03 | 23069 | 58982400 | 153 | 51200 | 25.2 |reached max iter \n", + " 10.0 | 2.0972e-05 | 2.7344e-04 | 1237 | 58982400 | 14 | 51200 | 25.1 |reached max iter \n", + " 13.333 | 0.0000e+00 | 0.0000e+00 | 0 | 58982400 | 0 | 51200 | 25.1 |reached max iter \n", + "\n", + "Simulation stopped as no error occurred @ EbNo = 13.3 dB.\n", + "\n", + "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", + "---------------------------------------------------------------------------------------------------------------------------------------\n", + " -10.0 | 2.1076e-01 | 9.2969e-01 | 62155 | 294912 | 238 | 256 | 6.5 |reached target block errors\n", + " -6.667 | 1.0710e-01 | 6.1914e-01 | 63171 | 589824 | 317 | 512 | 1.1 |reached target block errors\n", + " -3.333 | 3.8923e-02 | 2.4023e-01 | 45916 | 1179648 | 246 | 1024 | 2.2 |reached target block errors\n", + " 0.0 | 1.1103e-02 | 7.1378e-02 | 36018 | 3244032 | 201 | 2816 | 6.2 |reached target block errors\n", + " 3.333 | 2.2757e-03 | 1.6927e-02 | 32215 | 14155776 | 208 | 12288 | 27.0 |reached target block errors\n", + " 6.667 | 2.9185e-04 | 2.1875e-03 | 17214 | 58982400 | 112 | 51200 | 112.1 |reached max iter \n", + " 10.0 | 3.9978e-05 | 2.9297e-04 | 2358 | 58982400 | 15 | 51200 | 112.1 |reached max iter \n", + " 13.333 | 0.0000e+00 | 0.0000e+00 | 0 | 58982400 | 0 | 51200 | 112.1 |reached max iter \n", + "\n", + "Simulation stopped as no error occurred @ EbNo = 13.3 dB.\n", + "\n", + "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", + "---------------------------------------------------------------------------------------------------------------------------------------\n", + " -10.0 | 1.8315e-01 | 8.5547e-01 | 54013 | 294912 | 219 | 256 | 5.2 |reached target block errors\n", + " -6.667 | 1.1446e-01 | 5.9180e-01 | 67512 | 589824 | 303 | 512 | 0.3 |reached target block errors\n", + " -3.333 | 5.0348e-02 | 2.9297e-01 | 44545 | 884736 | 225 | 768 | 0.4 |reached target block errors\n", + " 0.0 | 1.6928e-02 | 1.0596e-01 | 39937 | 2359296 | 217 | 2048 | 1.1 |reached target block errors\n", + " 3.333 | 2.9010e-03 | 2.4148e-02 | 28233 | 9732096 | 204 | 8448 | 4.4 |reached target block errors\n", + " 6.667 | 5.5365e-04 | 4.4611e-03 | 28737 | 51904512 | 201 | 45056 | 23.3 |reached target block errors\n", + " 10.0 | 6.0560e-05 | 7.4219e-04 | 3572 | 58982400 | 38 | 51200 | 26.5 |reached max iter \n", + " 13.333 | 2.7466e-06 | 3.9063e-05 | 162 | 58982400 | 2 | 51200 | 26.5 |reached max iter \n", + " 16.667 | 1.6954e-08 | 1.9531e-05 | 1 | 58982400 | 1 | 51200 | 26.6 |reached max iter \n", + " 20.0 | 0.0000e+00 | 0.0000e+00 | 0 | 58982400 | 0 | 51200 | 26.4 |reached max iter \n", + "\n", + "Simulation stopped as no error occurred @ EbNo = 20.0 dB.\n", + "\n", + "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", + "---------------------------------------------------------------------------------------------------------------------------------------\n", + " -10.0 | 2.0073e-01 | 8.4375e-01 | 59199 | 294912 | 216 | 256 | 5.9 |reached target block errors\n", + " -6.667 | 1.5016e-01 | 6.8750e-01 | 88566 | 589824 | 352 | 512 | 0.5 |reached target block errors\n", + " -3.333 | 7.8442e-02 | 4.0430e-01 | 46267 | 589824 | 207 | 512 | 0.5 |reached target block errors\n", + " 0.0 | 3.9502e-02 | 2.1777e-01 | 46598 | 1179648 | 223 | 1024 | 1.1 |reached target block errors\n", + " 3.333 | 1.7726e-02 | 1.0791e-01 | 41822 | 2359296 | 221 | 2048 | 2.1 |reached target block errors\n", + " 6.667 | 6.3252e-03 | 3.7946e-02 | 39173 | 6193152 | 204 | 5376 | 5.6 |reached target block errors\n", + " 10.0 | 2.4057e-03 | 1.5855e-02 | 36183 | 15040512 | 207 | 13056 | 13.6 |reached target block errors\n", + " 13.333 | 9.3448e-04 | 5.5962e-03 | 38858 | 41582592 | 202 | 36096 | 37.5 |reached target block errors\n", + " 16.667 | 2.7039e-04 | 2.0117e-03 | 15948 | 58982400 | 103 | 51200 | 53.3 |reached max iter \n", + " 20.0 | 2.7354e-04 | 1.8555e-03 | 16134 | 58982400 | 95 | 51200 | 53.3 |reached max iter \n", + "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", + "---------------------------------------------------------------------------------------------------------------------------------------\n", + " -10.0 | 1.9646e-01 | 8.9453e-01 | 57939 | 294912 | 229 | 256 | 6.6 |reached target block errors\n", + " -6.667 | 1.3310e-01 | 6.5625e-01 | 78508 | 589824 | 336 | 512 | 0.6 |reached target block errors\n", + " -3.333 | 6.3611e-02 | 3.4505e-01 | 56279 | 884736 | 265 | 768 | 0.8 |reached target block errors\n", + " 0.0 | 2.7651e-02 | 1.6562e-01 | 40773 | 1474560 | 212 | 1280 | 1.4 |reached target block errors\n", + " 3.333 | 8.5775e-03 | 5.2083e-02 | 37944 | 4423680 | 200 | 3840 | 4.2 |reached target block errors\n", + " 6.667 | 2.0052e-03 | 1.3470e-02 | 34298 | 17104896 | 200 | 14848 | 16.4 |reached target block errors\n", + " 10.0 | 7.6427e-04 | 5.2083e-03 | 33809 | 44236800 | 200 | 38400 | 42.3 |reached target block errors\n", + " 13.333 | 4.1326e-04 | 2.8516e-03 | 24375 | 58982400 | 146 | 51200 | 56.1 |reached max iter \n", + " 16.667 | 2.0630e-04 | 1.6602e-03 | 12168 | 58982400 | 85 | 51200 | 56.0 |reached max iter \n", + " 20.0 | 1.7263e-04 | 1.6211e-03 | 10182 | 58982400 | 83 | 51200 | 56.3 |reached max iter \n", + "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", + "---------------------------------------------------------------------------------------------------------------------------------------\n", + " -10.0 | 2.4428e-01 | 9.7266e-01 | 72041 | 294912 | 249 | 256 | 7.3 |reached target block errors\n", + " -6.667 | 1.5539e-01 | 7.4414e-01 | 91650 | 589824 | 381 | 512 | 1.4 |reached target block errors\n", + " -3.333 | 6.4181e-02 | 3.5286e-01 | 56783 | 884736 | 271 | 768 | 2.1 |reached target block errors\n", + " 0.0 | 2.6555e-02 | 1.4844e-01 | 46988 | 1769472 | 228 | 1536 | 4.3 |reached target block errors\n", + " 3.333 | 5.6042e-03 | 3.4307e-02 | 38013 | 6782976 | 202 | 5888 | 16.4 |reached target block errors\n", + " 6.667 | 1.4845e-03 | 9.5538e-03 | 36337 | 24477696 | 203 | 21248 | 59.0 |reached target block errors\n", + " 10.0 | 5.6710e-04 | 3.6719e-03 | 33449 | 58982400 | 188 | 51200 | 142.6 |reached max iter \n", + " 13.333 | 3.0056e-04 | 1.9727e-03 | 17728 | 58982400 | 101 | 51200 | 142.7 |reached max iter \n", + " 16.667 | 2.2124e-04 | 1.5625e-03 | 13049 | 58982400 | 80 | 51200 | 142.7 |reached max iter \n", + " 20.0 | 1.3379e-04 | 1.0156e-03 | 7891 | 58982400 | 52 | 51200 | 142.5 |reached max iter \n", + "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", + "---------------------------------------------------------------------------------------------------------------------------------------\n", + " -10.0 | 2.1431e-01 | 9.0234e-01 | 63203 | 294912 | 231 | 256 | 6.1 |reached target block errors\n", + " -6.667 | 1.3881e-01 | 6.6016e-01 | 81876 | 589824 | 338 | 512 | 0.6 |reached target block errors\n", + " -3.333 | 8.4296e-02 | 4.5117e-01 | 49720 | 589824 | 231 | 512 | 0.6 |reached target block errors\n", + " 0.0 | 3.1447e-02 | 1.9062e-01 | 46370 | 1474560 | 244 | 1280 | 1.4 |reached target block errors\n", + " 3.333 | 9.9915e-03 | 6.5505e-02 | 38306 | 3833856 | 218 | 3328 | 3.7 |reached target block errors\n", + " 6.667 | 2.2112e-03 | 1.6342e-02 | 31954 | 14450688 | 205 | 12544 | 14.0 |reached target block errors\n", + " 10.0 | 8.0055e-04 | 6.0562e-03 | 30456 | 38043648 | 200 | 33024 | 36.9 |reached target block errors\n", + " 13.333 | 5.1027e-04 | 3.6719e-03 | 30097 | 58982400 | 188 | 51200 | 57.3 |reached max iter \n", + " 16.667 | 2.7083e-04 | 2.4609e-03 | 15974 | 58982400 | 126 | 51200 | 57.2 |reached max iter \n", + " 20.0 | 2.3241e-04 | 1.7383e-03 | 13708 | 58982400 | 89 | 51200 | 57.2 |reached max iter \n" + ] + } + ], + "source": [ + "BER = {} # Store the results\n", + "\n", + "# Perfect CSI\n", + "ber_lmmse, ber_ep, ber_kbest, ber_mmse_pic = run_sim(NUM_TX, NUM_BITS_PER_SYMBOL, \"bit\", EBN0_DBs, True)\n", + "BER['Perf. CSI / LMMSE'] = ber_lmmse\n", + "BER['Perf. CSI / EP'] = ber_ep\n", + "BER['Perf. CSI / K-Best'] = ber_kbest\n", + "BER['Perf. CSI / MMSE-PIC'] = ber_mmse_pic\n", + "\n", + "# Imperfect CSI\n", + "ber_lmmse, ber_ep, ber_kbest, ber_mmse_pic = run_sim(NUM_TX, NUM_BITS_PER_SYMBOL, \"bit\", EBN0_DBs, False)\n", + "BER['Ch. Est. / LMMSE'] = ber_lmmse\n", + "BER['Ch. Est. / EP'] = ber_ep\n", + "BER['Ch. Est. / K-Best'] = ber_kbest\n", + "BER['Ch. Est. / MMSE-PIC'] = ber_mmse_pic" + ] + }, + { + "cell_type": "markdown", + "id": "2755232f-5704-4246-9fc2-c08304f8cf5a", + "metadata": {}, + "source": [ + "Finally, we plot the results." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "818ccd65-02ab-4636-9cf5-887862ec978d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,2, figsize=(16,7))\n", + "fig.suptitle(f\"{NUM_TX}x{NUM_RX_ANT} UMi | {2**NUM_BITS_PER_SYMBOL}-QAM\")\n", + "\n", + "## SER\n", + "\n", + "ax[0].set_title(\"Symbol error rate\")\n", + "# Perfect CSI\n", + "ax[0].semilogy(EBN0_DBs, SER['Perf. CSI / LMMSE'], 'x-', label='Perf. CSI / LMMSE', c='C0')\n", + "ax[0].semilogy(EBN0_DBs, SER['Perf. CSI / EP'], 'o--', label='Perf. CSI / EP', c='C0')\n", + "ax[0].semilogy(EBN0_DBs, SER['Perf. CSI / K-Best'], 's-.', label='Perf. CSI / K-Best', c='C0')\n", + "ax[0].semilogy(EBN0_DBs, SER['Perf. CSI / MMSE-PIC'], 'd:', label='Perf. CSI / MMSE-PIC', c='C0')\n", + "\n", + "# Imperfect CSI\n", + "ax[0].semilogy(EBN0_DBs, SER['Ch. Est. / LMMSE'], 'x-', label='Ch. Est. / LMMSE', c='C1')\n", + "ax[0].semilogy(EBN0_DBs, SER['Ch. Est. / EP'], 'o--', label='Ch. Est. / EP', c='C1')\n", + "ax[0].semilogy(EBN0_DBs, SER['Ch. Est. / K-Best'], 's-.', label='Ch. Est. / K-Best', c='C1')\n", + "ax[0].semilogy(EBN0_DBs, SER['Ch. Est. / MMSE-PIC'], 'd:', label='Ch. Est. / MMSE-PIC', c='C1')\n", + "\n", + "ax[0].set_xlabel(r\"$E_b/N0$\")\n", + "ax[0].set_ylabel(\"SER\")\n", + "ax[0].set_ylim((1e-4, 1.0))\n", + "ax[0].legend()\n", + "ax[0].grid(True)\n", + "\n", + "## SER\n", + "\n", + "ax[1].set_title(\"Bit error rate\")\n", + "# Perfect CSI\n", + "ax[1].semilogy(EBN0_DBs, BER['Perf. CSI / LMMSE'], 'x-', label='Perf. CSI / LMMSE', c='C0')\n", + "ax[1].semilogy(EBN0_DBs, BER['Perf. CSI / EP'], 'o--', label='Perf. CSI / EP', c='C0')\n", + "ax[1].semilogy(EBN0_DBs, BER['Perf. CSI / K-Best'], 's-.', label='Perf. CSI / K-Best', c='C0')\n", + "ax[1].semilogy(EBN0_DBs, BER['Perf. CSI / MMSE-PIC'], 'd:', label='Perf. CSI / MMSE-PIC', c='C0')\n", + "\n", + "# Imperfect CSI\n", + "ax[1].semilogy(EBN0_DBs, BER['Ch. Est. / LMMSE'], 'x-', label='Ch. Est. / LMMSE', c='C1')\n", + "ax[1].semilogy(EBN0_DBs, BER['Ch. Est. / EP'], 'o--', label='Ch. Est. / EP', c='C1')\n", + "ax[1].semilogy(EBN0_DBs, BER['Ch. Est. / K-Best'], 's-.', label='Ch. Est. / K-Best', c='C1')\n", + "ax[1].semilogy(EBN0_DBs, BER['Ch. Est. / MMSE-PIC'], 'd:', label='Ch. Est. / MMSE-PIC', c='C1')\n", + "\n", + "ax[1].set_xlabel(r\"$E_b/N0$\")\n", + "ax[1].set_ylabel(\"BER\")\n", + "ax[1].set_ylim((1e-4, 1.0))\n", + "ax[1].legend()\n", + "ax[1].grid(True)" + ] + }, + { + "cell_type": "markdown", + "id": "aedbd5e6-c779-48f1-96b0-8ca604d726a2", + "metadata": {}, + "source": [ + "For this setup, the non-linear detection algorithms K-Best, EP, and MMSE-PIC, outperform the linear MMSE detection method.\n", + "It is remarkable that K-Best and EP with imperfect CSI achieve lower BER than LMMSE detection with perfect CSI.\n", + "\n", + "However, one should keep in mind that:\n", + "\n", + "- EP is prone to numerical imprecision and could therefore achieve better BER/SER with double precision (`dtype=tf.complex128`). The number of iterations `l` as well as the update smoothing parameter `beta` impact performance.\n", + "\n", + "- For K-Best, there is not a unique way to compute soft information and better performance could be achieved with improved methods for computing soft information from a list of candidates (see [list2llr](https://nvlabs.github.io/sionna/api/mimo.html#list2llr)). Increasing the list size `k` results in improved accuracy at the cost of higher complexity.\n", + "\n", + "- MMSE-PIC can be easily combined with a decoder to implement iterative detection and decoding, as it takes as input soft prior information on the bits/symbols." + ] + } + ], + "metadata": { + "interpreter": { + "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/requirements.txt b/requirements.txt index c2ebd650..a0447dcf 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ -tensorflow >=2.6.4, !=2.7.0, !=2.7.1, !=2.8.0, <2.11 ; sys_platform != "darwin" -tensorflow-macos >=2.6, <2.10 ; sys_platform == "darwin" +tensorflow >=2.7.4, !=2.8.0, !=2.8.1, !=2.8.2, !=2.8.3, !=2.9.0, !=2.9.1 ; sys_platform != "darwin" +tensorflow-macos >=2.7 ; sys_platform == "darwin" numpy scipy matplotlib diff --git a/setup.cfg b/setup.cfg index 07ccdba1..0a00ece3 100644 --- a/setup.cfg +++ b/setup.cfg @@ -25,8 +25,8 @@ include_package_data = True python_requires = >=3.6 install_requires = - tensorflow >=2.6.4, !=2.7.0, !=2.7.1, !=2.8.0, <2.11 ; sys_platform != "darwin" - tensorflow-macos >=2.6, <2.10 ; sys_platform == "darwin" + tensorflow >=2.7.4, !=2.8.0, !=2.8.1, !=2.8.2, !=2.8.3, !=2.9.0, !=2.9.1 ; sys_platform != "darwin" + tensorflow-macos >=2.7 ; sys_platform == "darwin" numpy matplotlib scipy diff --git a/sionna/__init__.py b/sionna/__init__.py index b412d245..5e9a2969 100644 --- a/sionna/__init__.py +++ b/sionna/__init__.py @@ -5,7 +5,7 @@ """This is the Sionna library. """ -__version__ = '0.11.0' +__version__ = '0.12.0' from . import utils from .constants import * diff --git a/sionna/channel/tr38901/models/TDL-A.json b/sionna/channel/tr38901/models/TDL-A.json index 38dbe19f..c8a482dd 100644 --- a/sionna/channel/tr38901/models/TDL-A.json +++ b/sionna/channel/tr38901/models/TDL-A.json @@ -1,6 +1,7 @@ { "los" : 0, "num_clusters" : 23, + "scale_delays" : true, "delays" : [ 0.0, 0.3819, diff --git a/sionna/channel/tr38901/models/TDL-A30.json b/sionna/channel/tr38901/models/TDL-A30.json new file mode 100644 index 00000000..a4abe331 --- /dev/null +++ b/sionna/channel/tr38901/models/TDL-A30.json @@ -0,0 +1,31 @@ +{ + "los" : 0, + "num_clusters" : 12, + "scale_delays" : false, + + "delays" : [ 0.0, + 10.0, + 15.0, + 20.0, + 25.0, + 50.0, + 65.0, + 75.0, + 105.0, + 135.0, + 150.0, + 290.0], + + "powers" : [ -15.5, + 0.0, + -5.1, + -5.1, + -9.6, + -8.2, + -13.1, + -11.5, + -11.0, + -16.2, + -16.6, + -26.2] +} diff --git a/sionna/channel/tr38901/models/TDL-B.json b/sionna/channel/tr38901/models/TDL-B.json index 8b6ea3c8..f85f9354 100644 --- a/sionna/channel/tr38901/models/TDL-B.json +++ b/sionna/channel/tr38901/models/TDL-B.json @@ -1,6 +1,7 @@ { "los" : 0, "num_clusters" : 23, + "scale_delays" : true, "powers" : [ 0.0, -2.2, diff --git a/sionna/channel/tr38901/models/TDL-B100.json b/sionna/channel/tr38901/models/TDL-B100.json new file mode 100644 index 00000000..4df1aaa3 --- /dev/null +++ b/sionna/channel/tr38901/models/TDL-B100.json @@ -0,0 +1,31 @@ +{ + "los" : 0, + "num_clusters" : 12, + "scale_delays" : false, + + "powers" : [ 0.0, + -2.2, + -0.6, + -0.6, + -0.3, + -1.2, + -5.9, + -2.2, + -0.8, + -6.3, + -7.5, + -7.1], + + "delays" : [ 0.0, + 10.0, + 20.0, + 30.0, + 35.0, + 45.0, + 55.0, + 120.0, + 170.0, + 245.0, + 330.0, + 480.0] +} diff --git a/sionna/channel/tr38901/models/TDL-C.json b/sionna/channel/tr38901/models/TDL-C.json index 1cc55aef..47fa1f18 100644 --- a/sionna/channel/tr38901/models/TDL-C.json +++ b/sionna/channel/tr38901/models/TDL-C.json @@ -1,6 +1,7 @@ { "los" : 0, "num_clusters" : 24, + "scale_delays" : true, "powers" : [ -4.4, -1.2, diff --git a/sionna/channel/tr38901/models/TDL-C300.json b/sionna/channel/tr38901/models/TDL-C300.json new file mode 100644 index 00000000..34833d7a --- /dev/null +++ b/sionna/channel/tr38901/models/TDL-C300.json @@ -0,0 +1,31 @@ +{ + "los" : 0, + "num_clusters" : 12, + "scale_delays" : false, + + "powers" : [ -6.9, + 0.0, + -7.7, + -2.5, + -2.4, + -9.9, + -8.0, + -6.6, + -7.1, + -13.0, + -14.2, + -16.0], + + "delays" : [ 0.0, + 65.0, + 70.0, + 190.0, + 195.0, + 200.0, + 240.0, + 325.0, + 520.0, + 1045.0, + 1510.0, + 2595.0] +} diff --git a/sionna/channel/tr38901/models/TDL-D.json b/sionna/channel/tr38901/models/TDL-D.json index b7c1cad6..ba225fb5 100644 --- a/sionna/channel/tr38901/models/TDL-D.json +++ b/sionna/channel/tr38901/models/TDL-D.json @@ -1,6 +1,7 @@ { "los" : 1, "num_clusters" : 13, + "scale_delays" : true, "powers" : [ -0.2, -13.5, diff --git a/sionna/channel/tr38901/models/TDL-E.json b/sionna/channel/tr38901/models/TDL-E.json index e5819b34..35733cc6 100644 --- a/sionna/channel/tr38901/models/TDL-E.json +++ b/sionna/channel/tr38901/models/TDL-E.json @@ -1,6 +1,7 @@ { "los" : 1, "num_clusters" : 14, + "scale_delays" : true, "powers" : [ -0.03, -22.03, diff --git a/sionna/channel/tr38901/tdl.py b/sionna/channel/tr38901/tdl.py index a6a9498b..295e4621 100644 --- a/sionna/channel/tr38901/tdl.py +++ b/sionna/channel/tr38901/tdl.py @@ -11,14 +11,14 @@ import tensorflow as tf from sionna import PI, SPEED_OF_LIGHT -from sionna.utils import insert_dims +from sionna.utils import insert_dims, expand_to_rank, matrix_sqrt, split_dim, flatten_last_dims from sionna.channel import ChannelModel from . import models # pylint: disable=relative-beyond-top-level class TDL(ChannelModel): # pylint: disable=line-too-long - r"""TDL(model, delay_spread, carrier_frequency, num_sinusoids=20, los_angle_of_arrival=PI/4., min_speed=0., max_speed=None, dtype=tf.complex64) + r"""TDL(model, delay_spread, carrier_frequency, num_sinusoids=20, los_angle_of_arrival=PI/4., min_speed=0., max_speed=None, num_rx_ant=1, num_tx_ant=1, spatial_corr_mat=None, rx_corr_mat=None, tx_corr_mat=None, dtype=tf.complex64) Tapped delay line (TDL) channel model from the 3GPP [TR38901]_ specification. @@ -32,9 +32,41 @@ class TDL(ChannelModel): and uniformly sampled from the specified interval for each link and each batch example. - The TDL model only works for single-input single-output (SISO) systems. - One can conduct simulations for multiple-input multiple-output (MIMO) - systems using the other channel models available in Sionna. + The TDL model only works for systems with a single transmitter and a single + receiver. The transmitter and receiver can be equipped with multiple + antennas. Spatial correlation is simulated through filtering by specified + correlation matrices. + + The ``spatial_corr_mat`` parameter can be used to specify an arbitrary + spatial correlation matrix. In particular, it can be used to model + correlated cross-polarized transmit and receive antennas as follows + (see, e.g., Annex G.2.3.2.1 [TS38141-1]_): + + .. math:: + + \mathbf{R} = \mathbf{R}_{\text{rx}} \otimes \mathbf{\Gamma} \otimes \mathbf{R}_{\text{tx}} + + where :math:`\mathbf{R}` is the spatial correlation matrix ``spatial_corr_mat``, + :math:`\mathbf{R}_{\text{rx}}` the spatial correlation matrix at the receiver + with same polarization, :math:`\mathbf{R}_{\text{tx}}` the spatial correlation + matrix at the transmitter with same polarization, and :math:`\mathbf{\Gamma}` + the polarization correlation matrix. :math:`\mathbf{\Gamma}` is 1x1 for single-polarized + antennas, 2x2 when only the transmit or receive antennas are cross-polarized, and 4x4 when + transmit and receive antennas are cross-polarized. + + It is also possible not to specify ``spatial_corr_mat``, but instead the correlation matrices + at the receiver and transmitter, using the ``rx_corr_mat`` and ``tx_corr_mat`` + parameters, respectively. + This can be useful when single polarized antennas are simulated, and it is also + more computationally efficient. + This is equivalent to setting ``spatial_corr_mat`` to : + + .. math:: + \mathbf{R} = \mathbf{R}_{\text{rx}} \otimes \mathbf{R}_{\text{tx}} + + where :math:`\mathbf{R}_{\text{rx}}` is the correlation matrix at the receiver + ``rx_corr_mat`` and :math:`\mathbf{R}_{\text{tx}}` the correlation matrix at + the transmitter ``tx_corr_mat``. Example -------- @@ -111,10 +143,12 @@ class TDL(ChannelModel): ----------- model : str - TDL model to use. Must be one of "A", "B", "C", "D" or "E". + TDL model to use. Must be one of "A", "B", "C", "D", "E", "A30", "B100", or "C300". delay_spread : float - RMS delay spread [s] + RMS delay spread [s]. + For the "A30", "B100", and "C300" models, the delay spread must be set + to 10ns, 100ns, and 300ns, respectively. carrier_frequency : float Carrier frequency [Hz] @@ -134,6 +168,34 @@ class TDL(ChannelModel): then ``max_speed`` takes the same value as ``min_speed``. Defaults to `None`. + num_rx_ant : int + Number of receive antennas. + Defaults to 1. + + num_tx_ant : int + Number of transmit antennas. + Defaults to 1. + + spatial_corr_mat : [num_rx_ant*num_tx_ant,num_rx_ant*num_tx_ant], tf.complex or `None` + Spatial correlation matrix. + If not set to `None`, then ``rx_corr_mat`` and ``tx_corr_mat`` are ignored and + this matrix is used for spatial correlation. + If set to `None` and ``rx_corr_mat`` and ``tx_corr_mat`` are also set to `None`, + then no correlation is applied. + Defaults to `None`. + + rx_corr_mat : [num_rx_ant,num_rx_ant], tf.complex or `None` + Spatial correlation matrix for the receiver. + If set to `None` and ``spatial_corr_mat`` is also set to `None`, then no receive + correlation is applied. + Defaults to `None`. + + tx_corr_mat : [num_tx_ant,num_tx_ant], tf.complex or `None` + Spatial correlation matrix for the transmitter. + If set to `None` and ``spatial_corr_mat`` is also set to `None`, then no transmit + correlation is applied. + Defaults to `None`. + dtype : Complex tf.DType Defines the datatype for internal calculations and the output dtype. Defaults to `tf.complex64`. @@ -168,6 +230,11 @@ def __init__( self, los_angle_of_arrival=PI/4., min_speed=0., max_speed=None, + num_rx_ant=1, + num_tx_ant=1, + spatial_corr_mat=None, + rx_corr_mat=None, + tx_corr_mat=None, dtype=tf.complex64): assert dtype.is_complex, "dtype must be a complex datatype" @@ -176,7 +243,8 @@ def __init__( self, self._real_dtype = real_dtype # Set the file from which to load the model - assert model in ('A', 'B', 'C', 'D', 'E'), "Invalid TDL model" + assert model in ('A', 'B', 'C', 'D', 'E', 'A30', 'B100', 'C300'),\ + "Invalid TDL model" if model == 'A': parameters_fname = "TDL-A.json" elif model == 'B': @@ -187,10 +255,27 @@ def __init__( self, parameters_fname = "TDL-D.json" elif model == 'E': parameters_fname = "TDL-E.json" + elif model == 'A30': + parameters_fname = "TDL-A30.json" + if delay_spread != 30e-9: + print("Warning: Delay spread is set to 30ns with this model") + delay_spread = 30e-9 + elif model == 'B100': + parameters_fname = "TDL-B100.json" + if delay_spread != 100e-9: + print("Warning: Delay spread is set to 100ns with this model") + delay_spread = 100e-9 + elif model == 'C300': + parameters_fname = "TDL-C300.json" + if delay_spread != 300e-9: + print("Warning: Delay spread is set to 300ns with this model") + delay_spread = 300e-9 # Load model parameters self._load_parameters(parameters_fname) + self._num_rx_ant = num_rx_ant + self._num_tx_ant = num_tx_ant self._carrier_frequency = tf.constant(carrier_frequency, real_dtype) self._num_sinusoids = tf.constant(num_sinusoids, tf.int32) self._los_angle_of_arrival = tf.constant( los_angle_of_arrival, @@ -221,6 +306,29 @@ def __init__( self, 1, # num time steps num_sinusoids]) + # Precompute square root of spatial covariance matrices + if spatial_corr_mat is not None: + spatial_corr_mat = tf.cast(spatial_corr_mat, self._dtype) + spatial_corr_mat_sqrt = matrix_sqrt(spatial_corr_mat) + spatial_corr_mat_sqrt = expand_to_rank(spatial_corr_mat_sqrt, 7, 0) + self._spatial_corr_mat_sqrt = spatial_corr_mat_sqrt + else: + self._spatial_corr_mat_sqrt = None + if rx_corr_mat is not None: + rx_corr_mat = tf.cast(rx_corr_mat, self._dtype) + rx_corr_mat_sqrt = matrix_sqrt(rx_corr_mat) + rx_corr_mat_sqrt = expand_to_rank(rx_corr_mat_sqrt, 7, 0) + self._rx_corr_mat_sqrt = rx_corr_mat_sqrt + else: + self._rx_corr_mat_sqrt = None + if tx_corr_mat is not None: + tx_corr_mat = tf.cast(tx_corr_mat, self._dtype) + tx_corr_mat_sqrt = matrix_sqrt(tx_corr_mat) + tx_corr_mat_sqrt = expand_to_rank(tx_corr_mat_sqrt, 7, 0) + self._tx_corr_mat_sqrt = tx_corr_mat_sqrt + else: + self._tx_corr_mat_sqrt = None + @property def num_clusters(self): r"""Number of paths (:math:`M`)""" @@ -240,7 +348,10 @@ def k_factor(self): @property def delays(self): r"""Path delays [s]""" - return self._delays*self._delay_spread + if self._scale_delays: + return self._delays*self._delay_spread + else: + return self._delays*1e-9 # ns to s @property def mean_powers(self): @@ -266,7 +377,10 @@ def delay_spread(self): @delay_spread.setter def delay_spread(self, value): - self._delay_spread = value + if self._scale_delays: + self._delay_spread = value + else: + print("Warning: The delay spread cannot be set with this model") def __call__(self, batch_size, num_time_steps, sampling_frequency): @@ -310,9 +424,9 @@ def __call__(self, batch_size, num_time_steps, sampling_frequency): # Eq. (6a)-(6c) in the paper [TDL] (see class docstring) phi = tf.random.uniform([ batch_size, 1, # 1 RX - 1, # 1 RX antenna + self._num_rx_ant, # 1 RX antenna 1, # 1 TX - 1, # 1 TX antenna + self._num_tx_ant, # 1 TX antenna self._num_clusters, 1, # Phase shift is shared by all time steps self._num_sinusoids], @@ -346,8 +460,8 @@ def __call__(self, batch_size, num_time_steps, sampling_frequency): 1, # 1 TX antenna 1, # only the first tap is concerned 1], # Shared by all time steps - PI, -PI, + PI, self._real_dtype) # Remove the sinusoids dim doppler = tf.squeeze(doppler, axis=-1) @@ -362,10 +476,34 @@ def __call__(self, batch_size, num_time_steps, sampling_frequency): axis=5) # Path dims # Delays - delays = self._delays*self._delay_spread + if self._scale_delays: + delays = self._delays*self._delay_spread + else: + delays = self._delays*1e-9 # ns to s delays = insert_dims(delays, 3, 0) delays = tf.tile(delays, [batch_size, 1, 1, 1]) + # Apply spatial correlation if required + if self._spatial_corr_mat_sqrt is not None: + h = tf.transpose(h, [0,1,3,5,6,2,4]) # [..., num_rx_ant, num_tx_ant] + #h = flatten_dims(h, 2, tf.rank(h)-2) # [..., num_rx_ant*num_tx_ant] + h = flatten_last_dims(h, 2) # [..., num_rx_ant*num_tx_ant] + h = tf.expand_dims(h, axis=-1) # [..., num_rx_ant*num_tx_ant, 1] + h = tf.matmul(self._spatial_corr_mat_sqrt, h) + h = tf.squeeze(h, axis=-1) + h = split_dim(h, [self._num_rx_ant, self._num_tx_ant], + tf.rank(h)-1) # [..., num_rx_ant, num_tx_ant] + h = tf.transpose(h, [0,1,5,2,6,3,4]) + else: + if ( (self._rx_corr_mat_sqrt is not None) + or (self._tx_corr_mat_sqrt is not None) ): + h = tf.transpose(h, [0,1,3,5,6,2,4]) + if self._rx_corr_mat_sqrt is not None: + h = tf.matmul(self._rx_corr_mat_sqrt, h) + if self._tx_corr_mat_sqrt is not None: + h = tf.matmul(h, self._tx_corr_mat_sqrt) + h = tf.transpose(h, [0,1,5,2,6,3,4]) + # Stop gadients to avoid useless backpropagation h = tf.stop_gradient(h) delays = tf.stop_gradient(delays) @@ -433,6 +571,9 @@ def _load_parameters(self, fname): # LoS scenario ? self._los = bool(params['los']) + # Scale the delays + self._scale_delays = bool(params['scale_delays']) + # Loading cluster delays and mean powers self._num_clusters = tf.constant(params['num_clusters'], tf.int32) @@ -451,7 +592,7 @@ def _load_parameters(self, fname): # We need to keep only one. delays = delays[1:] - # Normalize the PDP if requested + # Normalize the PDP if self._los: norm_factor = tf.reduce_sum(mean_powers) + self._los_power self._los_power = self._los_power / norm_factor diff --git a/sionna/channel/utils.py b/sionna/channel/utils.py index 418e0846..f57f9ea2 100644 --- a/sionna/channel/utils.py +++ b/sionna/channel/utils.py @@ -1015,7 +1015,8 @@ def gen_single_sector_topology( batch_size, # of the sector sector_center = (min_bs_ut_dist + 0.5*isd)*0.5 bs_downtilt = 0.5*PI - tf.math.atan(sector_center/bs_height) - bs_orientation = tf.stack([ tf.zeros([batch_size, 1], real_dtype), + bs_yaw = tf.constant(0.25*PI, real_dtype) + bs_orientation = tf.stack([ tf.fill([batch_size, 1], bs_yaw), tf.fill([batch_size, 1], bs_downtilt), tf.zeros([batch_size, 1], real_dtype)], axis=-1) @@ -1218,7 +1219,8 @@ def gen_single_sector_topology_interferers( batch_size, # of the sector sector_center = (min_bs_ut_dist + 0.5*isd)*0.5 bs_downtilt = 0.5*PI - tf.math.atan(sector_center/bs_height) - bs_orientation = tf.stack([ tf.zeros([batch_size, 1], real_dtype), + bs_yaw = tf.constant(0.25*PI, real_dtype) + bs_orientation = tf.stack([ tf.fill([batch_size, 1], bs_yaw), tf.fill([batch_size, 1], bs_downtilt), tf.zeros([batch_size, 1], real_dtype)], axis=-1) diff --git a/sionna/fec/__init__.py b/sionna/fec/__init__.py index 4916380a..9574e2f3 100644 --- a/sionna/fec/__init__.py +++ b/sionna/fec/__init__.py @@ -4,6 +4,7 @@ # """FEC sub-package of the Sionna library""" + from . import ldpc from . import polar from . import conv @@ -12,3 +13,7 @@ from . import scrambling from . import interleaving from . import utils +from . import linear + + + diff --git a/sionna/fec/conv/decoding.py b/sionna/fec/conv/decoding.py index 43942316..0fd3ea02 100644 --- a/sionna/fec/conv/decoding.py +++ b/sionna/fec/conv/decoding.py @@ -10,14 +10,15 @@ from sionna.fec.utils import int2bin from sionna.fec.conv.utils import polynomial_selector, Trellis + class ViterbiDecoder(Layer): # pylint: disable=line-too-long - r"""ViterbiDecoder(gen_poly=None, rate=1/2, constraint_length=3, method='soft_llr', output_dtype=tf.float32, **kwargs) + r"""ViterbiDecoder(encoder=None, gen_poly=None, rate=1/2, constraint_length=3, rsc=False, terminate=False, method='soft_llr', output_dtype=tf.float32, **kwargs) Implements the Viterbi decoding algorithm [Viterbi]_ that returns an estimate of the information bits for a noisy convolutional codeword. Takes as input either LLR values (`method` = `soft_llr`) or hard bit values - (`method` = `hard`) and returns the hard decided estimate of information + (`method` = `hard`) and returns a hard decided estimation of the information bits. The class inherits from the Keras layer class and can be used as layer in @@ -25,6 +26,13 @@ class ViterbiDecoder(Layer): Parameters ---------- + encoder: :class:`~sionna.fec.conv.encoding.ConvEncoder` + If ``encoder`` is provided as input, the following input parameters + are not required and will be ignored: ``gen_poly``, ``rate``, + ``constraint_length``, ``rsc``, ``terminate``. They will be inferred + from the ``encoder`` object itself. If ``encoder`` is `None`, the + above parameters must be provided explicitly. + gen_poly: tuple tuple of strings with each string being a 0, 1 sequence. If `None`, ``rate`` and ``constraint_length`` must be provided. @@ -36,9 +44,23 @@ class ViterbiDecoder(Layer): Valid values are between 3 and 8 inclusive. Only required if ``gen_poly`` is `None`. + rsc: boolean + Boolean flag indicating whether the encoder is recursive-systematic for + given generator polynomials. + `True` indicates encoder is recursive-systematic. + `False` indicates encoder is feed-forward non-systematic. + + terminate: boolean + Boolean flag indicating whether the codeword is terminated. + `True` indicates codeword is terminated to all-zero state. + `False` indicates codeword is not terminated. + method: str - Choices are `soft_llr' or `hard` or `soft`. In computing path - metrics, `soft_llr` expects channel LLRs whereas `hard` assumes a `binary symmetric channel` (BSC). In case of `hard`, `inputs` will be quantized to 0/1 values. + Valid values are `soft_llr` or `hard`. In computing path + metrics, + `soft_llr` expects channel LLRs as input + `hard` assumes a `binary symmetric channel` (BSC) with 0/1 values are + inputs. In case of `hard`, `inputs` will be quantized to 0/1 values. output_dtype: tf.DType Defaults to tf.float32. Defines the output datatype of the layer. @@ -47,12 +69,12 @@ class ViterbiDecoder(Layer): ----- inputs: [...,n], tf.float32 2+D tensor containing the (noisy) channel output symbols where `n` - denotes the codeword length. + denotes the codeword length Output ------ : [...,rate*n], tf.float32 - 2+D tensor containing the estimates of the information bit tensor. + 2+D tensor containing the estimates of the information bit tensor Note ---- @@ -66,44 +88,54 @@ class ViterbiDecoder(Layer): """ def __init__(self, + encoder=None, gen_poly=None, rate=1/2, constraint_length=3, + rsc=False, + terminate=False, method='soft_llr', return_info_bits=True, output_dtype=tf.float32, **kwargs): super().__init__(**kwargs) - valid_rates = (1/2, 1/3) - valid_constraint_length = (3, 4, 5, 6, 7, 8) + if encoder is not None: + self._gen_poly = encoder.gen_poly + self._trellis = encoder.trellis + self._terminate = encoder.terminate + else: + valid_rates = (1/2, 1/3) + valid_constraint_length = (3, 4, 5, 6, 7, 8) - if gen_poly is not None: - assert all(isinstance(poly, str) for poly in gen_poly), \ + if gen_poly is not None: + assert all(isinstance(poly, str) for poly in gen_poly), \ "Each polynomial must be a string." - assert all(len(poly)==len(gen_poly[0]) for poly in gen_poly), \ + assert all(len(poly)==len(gen_poly[0]) for poly in gen_poly), \ "Each polynomial must be of same length." - assert all(all( + assert all(all( char in ['0','1'] for char in poly) for poly in gen_poly),\ "Each polynomial must be a string of 0's and 1's." - self._gen_poly = gen_poly - else: - valid_rates = (1/2, 1/3) - valid_constraint_length = (3, 4, 5, 6, 7, 8) + self._gen_poly = gen_poly + else: + valid_rates = (1/2, 1/3) + valid_constraint_length = (3, 4, 5, 6, 7, 8) + + assert constraint_length in valid_constraint_length, \ + "Constraint length must be between 3 and 8." + assert rate in valid_rates, \ + "Rate must be 1/3 or 1/2." + self._gen_poly = polynomial_selector(rate, constraint_length) - assert constraint_length in valid_constraint_length, \ - "Constraint length must be between 3 and 8." - assert rate in valid_rates, \ - "Rate must be 1/3 or 1/2." - self._gen_poly = polynomial_selector(rate, constraint_length) + # init Trellis parameters + self._trellis = Trellis(self.gen_poly, rsc=rsc) + self._terminate = terminate + self._coderate_desired = 1/len(self.gen_poly) + self._mu = len(self._gen_poly[0])-1 assert method in ('soft_llr', 'hard'), \ "method must be `soft_llr` or `hard`." - # init Trellis parameters - self._trellis = Trellis(self.gen_poly, rsc=False) - self._coderate = 1/len(self.gen_poly) - # conv_k denotes number of input bit streams # can only be 1 in current implementation self._conv_k = self._trellis.conv_k @@ -133,19 +165,49 @@ def __init__(self, @property def gen_poly(self): - """The generator polynomial used by the encoder.""" + """Generator polynomial used by the encoder""" return self._gen_poly @property def coderate(self): - """Rate of the code used in the encoder.""" + """Rate of the code used in the encoder""" + if self.terminate and self._n is None: + print("Note that, due to termination, the true coderate is lower "\ + "than the returned design rate. "\ + "The exact true rate is dependent on the value of n and "\ + "hence cannot be computed before the first call().") + self._coderate = self._coderate_desired + elif self.terminate and self._n is not None: + k = (self._coderate_desired*self._n - self._mu) + self._coderate = k/self._n return self._coderate @property def trellis(self): - """Trellis object used during encoding.""" + """Trellis object used during encoding""" return self._trellis + @property + def terminate(self): + """Indicates if the encoder is terminated during codeword generation""" + return self._terminate + + @property + def k(self): + """Number of information bits per codeword""" + if self._k is None: + print("Note: The value of k cannot be computed before the first " \ + "call().") + return self._k + + @property + def n(self): + """Number of codeword bits""" + if self._n is None: + print("Note: The value of n cannot be computed before the first " \ + "call().") + return self._n + ######################### # Utility functions ######################### @@ -167,34 +229,42 @@ def _mask_by_tonode(self): return st_op_idx - def _update(self, cum_tminus1, metrics_t): - r""" - Update optimal cumulative path metrics at time t given optimal - cumulative metrics at time t-1. - - Also returns tb_states, the traceback states at t-1 that result - in optimal cumulative metric at time t, for each state. - """ + def _update_fwd(self, init_cm, bm_mat): state_vec = tf.tile(tf.range(self._ns, dtype=tf.int32)[None,:], - [tf.shape(cum_tminus1)[0], 1]) - # Ns x No matrix. Element (s,j) is path_metric at state s,tminus1 - # with transition output j - sum_metric = tf.math.add( - tf.expand_dims(metrics_t, axis=1), - tf.cast(tf.expand_dims(cum_tminus1, axis=-1),tf.float32)) - - sum_metric_bytonode = tf.gather_nd(sum_metric, - tf.tile(self.ipst_op_idx[None,:], - [tf.shape(cum_tminus1)[0],1,1,1]), - batch_dims=1) - tb_state_idx = tf.cast(tf.math.argmin(sum_metric_bytonode,axis=2), - tf.int32) - # Transition to States argmin state index - from_st_idx = tf.transpose(tf.stack([state_vec, tb_state_idx]), - perm=[1, 2,0]) - tb_states = tf.gather_nd(self._trellis.from_nodes, from_st_idx) - cum_t = tf.math.reduce_min(sum_metric_bytonode,axis=2) - return cum_t, tb_states + [tf.shape(init_cm)[0], 1]) + ipst_op_mask = tf.tile(self.ipst_op_idx[None,:], [tf.shape(init_cm)[0], 1, 1, 1]) + + cm_ta = tf.TensorArray(tf.float32, size=self._num_syms, + dynamic_size=False, clear_after_read=False) + tb_ta = tf.TensorArray(tf.int32, size=self._num_syms, + dynamic_size=False, clear_after_read=False) + + prev_cm = init_cm + for idx in tf.range(0, self._n, self._conv_n): + sym = idx//self._conv_n + metrics_t = bm_mat[..., sym] + # Ns x No matrix- (s,j) is path_metric at state s with transition op=j + sum_metric = prev_cm[:,:,None] + metrics_t[:,None,:] + sum_metric_bytonode = tf.gather_nd(sum_metric, ipst_op_mask, + batch_dims=1) + + tb_state_idx = tf.math.argmin(sum_metric_bytonode, axis=2) + tb_state_idx = tf.cast(tb_state_idx, tf.int32) + + # Transition to states argmin state index + from_st_idx = tf.transpose(tf.stack([state_vec, tb_state_idx]), + perm=[1, 2, 0]) + + tb_states = tf.gather_nd(self._trellis.from_nodes, from_st_idx) + cum_t = tf.math.reduce_min(sum_metric_bytonode,axis=2) + + cm_ta = cm_ta.write(sym, cum_t) + tb_ta = tb_ta.write(sym, tb_states) + + prev_cm = cum_t + + return cm_ta, tb_ta + def _op_bits_path(self, paths): r""" @@ -250,14 +320,16 @@ def _optimal_path(self, cm_, tb_): cm_: cumulative metrics for each state at time t(0 to T) tb_: traceback state for each state at time t(0 to T) """ - # tb and ca are of shape batch x self._ns x num_syms + # tb and ca are of shape (batch x self._ns x num_syms) assert(tb_.get_shape()[1] == self._ns), "Invalid shape." optst_ta = tf.TensorArray(tf.int32, size=tb_.shape[-1], dynamic_size=False, clear_after_read=False) - - opt_term_state =tf.cast(tf.argmin(cm_[:, :, -1], axis=1), tf.int32) - optst_ta= optst_ta.write(tb_.shape[-1]-1,opt_term_state) + if self._terminate: + opt_term_state = tf.zeros((tf.shape(cm_)[0],), tf.int32) + else: + opt_term_state =tf.cast(tf.argmin(cm_[:, :, -1], axis=1), tf.int32) + optst_ta = optst_ta.write(tb_.shape[-1]-1,opt_term_state) for sym in tf.range(tb_.shape[-1]-1, 0, -1): opt_st = optst_ta.read(sym)[:,None] @@ -313,13 +385,15 @@ def build(self, input_shape): tf.debugging.assert_greater_equal(len(input_shape), 2) self._n = input_shape[-1] - self._k = int(self._n*self.coderate) divisible = tf.math.floormod(self._n, self._conv_n) assert divisible==0, 'length of codeword should be divisible by \ number of output bits per symbol.' - self._num_syms = int(self._n/self._conv_n) + self._num_syms = int(self._n*self._coderate_desired) + + self._num_term_syms = self._mu if self.terminate else 0 + self._k = self._num_syms - self._num_term_syms def call(self, inputs): """ @@ -342,7 +416,6 @@ def call(self, inputs): output_shape = inputs.get_shape().as_list() y_resh = tf.reshape(inputs, [-1, self._n]) - output_shape[0] = -1 if self._return_info_bits: output_shape[-1] = self._k # assign k to the last dimension @@ -351,41 +424,24 @@ def call(self, inputs): # Branch metrics matrix for a given y bm_mat = self._bmcalc(y_resh) - cm_ta = tf.TensorArray(tf.float32, - size=self._num_syms, - dynamic_size=False, - clear_after_read=False) - tb_ta = tf.TensorArray(tf.int32, - size=self._num_syms, - dynamic_size=False, - clear_after_read=False) - - prev_cm_np = np.full((self._ns,), LARGEDIST) - prev_cm_np[0] = 0.0 - prev_cm_ = tf.convert_to_tensor(prev_cm_np, dtype=tf.float32) - + init_cm_np = np.full((self._ns,), LARGEDIST) + init_cm_np[0] = 0.0 + prev_cm_ = tf.convert_to_tensor(init_cm_np, dtype=tf.float32) prev_cm = tf.tile(prev_cm_[None,:], [tf.shape(y_resh)[0], 1]) - for idx in tf.range(0, self._n, self._conv_n): - sym = idx//self._conv_n + cm_ta, tb_ta = self._update_fwd(prev_cm, bm_mat) - cum_t, tb_states = self._update(prev_cm, bm_mat[..., sym]) - cm_ta = cm_ta.write(sym, cum_t) - tb_ta = tb_ta.write(sym, tb_states) - - prev_cm = cum_t cm = tf.transpose(cm_ta.stack(), perm=[1,2,0]) tb = tf.transpose(tb_ta.stack(),perm=[1,2,0]) del cm_ta, tb_ta + zero_st = tf.zeros((tf.shape(y_resh)[0], 1), tf.int32) opt_path = self._optimal_path(cm, tb) - opt_path = tf.concat( - (tf.zeros((tf.shape(cm)[0], 1), tf.int32), - opt_path), axis=1) + opt_path = tf.concat((zero_st, opt_path), axis=1) del cm, tb msghat, cwhat = self._op_bits_path(opt_path) - if self._return_info_bits: + msghat = msghat[...,:self._k] output = tf.cast(msghat, self.output_dtype) else: output = tf.cast(cwhat, self.output_dtype) @@ -396,25 +452,33 @@ def call(self, inputs): class BCJRDecoder(Layer): # pylint: disable=line-too-long - """BCJRDecoder(gen_poly=None, rate=1/2, constraint_length=3,, rsc=False, terminate=False, hard_out=True, output_dtype=tf.float32, **kwargs) + r"""BCJRDecoder(encoder=None, gen_poly=None, rate=1/2, constraint_length=3, rsc=False, terminate=False, hard_out=True, algorithm='map', output_dtype=tf.float32, **kwargs) Implements the BCJR decoding algorithm [BCJR]_ that returns an estimate of the information bits for a noisy convolutional codeword. - Takes as input either channel LLRs or a tuple of - (channel LLRs, a priori LLRs). Returns an estimate of the information bits, either the as LLRs (if ``hard_out`` =False) or hard decoded - bits (if ``hard_out`` =True), respectively. + Takes as input either channel LLRs or a tuple + (channel LLRs, apriori LLRs). Returns an estimate of the information + bits, either output LLRs ( ``hard_out`` = `False`) or hard decoded + bits ( ``hard_out`` = `True`), respectively. The class inherits from the Keras layer class and can be used as layer in a Keras model. Parameters ---------- + encoder: :class:`~sionna.fec.conv.encoding.ConvEncoder` + If ``encoder`` is provided as input, the following input parameters + are not required and will be ignored: ``gen_poly``, ``rate``, + ``constraint_length``, ``rsc``, ``terminate``. They will be inferred + from the ``encoder`` object itself. If ``encoder`` is `None`, the + above parameters must be provided explicitly. + gen_poly: tuple tuple of strings with each string being a 0, 1 sequence. If `None`, ``rate`` and ``constraint_length`` must be provided. rate: float - Valid values are 1/3 and 0.5. Only required if ``gen_poly`` is `None`. + Valid values are 1/3 and 1/2. Only required if ``gen_poly`` is `None`. constraint_length: int Valid values are between 3 and 8 inclusive. Only required if @@ -422,20 +486,26 @@ class BCJRDecoder(Layer): rsc: boolean Boolean flag indicating whether the encoder is recursive-systematic for - given generator polynomials. - `"True"` indicates encoder is recursive-systematic. - `"False"` indicates encoder is feed-forward non-systematic. + given generator polynomials. `True` indicates encoder is + recursive-systematic. `False` indicates encoder is feed-forward non-systematic. terminate: boolean Boolean flag indicating whether the codeword is terminated. - `"True"` indicates codeword is terminated to all-zero state. - `"False"` indicates codeword is not terminated + `True` indicates codeword is terminated to all-zero state. + `False` indicates codeword is not terminated. hard_out: boolean Boolean flag indicating whether to output hard or soft decisions on - the decoded information vector. `"True"` implies a hard-decoded - information vector of 0/1's as output. `"False"` implies output is - decoded LLR's of the information. + the decoded information vector. + `True` implies a hard-decoded information vector of 0/1's as output. + `False` implies output is decoded LLR's of the information. + + algorithm: str + Defaults to `map`. Indicates the implemented BCJR algorithm, + where `map` denotes the exact MAP algorithm, `log` indicates the + exact MAP implementation, but in log-domain, and + `maxlog` indicates the approximated MAP implementation in log-domain, + where :math:`\log(e^{a}+e^{b}) \sim \max(a,b)`. output_dtype: tf.DType Defaults to tf.float32. Defines the output datatype of the layer. @@ -447,7 +517,7 @@ class BCJRDecoder(Layer): llr_ch: [...,n], tf.float32 2+D tensor containing the (noisy) channel - LLRs where `n` denotes the codeword length. + LLRs, where `n` denotes the codeword length llr_a: [...,k], tf.float32 2+D tensor containing the a priori information of each information bit. @@ -456,57 +526,59 @@ class BCJRDecoder(Layer): Output ------ : tf.float32 - 2+D tensor of shape `[...,rate*n]` containing the estimates of the - information bit tensor. + 2+D tensor of shape `[...,coderate*n]` containing the estimates of the + information bit tensor - Note - ---- - A full implementation of the decoder rather than a windowed approach - is used. For a given codeword of duration `T`, the path metric is - computed from time `0` to `T` and the path with optimal metric at time - `T` is selected. The optimal path is then traced back from `T` to `0` - to output the estimate of the information bit vector used to encode. - For larger codewords, note that the current method is sub-optimal - in terms of memory utilization and latency. """ def __init__(self, + encoder=None, gen_poly=None, rate=1/2, constraint_length=3, rsc=False, terminate=False, hard_out=True, + algorithm='map', output_dtype=tf.float32, **kwargs): super().__init__(**kwargs) - - if gen_poly is not None: - assert all(isinstance(poly, str) for poly in gen_poly), \ - "Each polynomial must be a string." - assert all(len(poly)==len(gen_poly[0]) for poly in gen_poly), \ - "Each polynomial must be of same length." - assert all(all( - char in ['0','1'] for char in poly) for poly in gen_poly),\ - "Each polynomial must be a string of 0's and 1's." - self._gen_poly = gen_poly + if encoder is not None: + self._gen_poly = encoder.gen_poly + self._trellis = encoder.trellis + self._terminate = encoder.terminate else: - valid_rates = (1/2, 1/3) - valid_constraint_length = (3, 4, 5, 6, 7, 8) + if gen_poly is not None: + assert all(isinstance(poly, str) for poly in gen_poly), \ + "Each polynomial must be a string." + assert all(len(poly)==len(gen_poly[0]) for poly in gen_poly), \ + "Each polynomial must be of same length." + assert all(all( + char in ['0','1'] for char in poly) for poly in gen_poly),\ + "Each polynomial must be a string of 0's and 1's." + self._gen_poly = gen_poly + else: + valid_rates = (1/2, 1/3) + valid_constraint_length = (3, 4, 5, 6, 7, 8) + + assert constraint_length in valid_constraint_length, \ + "Constraint length must be between 3 and 8." + assert rate in valid_rates, \ + "Rate must be 1/3 or 1/2." + self._gen_poly = polynomial_selector(rate, constraint_length) - assert constraint_length in valid_constraint_length, \ - "Constraint length must be between 3 and 8." - assert rate in valid_rates, \ - "Rate must be 1/3 or 1/2." - self._gen_poly = polynomial_selector(rate, constraint_length) + # init Trellis parameters + self._trellis = Trellis(self.gen_poly, rsc=rsc) + self._terminate = terminate - # init Trellis parameters - self._trellis = Trellis(self.gen_poly, rsc=rsc) - self._coderate = 1/len(self._gen_poly) + valid_algorithms = ['map', 'log', 'maxlog'] + assert algorithm in valid_algorithms, \ + "algorithm must be one of map, log or maxlog" + + self._coderate_desired = 1/len(self._gen_poly) self._mu = len(self._gen_poly[0])-1 - self._terminate = terminate self._num_term_bits = None self._num_term_syms = None @@ -531,6 +603,8 @@ def __init__(self, self._ns = self._trellis.ns self._hard_out = hard_out + self._algorithm = algorithm + self._output_dtype = output_dtype self.ipst_op_idx, self.ipst_ip_idx = self._mask_by_tonode() @@ -540,19 +614,49 @@ def __init__(self, @property def gen_poly(self): - """The generator polynomial used by the encoder.""" + """Generator polynomial used by the encoder""" return self._gen_poly @property def coderate(self): - """Rate of the code used in the encoder.""" + """Rate of the code used in the encoder""" + if self.terminate and self._n is None: + print("Note that, due to termination, the true coderate is lower "\ + "than the returned design rate. "\ + "The exact true rate is dependent on the value of n and "\ + "hence cannot be computed before the first call().") + self._coderate = self._coderate_desired + elif self.terminate and self._n is not None: + k = (self._coderate_desired*self._n - self._mu) + self._coderate = k/self._n return self._coderate @property def trellis(self): - """Trellis object used during encoding.""" + """Trellis object used during encoding""" return self._trellis + @property + def terminate(self): + """Indicates if the encoder is terminated during codeword generation""" + return self._terminate + + @property + def k(self): + """Number of information bits per codeword""" + if self._k is None: + print("Note: The value of k cannot be computed before the first " \ + "call().") + return self._k + + @property + def n(self): + """Number of codeword bits""" + if self._n is None: + print("Note: The value of n cannot be computed before the first " \ + "call().") + return self._n + ######################### # Utility functions ######################### @@ -604,10 +708,35 @@ def _bmcalc(self, llr_in): llr_sign = tf.math.multiply(llr_in, op_mat_sign) half_llr_sign = tf.reshape(0.5 * llr_sign, (-1, self._no, self._num_syms, self._conv_n)) - bm = tf.math.exp(tf.math.reduce_sum(half_llr_sign, axis=-1)) + + if self._algorithm in ['log', 'maxlog']: + bm = tf.math.reduce_sum(half_llr_sign, axis=-1) + else: + bm = tf.math.exp(tf.math.reduce_sum(half_llr_sign, axis=-1)) return bm + def _initialize(self, llr_ch): + if self._algorithm in ['log', 'maxlog']: + init_vals = -np.inf, 0.0 + else: + init_vals = 0.0, 1.0 + alpha_init_np = np.full((self._ns,), init_vals[0]) + alpha_init_np[0] = init_vals[1] + + beta_init_np = alpha_init_np + if not self._terminate: + eq_prob = 1./self._ns + if self._algorithm in ['log', 'maxlog']: + eq_prob = np.log(eq_prob) + beta_init_np = np.full((self._ns,), eq_prob) + + alpha_init = tf.convert_to_tensor(alpha_init_np, dtype=tf.float32) + alpha_init = tf.tile(alpha_init[None,:], [tf.shape(llr_ch)[0], 1]) + beta_init = tf.convert_to_tensor(beta_init_np, dtype=tf.float32) + beta_init = tf.tile(beta_init[None,:], [tf.shape(llr_ch)[0], 1]) + return alpha_init, beta_init + def _update_fwd(self, alph_init, bm_mat, llr): """ Run forward update from time t=0 to t=k-1. @@ -617,37 +746,46 @@ def _update_fwd(self, alph_init, bm_mat, llr): """ alph_ta = tf.TensorArray(tf.float32, size=self._num_syms+1, dynamic_size=False, clear_after_read=False) - - alph_prev = alph_init - alph_prev = tf.cast(alph_prev, tf.float32) + alph_prev = tf.cast(alph_init, tf.float32) # (bs, _Ns, _ni, 2) matrix ipst_ip_mask = tf.tile( self.ipst_ip_idx[None,:],[tf.shape(alph_init)[0],1,1,1]) - # (bs, _Ns, _ni) matricx, by from state + # (bs, _Ns, _ni) matrix, by from state op_mask = tf.tile(self.trellis.op_by_fromnode[None,:,:], [tf.shape(alph_init)[0],1,1]) ipbit_mat = tf.tile(tf.range(self._ni)[None, None, :], [tf.shape(alph_init)[0], self._ns, 1]) - ipbitsign_mat = 1.0 - 2.0*tf.cast(ipbit_mat, tf.float32) + ipbitsign_mat = 1. - 2. * tf.cast(ipbit_mat, tf.float32) alph_ta = alph_ta.write(0, alph_prev) - for t in tf.range(0, self._num_syms): + for t in tf.range(self._num_syms): bm_t = bm_mat[..., t] llr_t = 0.5 * llr[...,t][:, None,None] bm_byfromst = tf.gather(bm_t, op_mask, batch_dims=1) - llr_byfromst = tf.math.exp(tf.math.multiply( - tf.tile(llr_t,[1, self._ns, self._ni]), ipbitsign_mat)) - gamma_byfromst = tf.multiply(llr_byfromst, bm_byfromst) - - alph_gam_prod = tf.math.multiply(gamma_byfromst, + signed_half_llr = tf.math.multiply( + tf.tile(llr_t,[1, self._ns, self._ni]), ipbitsign_mat) + if self._algorithm in ['log', 'maxlog']: + llr_byfromst = signed_half_llr + gamma_byfromst = llr_byfromst + bm_byfromst + alph_gam_prod = gamma_byfromst + alph_prev[:,:,None] + else: + llr_byfromst = tf.math.exp(signed_half_llr) + gamma_byfromst = tf.multiply(llr_byfromst, bm_byfromst) + alph_gam_prod = tf.math.multiply(gamma_byfromst, alph_prev[:,:,None]) + alphgam_bytost = tf.gather_nd(alph_gam_prod, ipst_ip_mask, batch_dims=1) - alph_t = tf.math.reduce_sum(alphgam_bytost, axis=-1) - alph_t_sum = tf.reduce_sum(alph_t, axis=-1) - alph_t = tf.divide(alph_t, tf.tile(alph_t_sum[:,None],[1,self._ns])) + if self._algorithm =='map': + alph_t = tf.math.reduce_sum(alphgam_bytost, axis=-1) + alph_t_sum = tf.reduce_sum(alph_t, axis=-1) + alph_t = tf.divide(alph_t, tf.tile(alph_t_sum[:,None],[1,self._ns])) + elif self._algorithm == 'log': + alph_t = tf.math.reduce_logsumexp(alphgam_bytost, axis=-1) + else: # self._algorithm = 'maxlog' + alph_t = tf.math.reduce_max(alphgam_bytost, axis=-1) alph_prev = alph_t alph_ta = alph_ta.write(t+1, alph_t) @@ -681,28 +819,54 @@ def _update_bwd(self, beta_init, bm_mat, llr, alpha_ta): for t in tf.range(self._num_syms-1, -1, -1): bm_t = bm_mat[..., t] llr_t = 0.5 * llr[...,t][:, None,None] + signed_half_llr = tf.math.multiply( + tf.tile(llr_t,[1, self._ns, self._ni]), ipbitsign_mat) bm_byfromst = tf.gather(bm_t, op_mask, batch_dims=1) - llr_byfromst = tf.math.exp(tf.math.multiply( - tf.tile(llr_t,[1, self._ns, self._ni]), ipbitsign_mat)) - gamma_byfromst = tf.multiply(bm_byfromst, llr_byfromst) + + if self._algorithm in ['log', 'maxlog']: + llr_byfromst = signed_half_llr + gamma_byfromst = tf.math.add(llr_byfromst, bm_byfromst) + else: + llr_byfromst = tf.math.exp(signed_half_llr) + gamma_byfromst = tf.multiply(llr_byfromst, bm_byfromst) beta_bytonode = tf.gather(beta_next, tonode_mask, batch_dims=1) - beta_gam_prod = tf.math.multiply(gamma_byfromst, beta_bytonode) - beta_t = tf.math.reduce_sum(beta_gam_prod, axis=-1) - beta_t_sum = tf.reduce_sum(beta_t, axis=-1) - beta_t = tf.divide(beta_t, tf.tile(beta_t_sum[:,None],[1,self._ns])) + + if self._algorithm not in ['log', 'maxlog']: + beta_gam_prod = tf.math.multiply(gamma_byfromst, beta_bytonode) + beta_t = tf.math.reduce_sum(beta_gam_prod, axis=-1) + beta_t_sum = tf.reduce_sum(beta_t, axis=-1) + beta_t = tf.divide(beta_t, tf.tile(beta_t_sum[:,None],[1,self._ns])) + elif self._algorithm == 'log': + beta_gam_prod = gamma_byfromst + beta_bytonode + beta_t = tf.math.reduce_logsumexp(beta_gam_prod, axis=-1, keepdims=False) + else: #self._algorithm = 'maxlog' + beta_gam_prod = gamma_byfromst + beta_bytonode + beta_t = tf.math.reduce_max(beta_gam_prod, axis=-1) alph_t = alpha_ta.read(t) - llr_op_t0 = tf.math.multiply( - tf.math.multiply(alph_t, gamma_byfromst[...,0]), - beta_bytonode[...,0]) - llr_op_t1 = tf.math.multiply( - tf.math.multiply(alph_t,gamma_byfromst[...,1]), - beta_bytonode[...,1]) - llr_op_t = tf.math.log(tf.divide(tf.reduce_sum(llr_op_t0, axis=-1), - tf.reduce_sum(llr_op_t1,axis=-1))) - llr_op_ta = llr_op_ta.write(t, llr_op_t) + if self._algorithm not in ['log', 'maxlog']: + llr_op_t0 = tf.math.multiply( + tf.math.multiply(alph_t, gamma_byfromst[...,0]), + beta_bytonode[...,0]) + llr_op_t1 = tf.math.multiply( + tf.math.multiply(alph_t,gamma_byfromst[...,1]), + beta_bytonode[...,1]) + llr_op_t = tf.math.log(tf.divide(tf.reduce_sum(llr_op_t0, axis=-1), + tf.reduce_sum(llr_op_t1,axis=-1))) + else: + llr_op_t0 = alph_t + gamma_byfromst[...,0] + beta_bytonode[...,0] + llr_op_t1 = alph_t + gamma_byfromst[...,1] + beta_bytonode[...,1] + if self._algorithm == 'log': + llr_op_t = tf.math.subtract( + tf.math.reduce_logsumexp(llr_op_t0, axis=-1), + tf.math.reduce_logsumexp(llr_op_t1, axis=-1)) + else: + llr_op_t = tf.math.subtract( + tf.math.reduce_max(llr_op_t0, axis=-1), + tf.math.reduce_max(llr_op_t1, axis=-1)) + llr_op_ta = llr_op_ta.write(t, llr_op_t) beta_next = beta_t llr_op = tf.transpose(llr_op_ta.stack()) @@ -722,13 +886,10 @@ def build(self, input_shape): else: self._n = input_shape[0][-1] - self._num_syms = int(self._n*self.coderate) - if self._terminate: - self._num_term_syms = self._mu - self._num_term_bits = self._num_term_syms * 2 - else: - self._num_term_syms = 0 - self._num_term_bits = 0 + self._num_syms = int(self._n*self._coderate_desired) + + self._num_term_syms = self._mu if self._terminate else 0 + self._num_term_bits = int(self._num_term_syms/self._coderate_desired) self._k = self._num_syms - self._num_term_syms @@ -752,7 +913,7 @@ def call(self, inputs): output_shape = llr_ch.get_shape().as_list() - # allow different codeword lenghts in eager mode + # allow different codeword lengths in eager mode if output_shape[-1] != self._n: if isinstance(inputs, (tuple, list)): self.build((inputs[0].get_shape(), @@ -769,20 +930,10 @@ def call(self, inputs): dtype=tf.float32) llr_ch = -1. * llr_ch llr_apr = -1. * llr_apr + # Branch metrics matrix for a given y bm_mat = self._bmcalc(llr_ch) - - alpha_prev_np = np.full((self._ns,), 0.0) - alpha_prev_np[0] = 1.0 - alpha_init = tf.convert_to_tensor(alpha_prev_np, dtype=tf.float32) - alpha_init = tf.tile(alpha_init[None,:], [tf.shape(llr_ch)[0], 1]) - if self._terminate: - beta_init = alpha_init - else: - eq_prob = 1./self._ns - beta_init = tf.convert_to_tensor(np.full((self._ns,), eq_prob), - dtype=tf.float32) - beta_init = tf.tile(beta_init[None,:], [tf.shape(llr_ch)[0], 1]) + alpha_init, beta_init = self._initialize(llr_ch) alph_ta = self._update_fwd(alpha_init, bm_mat, llr_apr) llr_op = self._update_bwd(beta_init, bm_mat, llr_apr, alph_ta) diff --git a/sionna/fec/conv/encoding.py b/sionna/fec/conv/encoding.py index 5bea1995..84e97f27 100644 --- a/sionna/fec/conv/encoding.py +++ b/sionna/fec/conv/encoding.py @@ -11,11 +11,10 @@ class ConvEncoder(Layer): # pylint: disable=line-too-long - r"""ConvEncoder(gen_poly=None, rate= 1/2, constraint_length=3, output_dtype=tf.float32, **kwargs) + r"""ConvEncoder(gen_poly=None, rate= 1/2, constraint_length=3, rsc=False, terminate=False, output_dtype=tf.float32, **kwargs) - Encodes an information binary tensor to a convolutional codeword. - Only non-recursive encoding is available. Currently, only generator - polynomials for codes of rate=1/n for n=2,3,4,... are allowed. + Encodes an information binary tensor to a convolutional codeword. Currently, + only generator polynomials for codes of rate=1/n for n=2,3,4,... are allowed. The class inherits from the Keras layer class and can be used as layer in a Keras model. @@ -34,6 +33,17 @@ class ConvEncoder(Layer): Valid values are between 3 and 8 inclusive. Only required if ``gen_poly`` is `None`. + rsc: boolean + Boolean flag indicating whether the Trellis generated is recursive + systematic or not. If `True`, the encoder is recursive-systematic. + In this case first polynomial in ``gen_poly`` is used as the + feedback polynomial. Defaults to `False`. + + terminate: boolean + Encoder is terminated to all zero state if `True`. + If terminated, the `true` rate of the code is slightly lower than + ``rate``. + output_dtype: tf.DType Defaults to `tf.float32`. Defines the output datatype of the layer. @@ -41,14 +51,14 @@ class ConvEncoder(Layer): ----- inputs : [...,k], tf.float32 2+D tensor containing the information bits where `k` is the - information length. + information length Output ------ : [...,k/rate], tf.float32 2+D tensor containing the encoded codeword for the given input information tensor where `rate` is - :math:`\frac{1}{len\left(\textrm{gen_poly}\right)}` + :math:`\frac{1}{\textrm{len}\left(\textrm{gen_poly}\right)}` (if ``gen_poly`` is provided). Note @@ -77,12 +87,21 @@ class ConvEncoder(Layer): polynomial `10011` has a ``constraint_length`` of 5, however its ``memory`` is only 4. + When ``terminate`` is `True`, the true rate of the convolutional + code is slightly lower than ``rate``. It equals + :math:`\frac{r*k}{k+\mu}` where `r` denotes ``rate`` and + :math:`\mu` is ``constraint_length`` - 1. For example when + ``terminate`` is `True`, ``k=100``, + :math:`\mu=4` and ``rate`` =0.5, true rate equals + :math:`\frac{0.5*100}{104}=0.481`. """ def __init__(self, gen_poly=None, rate=1/2, constraint_length=3, + rsc=False, + terminate=False, output_dtype=tf.float32, **kwargs): @@ -107,8 +126,15 @@ def __init__(self, "Rate must be 1/3 or 1/2." self._gen_poly = polynomial_selector(rate, constraint_length) - self._coderate = 1/len(self.gen_poly) - self._trellis = Trellis(self.gen_poly,rsc=False) + self._rsc = rsc + self._terminate = terminate + + self._coderate_desired = 1/len(self.gen_poly) + # Differ when terminate is True + self._coderate = self._coderate_desired + + self._trellis = Trellis(self.gen_poly,rsc=self._rsc) + self._mu = self.trellis._mu # conv_k denotes number of input bit streams. # Only 1 allowed in current implementation @@ -131,19 +157,48 @@ def __init__(self, @property def gen_poly(self): - """The generator polynomial used by the encoder.""" + """Generator polynomial used by the encoder""" return self._gen_poly @property def coderate(self): - """Rate of the code used in the encoder.""" + """Rate of the code used in the encoder""" + if self.terminate and self._k is None: + print("Note that, due to termination, the true coderate is lower "\ + "than the returned design rate. "\ + "The exact true rate is dependent on the value of k and "\ + "hence cannot be computed before the first call().") + elif self.terminate and self._k is not None: + term_factor = (self._k/(self._k + self._mu)) + self._coderate = self._coderate_desired*term_factor return self._coderate @property def trellis(self): - """Trellis object used during encoding.""" + """Trellis object used during encoding""" return self._trellis + @property + def terminate(self): + """Indicates if the convolutional encoder is terminated""" + return self._terminate + + @property + def k(self): + """Number of information bits per codeword""" + if self._k is None: + print("Note: The value of k cannot be computed before the first " \ + "call().") + return self._k + + @property + def n(self): + """Number of codeword bits""" + if self._n is None: + print("Note: The value of n cannot be computed before the first " \ + "call().") + return self._n + ######################### # Keras layer functions ######################### @@ -178,9 +233,10 @@ def call(self, inputs): msg = tf.cast(inputs, tf.int32) output_shape = msg.get_shape().as_list() output_shape[0] = -1 # overwrite batch dim (can be none in keras) - output_shape[-1] = self._n # assign n to the last dimension + output_shape[-1] = self._n # assign n to the last dim msg_reshaped = tf.reshape(msg, [-1, self._k]) + term_syms = int(self._mu) if self._terminate else 0 prev_st = tf.zeros([tf.shape(msg_reshaped)[0]], tf.int32) ta = tf.TensorArray(tf.int32, size=self.num_syms, dynamic_size=False) @@ -201,8 +257,36 @@ def call(self, inputs): idx_bits = int2bin_tf(idx_syms, self._conv_n) ta = ta.write(idx//self._conv_k, idx_bits) prev_st = new_st - cw = tf.concat(tf.unstack(ta.stack()), axis=1) + + ta_term = tf.TensorArray(tf.int32, size=term_syms, dynamic_size=False) + # Termination + if self._terminate: + if self._rsc: + fb_poly = tf.constant([int(x) for x in self.gen_poly[0][1:]]) + fb_poly_tiled = tf.tile( + tf.expand_dims(fb_poly,0),[tf.shape(prev_st)[0],1]) + + for idx in tf.range(0, term_syms, self._conv_k): + prev_st_bits = int2bin_tf(prev_st, self._mu) + if self._rsc: + msg_idx = tf.math.reduce_sum( + tf.multiply(fb_poly_tiled, prev_st_bits),-1) + msg_idx = tf.squeeze(int2bin_tf(msg_idx,1),-1) + else: + msg_idx = tf.zeros((tf.shape(prev_st)[0],), dtype=tf.int32) + + indices = tf.stack([prev_st, msg_idx], -1) + new_st = tf.gather_nd(self._trellis.to_nodes, indices=indices) + idx_syms = tf.gather_nd(self._trellis.op_mat, + tf.stack([prev_st, new_st], -1)) + idx_bits = int2bin_tf(idx_syms, self._conv_n) + ta_term = ta_term.write(idx//self._conv_k, idx_bits) + prev_st = new_st + + term_bits = tf.concat(tf.unstack(ta_term.stack()), axis=1) + cw = tf.concat([cw, term_bits], axis=-1) + cw = tf.cast(cw, self.output_dtype) cw_reshaped = tf.reshape(cw, output_shape) diff --git a/sionna/fec/conv/utils.py b/sionna/fec/conv/utils.py index 7a4a7ed8..e68d5e59 100644 --- a/sionna/fec/conv/utils.py +++ b/sionna/fec/conv/utils.py @@ -17,11 +17,11 @@ def polynomial_selector(rate, constraint_length): Input ----- rate: float - A float defining the desired rate of the code. - Currently, only r=1/3 and r=1/2 is supported. + Desired rate of the code. + Currently, only r=1/3 and r=1/2 are supported. constraint_length: int - An integer defining the desired constraint length of the encoder. + Desired constraint length of the encoder Output ------ @@ -64,7 +64,7 @@ def polynomial_selector(rate, constraint_length): class Trellis(object): - """Trellis(gen_poly) + """Trellis(gen_poly, rsc=True) Trellis structure for a given generator polynomial. Defines state transitions and output symbols (and bits) for each current @@ -73,19 +73,20 @@ class Trellis(object): Parameters ---------- gen_poly: tuple - sequence of strings with each string being a 0,1 sequence. If None, - ``rate`` and ``constraint_length`` must be provided. - If `rsc` is True, then first polynomial will act as denominator - for the remaining generator polynomials. - For e.g., ('111', '101', '011') the generator matrix equals to - [1, 1+D^2/(1+D+D^2), D+D^2/(1+D+D^2)]. - Currently Trellis is only implemented for Generator matrices - of size 1/n. + Sequence of strings with each string being a 0,1 sequence. + If `None`, ``rate`` and ``constraint_length`` must be provided. If + `rsc` is True, then first polynomial will act as denominator for + the remaining generator polynomials. For e.g., ``rsc`` = `True` and + ``gen_poly`` = (`111`, `101`, `011`) implies generator matrix equals + :math:`G(D)=[\\frac{1+D^2}{1+D+D^2}, \\frac{D+D^2}{1+D+D^2}]`. + Currently Trellis is only implemented for generator matrices of + size :math:`\\frac{1}{n}`. + rsc: boolean Boolean flag indicating whether the Trellis is recursive systematic - or not. If `"True"`, the encoder is recursive systematic. In this + or not. If `True`, the encoder is recursive systematic in which case first polynomial in ``gen_poly`` is used as the feedback - polynomial. Default is `"True"`. + polynomial. Default is `True`. """ def __init__(self, gen_poly, rsc=True): @@ -165,7 +166,6 @@ def _generate_transitions(self): new_bit = int2bin(ip_bit + fb_bit, 1)[0] else: new_bit = ip_bit - state_bits = [new_bit] + curr_st_bits j_to = bin2int(state_bits[:-1]) diff --git a/sionna/fec/crc.py b/sionna/fec/crc.py index af41ad6f..e6c762f9 100644 --- a/sionna/fec/crc.py +++ b/sionna/fec/crc.py @@ -71,6 +71,9 @@ def __init__(self, crc_degree, dtype=tf.float32, **kwargs): # init 5G CRC polynomial self._crc_pol, self._crc_length = self._select_crc_pol(self._crc_degree) + self._k = None + self._n = None + ######################################### # Public methods and properties ######################################### @@ -90,6 +93,16 @@ def crc_pol(self): """CRC polynomial in binary representation.""" return self._crc_pol + @property + def k(self): + """Number of information bits per codeword.""" + return self._k + + @property + def n(self): + """Number of codeword bits.""" + return self._n + ######################### # Utility methods ######################### @@ -170,6 +183,9 @@ def build(self, input_shape): g_mat_crc = self._gen_crc_mat(k, self.crc_pol) self._g_mat_crc = tf.constant(g_mat_crc, dtype=tf.float32) + self._k = k + self._n = k + g_mat_crc.shape[1] + def call(self, inputs): """cyclic redundancy check function. diff --git a/sionna/fec/ldpc/decoding.py b/sionna/fec/ldpc/decoding.py index 6cc2431f..8f78b78e 100644 --- a/sionna/fec/ldpc/decoding.py +++ b/sionna/fec/ldpc/decoding.py @@ -75,6 +75,8 @@ class LDPCBPDecoder(Layer): Fig. 1: Weighted BP as proposed in [Nachmani]_. + For numerical stability, the decoder applies LLR clipping of + +/- 20 to the input LLRs. The class inherits from the Keras layer class and can be used as layer in a Keras model. @@ -325,8 +327,8 @@ def __init__(self, # clipping value for the atanh function is applied (tf.float32 is used) self._atanh_clip_value = 1 - 1e-7 - # clipping for min-sum decoding - self._llr_max_minsum = 20 + # internal value for llr clipping + self._llr_max = 20 # init code parameters self._num_cns = pcm.shape[0] # total number of check nodes @@ -751,8 +753,8 @@ def _cn_update_minsum(self, msg): # clip values for numerical stability msg = tf.clip_by_value(msg, - clip_value_min=-self._llr_max_minsum, - clip_value_max=self._llr_max_minsum) + clip_value_min=-self._llr_max, + clip_value_max=self._llr_max) # calculate sign of outgoing msg sign_val = tf.ragged.map_flat_values(self._sign_val_minsum, msg) @@ -901,6 +903,11 @@ def call(self, inputs): # internal calculations still in tf.float32 llr_ch = tf.cast(llr_ch, tf.float32) + # clip llrs for numerical stability + llr_ch = tf.clip_by_value(llr_ch, + clip_value_min=-self._llr_max, + clip_value_max=self._llr_max) + # last dim must be of length n tf.debugging.assert_equal(tf.shape(llr_ch)[-1], self._num_vns, @@ -1044,6 +1051,9 @@ class LDPC5GDecoder(LDPCBPDecoder): (the training of some check node types may be not supported) following the concept of "weighted BP" [Nachmani]_. + For numerical stability, the decoder applies LLR clipping of + +/- 20 to the input LLRs. + The class inherits from the Keras layer class and can be used as layer in a Keras model. diff --git a/sionna/fec/ldpc/encoding.py b/sionna/fec/ldpc/encoding.py index 568f10b1..b01a6bf2 100644 --- a/sionna/fec/ldpc/encoding.py +++ b/sionna/fec/ldpc/encoding.py @@ -12,138 +12,7 @@ from . import codes # pylint: disable=relative-beyond-top-level import numbers # to check if n, k are numbers -class AllZeroEncoder(Layer): - """AllZeroEncoder(k, n, dtype=tf.float32, **kwargs) - - Dummy encoder that always outputs the all-zero codeword of length ``n``. - Note that this encoder is a dummy encoder and does NOT perform real - encoding! - - The class inherits from the Keras layer class and can be used as layer in a - Keras model. - - Parameters - ---------- - k: int - Defining the number of information bit per codeword. - - n: int - Defining the desired codeword length. - - dtype: tf.DType - Defaults to `tf.float32`. Defines the datatype for internal - calculations and the output dtype. - - Input - ----- - inputs: [...,k], tf.float32 - 2+D tensor containing arbitrary values (not used!). - - Output - ------ - : [...,n], tf.float32 - 2+D tensor containing all-zero codewords. - - Raises - ------ - AssertionError - ``k`` and ``n`` must be positive integers and ``k`` must be smaller - (or equal) than ``n``. - - AssertionError - If ``k`` is not `int`. - - AssertionError - If ``n`` is not `int`. - - Note - ---- - As the all-zero codeword is part of any linear code, it is often used - to simulate BER curves of arbitrary (LDPC) codes without the need of - having access to the actual generator matrix. However, this `"all-zero - codeword trick"` requires symmetric channels (such as BPSK), otherwise - scrambling is required (cf. [Pfister]_ for further details). - - This encoder is a dummy encoder that is needed for some all-zero - codeword simulations independent of the input. It does NOT perform - real encoding although the information bits are taken as input. - This is just to ensure compatibility with other encoding layers. - """ - - def __init__(self, - k, - n, - dtype=tf.float32, - **kwargs): - - super().__init__(dtype=dtype, **kwargs) - - #assert error if r>1 or k,n are negativ - assert isinstance(k, numbers.Number), "k must be a number." - assert isinstance(n, numbers.Number), "n must be a number." - k = int(k) # k or n can be float (e.g. as result of n=k*r) - n = int(n) # k or n can be float (e.g. as result of n=k*r) - assert k>-1, "k cannot be negative." - assert n>-1, "n cannot be negative." - assert n>=k, "Invalid coderate (>1)." - # init encoder parameters - self._k = k - self._n = n - self._coderate = k / n - - ######################################### - # Public methods and properties - ######################################### - - @property - def k(self): - """Number of information bits per codeword.""" - return self._k - - @property - def n(self): - "Codeword length." - return self._n - - @property - def coderate(self): - """Coderate of the LDPC code.""" - return self._coderate - - ######################### - # Keras layer functions - ######################### - - def build(self, input_shape): - """Nothing to build.""" - pass - - def call(self, inputs): - """Encoding function that outputs the all-zero codeword. - - This function returns the all-zero codeword of shape `[..., n]`. - Note that this encoder is a dummy encoder and does NOT perform real - encoding! - - Args: - inputs (tf.float32): Tensor of arbitrary shape. - - Returns: - `tf.float32`: Tensor of shape `[...,n]`. - - Note: - This encoder is a dummy encoder that is needed for some all-zero - codeword simulations independent of the input. It does NOT perform - real encoding although the information bits are taken as input. - This is just to ensure compatibility with other encoding layers. - """ - # keep shape of first dimensions - # return an all-zero tensor of shape [..., n] - output_shape = tf.concat([tf.shape(inputs)[:-1], - tf.constant(self._n, shape=[1])], - 0) - c = tf.zeros(output_shape, dtype=super().dtype) - return c +from sionna.fec.linear import AllZeroEncoder as AllZeroEncoder_new class LDPC5GEncoder(Layer): # pylint: disable=line-too-long @@ -880,3 +749,20 @@ def call(self, inputs): c_reshaped = tf.reshape(c_short, output_shape) return tf.cast(c_reshaped, self._dtype) + + +########################################################### +# Deprecated aliases that will not be included in the next +# major release +########################################################### + +def AllZeroEncoder(k, + n, + dtype=tf.float32, + **kwargs): + print("Warning: The alias fec.ldpc.AllZeroEncoder will not be included in "\ + "Sionna 1.0. Please use sionna.fec.linear.AllZeroEncoder instead.") + return AllZeroEncoder_new(k=k, + n=n, + dtype=dtype, + **kwargs) diff --git a/sionna/fec/linear/__init__.py b/sionna/fec/linear/__init__.py new file mode 100644 index 00000000..a3724968 --- /dev/null +++ b/sionna/fec/linear/__init__.py @@ -0,0 +1,10 @@ +# +# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +"""Linear code sub-package of the Sionna library.""" + +from .encoding import LinearEncoder, AllZeroEncoder +from .decoding import OSDecoder + + diff --git a/sionna/fec/linear/decoding.py b/sionna/fec/linear/decoding.py new file mode 100644 index 00000000..24d3b5f3 --- /dev/null +++ b/sionna/fec/linear/decoding.py @@ -0,0 +1,473 @@ +# +# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +"""Layers for decoding of linear codes.""" + +import tensorflow as tf +import numpy as np +import scipy as sp # for sparse H matrix computations +from tensorflow.keras.layers import Layer +from sionna.fec.utils import pcm2gm, int_mod_2, make_systematic +from sionna.utils import hard_decisions +import itertools + +class OSDecoder(Layer): + # pylint: disable=line-too-long + r"""OSDecoder(enc_mat=None, t=0, is_pcm=False, encoder=None, dtype=tf.float32, **kwargs) + + Ordered statistics decoding (OSD) for binary, linear block codes. + + This layer implements the OSD algorithm as proposed in [Fossorier]_ and, + thereby, approximates maximum likelihood decoding for a sufficiently large + order :math:`t`. The algorithm works for arbitrary linear block codes, but + has a high computational complexity for long codes. + + The algorithm consists of the following steps: + + 1. Sort LLRs according to their reliability and apply the same column + permutation to the generator matrix. + + 2. Bring the permuted generator matrix into its systematic form + (so-called *most-reliable basis*). + + 3. Hard-decide and re-encode the :math:`k` most reliable bits and + discard the remaining :math:`n-k` received positions. + + 4. Generate all possible error patterns up to :math:`t` errors in the + :math:`k` most reliable positions find the most likely codeword within + these candidates. + + This implementation of the OSD algorithm uses the LLR-based distance metric + from [Stimming_LLR_OSD]_ which simplifies the handling of higher-order + modulation schemes. + + The class inherits from the Keras layer class and can be used as layer in a + Keras model. + + Parameters + ---------- + enc_mat : [k, n] or [n-k, n], ndarray + Binary generator matrix of shape `[k, n]`. If ``is_pcm`` is + True, ``enc_mat`` is interpreted as parity-check matrix of shape + `[n-k, n]`. + + t : int + Order of the OSD algorithm + + is_pcm: bool + Defaults to False. If True, ``enc_mat`` is interpreted as parity-check + matrix. + + encoder: Layer + Keras layer that implements a FEC encoder. + If not None, ``enc_mat`` will be ignored and the code as specified by he + encoder is used to initialize OSD. + + dtype: tf.DType + Defaults to `tf.float32`. Defines the datatype for the output dtype. + + Input + ----- + llrs_ch: [...,n], tf.float32 + 2+D tensor containing the channel logits/llr values. + + Output + ------ + : [...,n], tf.float32 + 2+D Tensor of same shape as ``llrs_ch`` containing + binary hard-decisions of all codeword bits. + + Note + ---- + OS decoding is of high complexity and is only feasible for small values of + :math:`t` as :math:`{n \choose t}` patterns must be evaluated. The + advantage of OSD is that it works for arbitrary linear block codes and + provides an estimate of the expected ML performance for sufficiently large + :math:`t`. However, for some code families, more efficient decoding + algorithms with close to ML performance exist which can exploit certain + code specific properties. Examples of such decoders are the + :class:`~sionna.fec.conv.ViterbiDecoder` algorithm for convolutional codes + or the :class:`~sionna.fec.polar.decoding.PolarSCLDecoder` for Polar codes + (for a sufficiently large list size). + + It is recommended to run the decoder in XLA mode as it + significantly reduces the memory complexity. + """ + + def __init__(self, + enc_mat=None, + t=0, + is_pcm=False, + encoder=None, + dtype=tf.float32, + **kwargs): + + super().__init__(dtype=dtype, **kwargs) + + assert isinstance(is_pcm, bool), 'is_pcm must be bool.' + + self._llr_max = 100. # internal clipping value for llrs + + if enc_mat is not None: + # check that gm is binary + if isinstance(enc_mat, np.ndarray): + assert np.array_equal(enc_mat, enc_mat.astype(bool)), \ + 'PC matrix must be binary.' + elif isinstance(enc_mat, sp.sparse.csr_matrix): + assert np.array_equal(enc_mat.data, enc_mat.data.astype(bool)),\ + 'PC matrix must be binary.' + elif isinstance(enc_mat, sp.sparse.csc_matrix): + assert np.array_equal(enc_mat.data, enc_mat.data.astype(bool)),\ + 'PC matrix must be binary.' + else: + raise TypeError("Unsupported dtype of pcm.") + + if dtype not in (tf.float16, tf.float32, tf.float64): + raise ValueError( + 'dtype must be {tf.float16, tf.float32, tf.float64}.') + + assert (int(t)==t), "t must be int." + self._t = int(t) + + if encoder is not None: + # test that encoder is already initialized (relevant for conv codes) + if encoder.k is None: + raise AttributeError("It seems as if the encoder is not "\ + "initialized or has no attribute k.") + # encode identity matrix to get k basis vectors of the code + u = tf.expand_dims(tf.eye(encoder.k), axis=0) + # encode and remove batch_dim + self._gm = tf.cast(tf.squeeze(encoder(u), axis=0), self.dtype) + else: + assert (enc_mat is not None),\ + "enc_mat cannot be None if no encoder is provided." + if is_pcm: + gm = pcm2gm(enc_mat) + else: + # check if gm is of full rank (raise error otherwise) + make_systematic(enc_mat) + gm = enc_mat + self._gm = tf.constant(gm, dtype=self.dtype) + + self._k = self._gm.shape[0] + self._n = self._gm.shape[1] + + # init error patterns + num_patterns = self._num_error_patterns(self._n, self._t) + + # storage/computational complexity scales with n + num_symbols = num_patterns * self._n + if num_symbols>1e9: # number still to be optimized + print(f"Note: Required memory complexity is large for the "\ + f"given code parameters and t={t}. Please consider small " \ + f"batch-sizes to keep the inference complexity small and " \ + f"activate XLA mode if possible." ) + if num_symbols>1e11: # number still to be optimized + raise ResourceWarning("Due to its high complexity, OSD is not " \ + "feasible for the selected parameters. " \ + "Please consider using a smaller value for t.") + + # pre-compute all error patterns + self._err_patterns = [] + for t_i in range(1, t+1): + self._err_patterns.append(self._gen_error_patterns(self._k, t_i)) + + ######################################### + # Public methods and properties + ######################################### + + @property + def gm(self): + """Generator matrix of the code""" + return self._gm + + @property + def n(self): + """Codeword length""" + return self._n + + @property + def k(self): + """Number of information bits per codeword""" + return self._k + + @property + def t(self): + """Order of the OSD algorithm""" + return self._t + + ######################### + # Utility methods + ######################### + + def _num_error_patterns(self, n, t): + r"""Returns number of possible error patterns for t errors in n + positions, i.e., calculates :math:`{n \choose t}`. + + Input + ----- + n: int + length of vector. + + t: int + number of errors. + """ + return sp.special.comb(n, t, exact=True, repetition=False) + + def _gen_error_patterns(self, n, t): + r"""Returns list of all possible error patterns for t errors in n + positions. + + Input + ----- + n: int + Length of vector. + + t: int + Number of errors. + + Output + ------ + : [num_patterns, t], tf.int32 + Tensor of size `num_patterns`=:math:`{n \choose t}` containing the + t error indices. + """ + + err_patterns = [] + for p in itertools.combinations(range(n), t): + err_patterns.append(p) + + return tf.constant(err_patterns) + + def _get_dist(self, llr, c_hat): + """Distance function used for ML candidate selection. + + Currently, the distance metric from Polar decoding [Stimming_LLR_OSD]_ + literature is implemented. + + Input + ----- + llr: [bs, n], tf.float32 + Received llrs of the channel observations. + + c_hat: [bs, num_cand, n], tf.float32 + Candidate codewords for which the distance to ``llr`` shall be + evaluated. + + Output + ------ + : [bs, num_cand], tf.float32 + Distance between ``llr`` and ``c_hat`` for each of the `num_cand` + codeword candidates. + + Reference + --------- + [Stimming_LLR_OSD] Alexios Balatsoukas-Stimming, Mani Bastani Parizi, + Andreas Burg, "LLR-Based Successive Cancellation List Decoding + of Polar Codes." IEEE Trans Signal Processing, 2015. + """ + + # broadcast llr to all codeword candidates + llr = tf.expand_dims(llr, axis=1) + llr_sign = llr * (-2.*c_hat + 1.) # apply BPSK mapping + + d = tf.math.log(1. + tf.exp(llr_sign)) + return tf.reduce_mean(d, axis=2) + + def _find_min_dist(self, llr_ch, ep, gm_mrb, c): + r"""Find error pattern which leads to minimum distance. + + Input + ----- + llr_ch: [bs, n], tf.float32 + Channel observations as llrs after mrb sorting. + + ep: [num_patterns, t], tf.int32 + Tensor of size `num_patterns`=:math:`{n \choose t}` containing the + t error indices. + + gm_mrb: [bs, k, n] tf.float32 + Most reliable basis for each batch example. + + c: [bs, n], tf.float32 + Most reliable base codeword. + + Output + ------ + : [bs], tf.float32 + Distance of the most likely codeword to ``llr_ch`` after testing all + ``ep`` error patterns. + + : [bs, n], tf.float32 + The most likely codeword after testing against all ``ep`` error + patterns. + """ + + # generate all test candidates for each possible error pattern + e = tf.gather(gm_mrb, ep, axis=1) + e = tf.reduce_sum(e, axis=2) + e += tf.expand_dims(c, axis=1) # add to mrb codeword + c_cand = int_mod_2(e) # apply modulo-2 operation + + # calculate distance for each candidate + # where c_cand has shape [bs, num_patterns, n] + d = self._get_dist(llr_ch, c_cand) + + # find candidate index with smallest metric + idx = tf.argmin(d, axis=1) + c_hat = tf.gather(c_cand, idx, batch_dims=1) + d = tf.gather(d, idx, batch_dims=1) + return d, c_hat + + def _find_mrb(self, gm): + """Find most reliable basis for all generator matrices in batch. + + Input + ----- + gm: [bs, k, n] tf.float32 + Generator matrix for each batch example. + + Output + ------ + gm_mrb: [bs, k, n] tf.float32 + Most reliable basis in systematic form for each batch example. + + idx_sort: [bs, n] tf.int64 + Indices of column permutations applied during mrb calculation. + """ + + bs = tf.shape(gm)[0] + idx_pivot = tf.TensorArray(tf.int64, self._k, dynamic_size=False) + + # bring gm in systematic form (by so-called pivot method) + for idx_c in tf.range(self._k): + + # find pivot (i.e., first pos with index 1) + idx_p = tf.argmax(gm[:, idx_c, :], axis=-1) + + # store pivot position + idx_pivot = idx_pivot.write(idx_c, idx_p) + + # and eliminate the column in all other rows + r = tf.gather(gm, idx_p, batch_dims=1, axis=-1) + + # ignore idx_c row itself by adding all-zero row + rz = tf.zeros((bs, 1), dtype=self.dtype) + r = tf.concat([r[:,:idx_c], rz , r[:,idx_c+1:]], axis=1) + + # mask is zero at all rows where pivot position of this row is zero + mask = tf.tile(tf.expand_dims(r, axis=-1), (1, 1, self._n)) + gm_off = tf.expand_dims(gm[:,idx_c,:], axis=1) + + # update all row in parallel + gm = int_mod_2(gm + mask * gm_off) # account for binary operations + + # pivot positions + idx_pivot = tf.transpose(idx_pivot.stack()) + + # find non-pivot positions (i.e., all indices that are not part of + # idx_pivot) + + # solution 1: sets.difference() does not support XLA (unknown shapes) + #idx_parity = tf.sets.difference(idx_range, idx_pivot) + #idx_parity = tf.sparse.to_dense(idx_parity) + #idx_pivot = tf.reshape(idx_pivot, (-1, self._n)) # ensure shape + + # solution 2: add large offset to pivot indices and sorting gives the + # indices of interest + idx_range = tf.tile(tf.expand_dims( + tf.range(self._n, dtype=tf.int64), axis=0), + (bs, 1)) + # large value to be added to irrelevant indices + updates = self._n * tf.ones((bs, self._k), tf.int64) + + # generate indices for tf.scatter_nd_add + s = tf.shape(idx_pivot, tf.int64) + ii, _ = tf.meshgrid(tf.range(s[0]), tf.range(s[1]), indexing='ij') + idx_updates = tf.stack([ii, idx_pivot], axis=-1) + + # add large value to pivot positions + idx = tf.tensor_scatter_nd_add(idx_range, idx_updates, updates) + + # sort and slice first n-k indices (equals parity positions) + idx_parity = tf.cast(tf.argsort(idx)[:,:self._n-self._k], tf.int64) + + idx_sort = tf.concat([idx_pivot, idx_parity], axis=1) + + # permute gm according to indices idx_sort + gm = tf.gather(gm, idx_sort, batch_dims=1, axis=-1) + + return gm, idx_sort + + ######################### + # Keras layer functions + ######################### + + def build(self, input_shape): + """Nothing to build, but check for valid shapes.""" + + assert input_shape[-1]==self._n, "Invalid input shape." + + def call(self, inputs): + r"""Applies ordered statistic decoding to inputs. + + Remark: the decoder is implemented with llr definition + llr = p(x=1)/p(x=0). + """ + + # flatten batch-dim + input_shape = tf.shape(inputs) + llr_ch = tf.reshape(inputs, (-1, self._n)) + llr_ch = tf.cast(llr_ch, self.dtype) + bs = tf.shape(llr_ch)[0] + + # clip inputs + llr_ch = tf.clip_by_value(llr_ch, -self._llr_max, self._llr_max) + + # Step 1: sort LLRs + idx_sort = tf.argsort(tf.abs(llr_ch), direction="DESCENDING") + + # permute gm per batch sample individually + gm = tf.broadcast_to(tf.expand_dims(self._gm, axis=0), + (bs, self._k,self._n)) + gm_sort = tf.gather(gm, idx_sort, batch_dims=1, axis=-1) + + # Step 2: Find most reliable basis (MRB) + gm_mrb, idx_mrb = self._find_mrb(gm_sort) + + # apply corresponding mrb permutations + idx_sort = tf.gather(idx_sort, idx_mrb, batch_dims=1) + llr_sort = tf.gather(llr_ch, idx_sort, batch_dims=1) + + # find inverse permutation for final output + idx_sort_inv = tf.argsort(idx_sort) + + # hard-decide k most reliable positions and encode + u_hd = hard_decisions(llr_sort[:,0:self._k]) + u_hd = tf.expand_dims(u_hd, axis=1) + c = tf.squeeze(tf.matmul(u_hd, gm_mrb), axis=1) + c = int_mod_2(c) + + # and search for most likely pattern + # _get_dist expects a list of candidates, thus expand_dims to [bs, 1, n] + d_best = self._get_dist(llr_sort, tf.expand_dims(c, axis=1)) + d_best = tf.squeeze(d_best, axis=1) + c_hat_best = c + + # known in advance - can be unrolled + for ep in self._err_patterns: + # compute distance for all candidate codewords + d, c_hat = self._find_min_dist(llr_sort, ep, gm_mrb, c) + + # select most likely candidate + ind = tf.expand_dims(d=2), 'The inputs must have at least rank 2.' + + def call(self, inputs): + """Generic encoding function based on generator matrix multiplication. + """ + + c = tf.linalg.matmul(inputs, self._gm) + + # faster implementation of tf.math.mod(c, 2) + c_uint8 = tf.cast(c, tf.uint8) + c_bin = tf.bitwise.bitwise_and(c_uint8, tf.constant(1, tf.uint8)) + c = tf.cast(c_bin, self.dtype) + + return c + +class AllZeroEncoder(Layer): + r"""AllZeroEncoder(k, n, dtype=tf.float32, **kwargs) + Dummy encoder that always outputs the all-zero codeword of length ``n``. + + Note that this encoder is a dummy encoder and does NOT perform real + encoding! + + The class inherits from the Keras layer class and can be used as layer in a + Keras model. + + Parameters + ---------- + k: int + Defining the number of information bit per codeword. + + n: int + Defining the desired codeword length. + + dtype: tf.DType + Defaults to `tf.float32`. Defines the datatype for internal + calculations and the output dtype. + + Input + ----- + inputs: [...,k], tf.float32 + 2+D tensor containing arbitrary values (not used!). + + Output + ------ + : [...,n], tf.float32 + 2+D tensor containing all-zero codewords. + + Raises + ------ + AssertionError + ``k`` and ``n`` must be positive integers and ``k`` must be smaller + (or equal) than ``n``. + + Note + ---- + As the all-zero codeword is part of any linear code, it is often used + to simulate BER curves of arbitrary (LDPC) codes without the need of + having access to the actual generator matrix. However, this `"all-zero + codeword trick"` requires symmetric channels (such as BPSK), otherwise + scrambling is required (cf. [Pfister]_ for further details). + + This encoder is a dummy encoder that is needed for some all-zero + codeword simulations independent of the input. It does NOT perform + real encoding although the information bits are taken as input. + This is just to ensure compatibility with other encoding layers. + """ + + def __init__(self, + k, + n, + dtype=tf.float32, + **kwargs): + + super().__init__(dtype=dtype, **kwargs) + + #assert error if r>1 or k,n are negativ + assert isinstance(k, numbers.Number), "k must be a number." + assert isinstance(n, numbers.Number), "n must be a number." + k = int(k) # k or n can be float (e.g. as result of n=k*r) + n = int(n) # k or n can be float (e.g. as result of n=k*r) + assert k>-1, "k cannot be negative." + assert n>-1, "n cannot be negative." + assert n>=k, "Invalid coderate (>1)." + # init encoder parameters + self._k = k + self._n = n + self._coderate = k / n + + ######################################### + # Public methods and properties + ######################################### + + @property + def k(self): + """Number of information bits per codeword.""" + return self._k + + @property + def n(self): + "Codeword length." + return self._n + + @property + def coderate(self): + """Coderate of the LDPC code.""" + return self._coderate + + ######################### + # Keras layer functions + ######################### + + def build(self, input_shape): + """Nothing to build.""" + pass + + def call(self, inputs): + """Encoding function that outputs the all-zero codeword. + + This function returns the all-zero codeword of shape `[..., n]`. + Note that this encoder is a dummy encoder and does NOT perform real + encoding! + + Args: + inputs (tf.float32): Tensor of arbitrary shape. + + Returns: + `tf.float32`: Tensor of shape `[...,n]`. + + Note: + This encoder is a dummy encoder that is needed for some all-zero + codeword simulations independent of the input. It does NOT perform + real encoding although the information bits are taken as input. + This is just to ensure compatibility with other encoding layers. + """ + # keep shape of first dimensions + # return an all-zero tensor of shape [..., n] + output_shape = tf.concat([tf.shape(inputs)[:-1], + tf.constant(self._n, shape=[1])], + 0) + c = tf.zeros(output_shape, dtype=super().dtype) + return c diff --git a/sionna/fec/polar/__init__.py b/sionna/fec/polar/__init__.py index 65e63cfb..42b4d11c 100644 --- a/sionna/fec/polar/__init__.py +++ b/sionna/fec/polar/__init__.py @@ -4,9 +4,6 @@ # """Polar sub-package of the Sionna library.""" -#from . import encoding -#from . import decoding -#from . import utils from .encoding import PolarEncoder, Polar5GEncoder from .decoding import Polar5GDecoder, PolarBPDecoder, PolarSCDecoder, PolarSCLDecoder from .utils import generate_5g_ranking, generate_polar_transform_mat, generate_rm_code, generate_dense_polar diff --git a/sionna/fec/polar/decoding.py b/sionna/fec/polar/decoding.py index 69d3e2ef..23752c94 100644 --- a/sionna/fec/polar/decoding.py +++ b/sionna/fec/polar/decoding.py @@ -1974,8 +1974,8 @@ def __init__(self, # Store internal attributes self._n_target = enc_polar.n_target self._k_target = enc_polar.k_target - self._n_polar = enc_polar.n - self._k_polar = enc_polar.k + self._n_polar = enc_polar.n_polar + self._k_polar = enc_polar.k_polar self._k_crc = enc_polar.enc_crc.crc_length self._llr_max = 100 # Internal max LLR value (for punctured positions) self._enc_polar = enc_polar diff --git a/sionna/fec/polar/encoding.py b/sionna/fec/polar/encoding.py index 5824da5d..57513798 100644 --- a/sionna/fec/polar/encoding.py +++ b/sionna/fec/polar/encoding.py @@ -395,6 +395,26 @@ def n_target(self): """Codeword length including rate-matching.""" return self._n_target + @property + def k_polar(self): + """Number of information bits of the underlying Polar code.""" + return self._k + + @property + def n_polar(self): + """Codeword length of the underlying Polar code.""" + return self._n + + @property + def k(self): + """Number of information bits including rate-matching.""" + return self._k_target + + @property + def n(self): + """Codeword length including rate-matching.""" + return self._n_target + def subblock_interleaving(self, u): """Input bit interleaving as defined in Sec 5.4.1.1 [3GPPTS38212]_. diff --git a/sionna/fec/turbo/decoding.py b/sionna/fec/turbo/decoding.py index 641c17bf..edcf1c5d 100644 --- a/sionna/fec/turbo/decoding.py +++ b/sionna/fec/turbo/decoding.py @@ -14,9 +14,9 @@ class TurboDecoder(Layer): # pylint: disable=line-too-long - r"""TurboDecoder(encoder=None, gen_poly=None, rate=1/3, constraint_length=None, interleaver='3GPP', terminate=False, num_iter=6, hard_out=True, output_dtype=tf.float32,**kwargs) + r"""TurboDecoder(encoder=None, gen_poly=None, rate=1/3, constraint_length=None, interleaver='3GPP', terminate=False, num_iter=6, hard_out=True, algorithm='map', output_dtype=tf.float32,**kwargs) - Decodes a noisy Turbo codeword to the information tensor [Berrou]_. + Turbo code decoder based on BCJR component decoders [Berrou]_. Takes as input LLRs and returns LLRs or hard decided bits, i.e., an estimate of the information tensor. @@ -30,10 +30,11 @@ class TurboDecoder(Layer): Parameters ---------- encoder: :class:`~sionna.fec.turbo.encoding.TurboEncoder` - If ``encoder`` is provided as input, the following parameters need not - be provided: `gen_poly`, `rate`, `constraint_length`, `terminate`, - `interleaver`. They will be inferred from the ``encoder`` object itself. - If ``encoder`` is `"None"`, the above parameters must be provided + If ``encoder`` is provided as input, the following input parameters + are not required and will be ignored: `gen_poly`, `rate`, + `constraint_length`, `terminate`, `interleaver`. They will be inferred + from the ``encoder`` object itself. + If ``encoder`` is `None`, the above parameters must be provided explicitly. gen_poly: tuple @@ -50,12 +51,12 @@ class TurboDecoder(Layer): ``encoder`` and ``gen_poly`` are `None`. interleaver: str - `"3GPP"` or `Random`. If `"3GPP"`, the internal interleaver for Turbo + `"3GPP"` or `"Random"`. If `"3GPP"`, the internal interleaver for Turbo codes as specified in [3GPPTS36212_Turbo]_ will be used. Only required - if ``encoder`` is None. + if ``encoder`` is `None`. terminate: bool - If `"True"`, the two underlying convolutional encoders are assumed + If `True`, the two underlying convolutional encoders are assumed to have terminated to all zero state. num_iter: int @@ -64,10 +65,17 @@ class TurboDecoder(Layer): convolutional code components. hard_out: boolean - Boolean flag indicating whether to output hard or soft decisions on - the decoded information vector. `"True"` implies a hard- decoded - information vector of 0/1's is output. `"False"` implies decoded LLRs - of the information is output. + Defaults to `True` and indicates whether to output hard or soft + decisions on the decoded information vector. `True` implies a hard- + decoded information vector of 0/1's is output. `False` implies + decoded LLRs of the information is output. + + algorithm: str + Defaults to `map`. Indicates the implemented BCJR algorithm, + where `map` denotes the exact MAP algorithm, `log` indicates the + exact MAP implementation, but in log-domain, and + `maxlog` indicates the approximated MAP implementation in log-domain, + where :math:`\log(e^{a}+e^{b}) \sim \max(a,b)`. output_dtype: tf.DType Defaults to `tf.float32`. Defines the output datatype of the layer. @@ -76,13 +84,13 @@ class TurboDecoder(Layer): ----- inputs: tf.float32 2+D tensor of shape `[...,n]` containing the (noisy) channel - output symbols where `n` is the codeword length. + output symbols where `n` is the codeword length Output ------ : tf.float32 - 2+D tensor of shape `[...,rate*n]` containing the estimates of the - information bit tensor. + 2+D tensor of shape `[...,coderate*n]` containing the estimates of the + information bit tensor Note ---- @@ -102,6 +110,7 @@ def __init__(self, terminate=False, num_iter=6, hard_out=True, + algorithm='map', output_dtype=tf.float32, **kwargs): @@ -181,10 +190,12 @@ def __init__(self, self._output_dtype = output_dtype self.num_iter = num_iter self._hard_out = hard_out - self.bcjrdecoder = BCJRDecoder(self._gen_poly, - rsc=self.rsc, - hard_out=False, - terminate=self._terminate) + + self.bcjrdecoder = BCJRDecoder(gen_poly=self._gen_poly, + rsc=self.rsc, + hard_out=False, + terminate=self._terminate, + algorithm=algorithm) ######################################### # Public methods and properties @@ -192,24 +203,40 @@ def __init__(self, @property def gen_poly(self): - """The generator polynomial used by the encoder.""" + """Generator polynomial used by the encoder""" return self._gen_poly @property def constraint_length(self): - """The constraint length of the encoder.""" + """Constraint length of the encoder""" return self._mu + 1 @property def coderate(self): - """Rate of the code used in the encoder.""" + """Rate of the code used in the encoder""" return self._coderate @property def trellis(self): - """Trellis object used during encoding.""" + """Trellis object used during encoding""" return self._trellis + @property + def k(self): + """Number of information bits per codeword""" + if self._k is None: + print("Note: The value of k cannot be computed before the first " \ + "call().") + return self._k + + @property + def n(self): + """Number of codeword bits""" + if self._n is None: + print("Note: The value of n cannot be computed before the first " \ + "call().") + return self._n + ######################### # Utility functions ######################### @@ -284,9 +311,14 @@ def build(self, input_shape): tf.debugging.assert_greater_equal(len(input_shape), 2) self._n = input_shape[-1] + if self.coderate == 1/2: + assert self._n%2 == 0, "Codeword length should be a multiple of 2" - turbo_n = int(self._n * self.coderate * 3) + codefactor = self.coderate * 3 + turbo_n = int(self._n * codefactor) turbo_n_preterm = turbo_n - self._num_term_bits + assert turbo_n_preterm%3 == 0, "Invalid codeword length for a terminated Turbo code" + self._k = int(turbo_n_preterm/3) # num of symbols for the convolutional codes. diff --git a/sionna/fec/turbo/encoding.py b/sionna/fec/turbo/encoding.py index 462bfbb7..3d2f5856 100644 --- a/sionna/fec/turbo/encoding.py +++ b/sionna/fec/turbo/encoding.py @@ -9,19 +9,19 @@ from tensorflow.keras.layers import Layer from sionna.fec import interleaving from sionna.fec.utils import bin2int_tf, int2bin_tf +from sionna.fec.conv.encoding import ConvEncoder from sionna.fec.conv.utils import Trellis from sionna.fec.turbo.utils import polynomial_selector, puncture_pattern, TurboTermination - class TurboEncoder(Layer): # pylint: disable=line-too-long r"""TurboEncoder(gen_poly=None, constraint_length=3, rate=1/3, terminate=False, interleaver_type='3GPP', output_dtype=tf.float32, **kwargs) - Encodes a binary information tensor to a Turbo codeword [Berrou]_. + Performs encoding of information bits to a Turbo code codeword [Berrou]_. Implements the standard Turbo code framework [Berrou]_: Two identical rate-1/2 convolutional encoders :class:`~sionna.fec.conv.encoding.ConvEncoder` - are combined to produce a rate-1/3 Turbo code. Further, puncturing to attain a - rate-1/2 Turbo code is supported. + are combined to produce a rate-1/3 Turbo code. Further, + puncturing to attain a rate-1/2 Turbo code is supported. The class inherits from the Keras layer class and can be used as layer in a Keras model. @@ -29,7 +29,7 @@ class TurboEncoder(Layer): Parameters ---------- gen_poly: tuple - Sequence of strings with each string being a 0,1 sequence. If + Tuple of strings with each string being a 0,1 sequence. If `None`, ``constraint_length`` must be provided. constraint_length: int @@ -38,12 +38,12 @@ class TurboEncoder(Layer): rate: float Valid values are 1/3 and 1/2. Note that ``rate`` here denotes - the `design` rate of the Turbo code. If ``terminate`` is `"True"`, a + the `design` rate of the Turbo code. If ``terminate`` is `True`, a small rate-loss occurs. terminate: boolean Underlying convolutional encoders are terminated to all zero state - if `"True"`. If terminated, the true rate of the code is slightly lower + if `True`. If terminated, the true rate of the code is slightly lower than ``rate``. interleaver_type: str @@ -59,22 +59,22 @@ class TurboEncoder(Layer): Input ----- inputs : [...,k], tf.float32 - 2+D tensor of information bits where `k` is the information length. + 2+D tensor of information bits where `k` is the information length Output ------ : `[...,k/rate]`, tf.float32 2+D tensor where `rate` is provided as input parameter. The output is the encoded codeword for the input - information tensor. When `terminate` is `"True"`, the effective rate - of the Turbo code is slightly less than `rate`. + information tensor. When ``terminate`` is `True`, the effective rate + of the Turbo code is slightly less than ``rate``. Note ---- Various notations are used in literature to represent the generator polynomials for convolutional codes. For simplicity :class:`~sionna.fec.turbo.encoding.TurboEncoder` only - accepts the binary format, i.e., `10011` for the ``gen_poly`` argument + accepts the binary format, i.e., `10011`, for the ``gen_poly`` argument which corresponds to the polynomial :math:`1 + D^3 + D^4`. Note that Turbo codes require the underlying convolutional encoders @@ -88,11 +88,11 @@ class TurboEncoder(Layer): `10011` has a ``constraint_length`` of 5, however its ``memory`` is only 4. - When ``terminate`` is `"True"`, the true rate of the Turbo code is + When ``terminate`` is `True`, the true rate of the Turbo code is slightly lower than ``rate``. It can be computed as :math:`\frac{k}{\frac{k}{r}+\frac{4\mu}{3r}}` where `r` denotes ``rate`` and :math:`\mu` is the ``constraint_length`` - 1. For example, in - 3GPP, ``constraint_length`` = 4, ``terminate`` = `"True"`, for + 3GPP, ``constraint_length`` = 4, ``terminate`` = `True`, for ``rate`` = 1/3, true rate is equal to :math:`\frac{k}{3k+12}` . """ @@ -135,11 +135,13 @@ def __init__(self, self._terminate = terminate self._interleaver_type = interleaver_type self.output_dtype = output_dtype + # Underlying convolutional encoders to be rsc or not + rsc = True self._coderate_conv = 1/len(self.gen_poly) self._punct_pattern = puncture_pattern(rate, self._coderate_conv) - self._trellis = Trellis(self.gen_poly, rsc=True) + self._trellis = Trellis(self.gen_poly, rsc=rsc) self._mu = self.trellis._mu # conv_n denotes number of output bits for conv_k input bits. @@ -167,23 +169,27 @@ def __init__(self, if self.punct_pattern is not None: self.punct_idx = tf.where(self.punct_pattern) + self.convencoder = ConvEncoder(gen_poly=self._gen_poly, + rsc=rsc, + terminate=self._terminate) + ######################################### # Public methods and properties ######################################### @property def gen_poly(self): - """The generator polynomial used by the encoder.""" + """Generator polynomial used by the encoder""" return self._gen_poly @property def constraint_length(self): - """The constraint length of the encoder.""" + """Constraint length of the encoder""" return self._mu + 1 @property def coderate(self): - """Rate of the code used in the encoder.""" + """Rate of the code used in the encoder""" if self.terminate and self._k is None: print("Note that, due to termination, the true coderate is lower "\ "than the returned design rate. "\ @@ -196,19 +202,35 @@ def coderate(self): @property def trellis(self): - """Trellis object used during encoding.""" + """Trellis object used during encoding""" return self._trellis @property def terminate(self): - """Indicates if the convolutional encoders are terminated.""" + """Indicates if the convolutional encoders are terminated""" return self._terminate @property def punct_pattern(self): - """Puncturing pattern for the Turbo codeword.""" + """Puncturing pattern for the Turbo codeword""" return self._punct_pattern + @property + def k(self): + """Number of information bits per codeword""" + if self._k is None: + print("Note: The value of k cannot be computed before the first " \ + "call().") + return self._k + + @property + def n(self): + """Number of codeword bits""" + if self._n is None: + print("Note: The value of n cannot be computed before the first " \ + "call().") + return self._n + def _conv_enc(self, info_vec, terminate): """ This method encodes the information tensor info_vec using the @@ -352,43 +374,41 @@ def call(self, inputs): if self._terminate: num_term_bits_ = int( - self.turbo_term.get_num_term_syms()/self._coderate_desired) + self.turbo_term.get_num_term_syms()/self._coderate_conv) + num_term_bits_punct = int( + num_term_bits_*self._coderate_conv/self._coderate_desired) else: num_term_bits_ = 0 + num_term_bits_punct = 0 output_shape = inputs.get_shape().as_list() output_shape[0] = -1 - output_shape[-1] = self._n + num_term_bits_ + output_shape[-1] = self._n + num_term_bits_punct + preterm_n = int(self._k/self._coderate_conv) msg = tf.cast(tf.reshape(inputs, [-1, self._k]), tf.int32) msg2 = self.internal_interleaver(msg) - ta1, ta1_term = self._conv_enc(msg, terminate=self._terminate) - ta2, ta2_term = self._conv_enc(msg2, terminate=self._terminate) + cw1_ = self.convencoder(msg) + cw2_ = self.convencoder(msg2) - cw1 = tf.concat(tf.unstack(ta1.stack()),axis=1) - cw2 = tf.concat(tf.unstack(ta2.stack()),axis=1) + cw1, term1 = cw1_[:, :preterm_n], cw1_[:, preterm_n:] + cw2, term2 = cw2_[:, :preterm_n], cw2_[:, preterm_n:] # Gather parity stream from 2nd enc - parity_idx = tf.range(1, - int(self._k/self._coderate_conv), - delta=self._conv_n) - cw2_parity = tf.gather(cw2, indices=parity_idx, axis=-1) - - # Concatenate to _conv_n streams from first encoder - cw = tf.concat([tf.reshape(cw1[:,:,None],(-1, self._k, self._conv_n)), - cw2_parity[:,:,None]], - axis=-1) + par_idx = tf.range(1, preterm_n, delta=self._conv_n) + cw2_par = tf.gather(cw2, indices=par_idx, axis=-1) - if self.terminate: - term_bits1 = tf.concat(tf.unstack(ta1_term.stack()), axis=1) - term_bits2 = tf.concat(tf.unstack(ta2_term.stack()), axis=1) + cw1 = tf.reshape(cw1,(-1, self._k, self._conv_n)) + cw2_par = tf.reshape(cw2_par, (-1, self._k, 1)) - term_syms_turbo = self.turbo_term.termbits_conv2turbo(term_bits1, - term_bits2) + # Concatenate 2nd enc parity to _conv_n streams from first encoder + cw = tf.concat([cw1, cw2_par], axis=-1) - term_syms_turbo = tf.reshape(term_syms_turbo, - (-1, tf.shape(term_syms_turbo)[-1]/3, 3)) + if self.terminate: + term_syms_turbo = self.turbo_term.termbits_conv2turbo(term1, term2) + term_syms_turbo = tf.reshape( + term_syms_turbo, (-1, num_term_bits_//2, 3)) cw = tf.concat([cw, term_syms_turbo], axis=-2) if self.punct_pattern is not None: diff --git a/sionna/fec/turbo/utils.py b/sionna/fec/turbo/utils.py index e5286ba1..e9220236 100644 --- a/sionna/fec/turbo/utils.py +++ b/sionna/fec/turbo/utils.py @@ -57,7 +57,7 @@ def puncture_pattern(turbo_coderate, conv_coderate): Desired coderate of the Turbo code conv_coderate: float - Coderate of the underlying convolutional encoder. + Coderate of the underlying convolutional encoder Output ------ @@ -91,13 +91,13 @@ class TurboTermination(object): conv_n: int Number of output bits for one state transition in the underlying - convolutional encoder. + convolutional encoder num_conv_encs: int - Number of parallel convolutional encoders used in the Turbo code. + Number of parallel convolutional encoders used in the Turbo code num_bit_streams: int - Number of output bit streams from Turbo code. + Number of output bit streams from Turbo code """ def __init__(self, @@ -186,10 +186,10 @@ def termbits_conv2turbo(self, term_bits1, term_bits2): Input ----- term_bits1: tf.int32 - 2+D Tensor containing termination bits from convolutional encoder 1. + 2+D Tensor containing termination bits from convolutional encoder 1 term_bits2: tf.int32 - 2+D Tensor containing termination bits from convolutional encoder 2. + 2+D Tensor containing termination bits from convolutional encoder 2 Output ------ @@ -209,7 +209,7 @@ def termbits_conv2turbo(self, term_bits1, term_bits2): tf.constant(extra_bits)], axis=0) term_bits = tf.concat( - [term_bits, tf.zeros(zer_shape, tf.int32)], axis=-1) + [term_bits, tf.zeros(zer_shape, tf.float32)], axis=-1) return term_bits def term_bits_turbo2conv(self, term_bits): @@ -256,13 +256,13 @@ def term_bits_turbo2conv(self, term_bits): ----- term_bits: tf.float32 Channel output of the Turbo codeword, corresponding to the - termination part. + termination part Output ------ : tf.float32 Two tensors of channel outputs, corresponding to encoders 1 and 2, - respectively. + respectively """ input_len = tf.shape(term_bits)[-1] divisible = tf.math.floormod(input_len, self.num_bitstreams) diff --git a/sionna/fec/utils.py b/sionna/fec/utils.py index 74f204b9..0ced8dde 100644 --- a/sionna/fec/utils.py +++ b/sionna/fec/utils.py @@ -13,7 +13,6 @@ from sionna.fec.ldpc import codes from sionna.utils.misc import log2 - class GaussianPriorSource(Layer): r"""GaussianPriorSource(specified_by_mi=False, dtype=tf.float32, **kwargs) @@ -26,8 +25,6 @@ class GaussianPriorSource(Layer): .. image:: ../figures/GaussianPriorSource.png - - The generated LLRs are drawn from a Gaussian distribution with .. math:: @@ -1225,141 +1222,6 @@ def verify_gm_pcm(gm, pcm): s = np.mod(np.matmul(pcm, np.transpose(gm)), 2) # mod2 to account for GF(2) return np.sum(s)==0 # Check for Non-zero syndrom of H*G' -class LinearEncoder(Layer): - # pylint: disable=line-too-long - r"""LinearEncoder(enc_mat, is_pcm=False, dtype=tf.float32, **kwargs) - - Linear binary encoder for a given encoding matrix ``enc_mat``. - - If ``is_pcm`` is True, ``enc_mat`` is interpreted as parity-check - matrix and internally converted to a corresponding generator matrix. - - The class inherits from the Keras layer class and can be used as layer in a - Keras model. - - Parameters - ---------- - enc_mat : [k, n] or [n-k, n], ndarray - Binary generator matrix of shape `[k, n]`. If ``is_pcm`` is - True, ``enc_mat`` is interpreted as parity-check matrix of shape - `[n-k, n]`. - - dtype: tf.DType - Defaults to `tf.float32`. Defines the datatype for the output dtype. - - Input - ----- - inputs: [...,k], tf.float32 - 2+D tensor containing information bits. - - Output - ------ - : [...,n], tf.float32 - 2+D tensor containing codewords with same shape as inputs, except the - last dimension changes to `[...,n]`. - - Raises - ------ - AssertionError - If the encoding matrix is not a valid binary 2-D matrix. - - Note - ---- - If ``is_pcm`` is True, this layer uses - :class:`~sionna.fec.utils.pcm2gm` to find the generator matrix for - encoding. Please note that this imposes a few constraints on the - provided parity-check matrix such as full rank and it must be binary. - - Note that this encoder is generic for all binary linear block codes - and, thus, cannot implement any code specifc optimizations. As a - result, the encoding complexity is :math:`O(k^2)`. Please consider code - specific encoders such as the - :class:`~sionna.fec.polar.encoding.Polar5GEncoder` or - :class:`~sionna.fec.ldpc.encoding.LDPC5GEncoder` for an improved - encoding performance. - """ - - def __init__(self, - enc_mat, - is_pcm=False, - dtype=tf.float32, - **kwargs): - - super().__init__(dtype=dtype, **kwargs) - - # tf.int8 currently not supported by tf.matmult - assert (dtype in - (tf.float16, tf.float32, tf.float64, tf.int32, tf.int64)), \ - "Unsupported dtype." - - # check input values for consistency - assert isinstance(is_pcm, bool), \ - 'is_parity_check must be bool.' - - # verify that enc_mat is binary - assert ((enc_mat==0) | (enc_mat==1)).all(), "enc_mat is not binary." - assert (len(enc_mat.shape)==2), "enc_mat must be 2-D array." - - # in case parity-check matrix is provided, convert to generator matrix - if is_pcm: - self._gm = pcm2gm(enc_mat, verify_results=True) - else: - self._gm = enc_mat - - self._k = self._gm.shape[0] - self._n = self._gm.shape[1] - self._coderate = self._k / self._n - - assert (self._k<=self._n), "Invalid matrix dimensions." - - self._gm = tf.cast(self._gm, dtype=self.dtype) - - ######################################### - # Public methods and properties - ######################################### - - @property - def k(self): - """Number of information bits per codeword.""" - return self._k - - @property - def n(self): - "Codeword length." - return self._n - - @property - def gm(self): - "Generator matrix used for encoding." - return self._gm - - @property - def coderate(self): - """Coderate of the code.""" - return self._coderate - - ######################### - # Keras layer functions - ######################### - - def build(self, input_shape): - """Nothing to build, but check for valid shapes.""" - assert input_shape[-1]==self._k, "Invalid input shape." - assert (len(input_shape)>=2), 'The inputs must have at least rank 2.' - - def call(self, inputs): - """Generic encoding function based on generator matrix multiplication. - """ - - c = tf.linalg.matmul(inputs, self._gm) - - # faster implementation of tf.math.mod(c, 2) - c_uint8 = tf.cast(c, tf.uint8) - c_bin = tf.bitwise.bitwise_and(c_uint8, tf.constant(1, tf.uint8)) - c = tf.cast(c_bin, self.dtype) - - return c - def generate_reg_ldpc(v, c, n, allow_flex_len=True, verbose=True): r"""Generate random regular (v,c) LDPC codes. @@ -1570,3 +1432,43 @@ def generate_prng_seq(length, n_rnti=0, n_id=0, c_init=None): c[idx] = np.mod(x1[idx+n_c] + x2[idx+n_c], 2) return c + + +def int_mod_2(x): + r"""Efficient implementation of modulo 2 operation for integer inputs. + + This function assumes integer inputs or implicitly casts to int. + + Remark: the function `tf.math.mod(x, 2)` is placed on the CPU and, thus, + causes unnecessary memory copies. + + Parameters + ---------- + x: tf.Tensor + Tensor to which the modulo 2 operation is applied. + + """ + + x_int8 = tf.cast(x, tf.int8) + y_int8 = tf.bitwise.bitwise_and(x_int8, tf.constant(1, tf.int8)) + return tf.cast(y_int8, x.dtype) + +########################################################### +# Deprecated aliases that will not be included in the next +# major release +########################################################### + +# ignore invalid name as this is required for legacy reasons +# pylint: disable=C0103 +def LinearEncoder(enc_mat, + is_pcm=False, + dtype=tf.float32, + **kwargs): + # import here as circular import is generated otherwise + from sionna.fec.linear import LinearEncoder as LE # pylint: disable=C0415 + print("Warning: The alias fec.utils.LinearEncoder will not be included in "\ + "Sionna 1.0. Please use fec.linear.LinearEncoder instead.") + return LE(enc_mat=enc_mat, + is_pcm=is_pcm, + dtype=dtype, + **kwargs) diff --git a/sionna/mapping.py b/sionna/mapping.py index 32da2b8e..ba263525 100644 --- a/sionna/mapping.py +++ b/sionna/mapping.py @@ -534,14 +534,14 @@ def call(self, inputs): else: return x -class SymbolLogits2LLRsWithPrior(Layer): +class SymbolLogits2LLRs(Layer): # pylint: disable=line-too-long r""" - SymbolLogits2LLRsWithPrior(method, num_bits_per_symbol, hard_out=False, dtype=tf.float32, **kwargs) + SymbolLogits2LLRs(method, num_bits_per_symbol, hard_out=False, with_prior=False, dtype=tf.float32, **kwargs) Computes log-likelihood ratios (LLRs) or hard-decisions on bits - from a tensor of logits (i.e., unnormalized log-probabilities) on constellation points, - assuming prior knowledge on the bits is available. + from a tensor of logits (i.e., unnormalized log-probabilities) on constellation points. + If the flag ``with_prior`` is set, prior knowledge on the bits is assumed to be available. Parameters ---------- @@ -555,13 +555,18 @@ class SymbolLogits2LLRsWithPrior(Layer): If `True`, the layer provides hard-decided bits instead of soft-values. Defaults to `False`. + with_prior : bool + If `True`, it is assumed that prior knowledge on the bits is available. + This prior information is given as LLRs as an additional input to the layer. + Defaults to `False`. + dtype : One of [tf.float32, tf.float64] tf.DType (dtype) The dtype for the input and output. Defaults to `tf.float32`. Input ----- - (logits, prior) : + logits or (logits, prior): Tuple: logits : [...,n, num_points], tf.float @@ -572,6 +577,7 @@ class SymbolLogits2LLRsWithPrior(Layer): It can be provided either as a tensor of shape `[num_bits_per_symbol]` for the entire input batch, or as a tensor that is "broadcastable" to `[..., n, num_bits_per_symbol]`. + Only required if the ``with_prior`` flag is set. Output ------ @@ -596,8 +602,8 @@ class SymbolLogits2LLRsWithPrior(Layer): sets of :math:`2^K` constellation points for which the :math:`i\text{th}` bit is equal to 1 and 0, respectively. :math:`\mathbf{z} = \left[z_{c_0},\dots,z_{c_{2^K-1}}\right]` is the vector of logits on the constellation points, :math:`\mathbf{p} = \left[p_0,\dots,p_{K-1}\right]` is the vector of LLRs that serves as prior knowledge on the :math:`K` bits that are mapped to - a constellation point, and :math:`\Pr(c\lvert\mathbf{p})` is the prior probability on the constellation symbol - :math:`c`: + a constellation point and is set to :math:`\mathbf{0}` if no prior knowledge is assumed to be available, + and :math:`\Pr(c\lvert\mathbf{p})` is the prior probability on the constellation symbol :math:`c`: .. math:: \Pr\left(c\lvert\mathbf{p}\right) = \prod_{k=0}^{K-1} \Pr\left(b_k = \ell(c)_k \lvert\mathbf{p} \right) @@ -629,6 +635,7 @@ def __init__(self, method, num_bits_per_symbol, hard_out=False, + with_prior=False, dtype=tf.float32, **kwargs): super().__init__(dtype=dtype, **kwargs) @@ -636,6 +643,7 @@ def __init__(self, self._method = method self._hard_out = hard_out self._num_bits_per_symbol = num_bits_per_symbol + self._with_prior = with_prior num_points = int(2**num_bits_per_symbol) # Array composed of binary representations of all symbols indices @@ -653,10 +661,11 @@ def __init__(self, self._c0 = tf.constant(c0, dtype=tf.int32) # Symbols with ith bit=0 self._c1 = tf.constant(c1, dtype=tf.int32) # Symbols with ith bit=1 - # Array of labels from {-1, 1} of all symbols - # [num_points, num_bits_per_symbol] - a = 2*a-1 - self._a = tf.constant(a, dtype=dtype) + if with_prior: + # Array of labels from {-1, 1} of all symbols + # [num_points, num_bits_per_symbol] + a = 2*a-1 + self._a = tf.constant(a, dtype=dtype) # Determine the reduce function for LLR computation if self._method == "app": @@ -669,7 +678,10 @@ def num_bits_per_symbol(self): return self._num_bits_per_symbol def call(self, inputs): - logits, prior = inputs + if self._with_prior: + logits, prior = inputs + else: + logits = inputs # Compute exponents exponents = logits @@ -679,41 +691,50 @@ def call(self, inputs): exp0 = tf.gather(exponents, self._c0, axis=-1, batch_dims=0) exp1 = tf.gather(exponents, self._c1, axis=-1, batch_dims=0) - # Expanding `prior` such that it is broadcastable with - # shape [..., n or 1, 1, num_bits_per_symbol] - prior = sn.utils.expand_to_rank(prior, tf.rank(logits), axis=0) - prior = tf.expand_dims(prior, axis=-2) + # Process the prior information + if self._with_prior: + # Expanding `prior` such that it is broadcastable with + # shape [..., n or 1, 1, num_bits_per_symbol] + prior = sn.utils.expand_to_rank(prior, tf.rank(logits), axis=0) + prior = tf.expand_dims(prior, axis=-2) - # Expand the symbol labeling to be broadcastable with prior - # shape [..., 1, num_points, num_bits_per_symbol] - a = sn.utils.expand_to_rank(self._a, tf.rank(prior), axis=0) + # Expand the symbol labeling to be broadcastable with prior + # shape [..., 1, num_points, num_bits_per_symbol] + a = sn.utils.expand_to_rank(self._a, tf.rank(prior), axis=0) - # Compute the prior probabilities on symbols exponents - # shape [..., n or 1, num_points] - exp_ps = tf.reduce_sum(tf.math.log_sigmoid(a*prior), axis=-1) + # Compute the prior probabilities on symbols exponents + # shape [..., n or 1, num_points] + exp_ps = tf.reduce_sum(tf.math.log_sigmoid(a*prior), axis=-1) - # Gather prior probability symbol for all bits - # shape [..., n or 1, num_points/2, num_bits_per_symbol] - exp_ps0 = tf.gather(exp_ps, self._c0, axis=-1) - exp_ps1 = tf.gather(exp_ps, self._c1, axis=-1) + # Gather prior probability symbol for all bits + # shape [..., n or 1, num_points/2, num_bits_per_symbol] + exp_ps0 = tf.gather(exp_ps, self._c0, axis=-1) + exp_ps1 = tf.gather(exp_ps, self._c1, axis=-1) # Compute LLRs using the definition log( Pr(b=1)/Pr(b=0) ) # shape [..., n, num_bits_per_symbol] - llr = self._reduce(exp_ps1 + exp1, axis=-2)\ - - self._reduce(exp_ps0 + exp0, axis=-2) + if self._with_prior: + llr = self._reduce(exp_ps1 + exp1, axis=-2)\ + - self._reduce(exp_ps0 + exp0, axis=-2) + else: + llr = self._reduce(exp1, axis=-2) - self._reduce(exp0, axis=-2) if self._hard_out: return sn.utils.hard_decisions(llr) else: return llr -class SymbolLogits2LLRs(SymbolLogits2LLRsWithPrior): +class SymbolLogits2LLRsWithPrior(SymbolLogits2LLRs): # pylint: disable=line-too-long r""" - SymbolLogits2LLRs(method, num_bits_per_symbol, hard_out=False, dtype=tf.float32, **kwargs) + SymbolLogits2LLRsWithPrior(method, num_bits_per_symbol, hard_out=False, dtype=tf.float32, **kwargs) Computes log-likelihood ratios (LLRs) or hard-decisions on bits - from a tensor of logits (i.e., unnormalized log-probabilities) on constellation points. + from a tensor of logits (i.e., unnormalized log-probabilities) on constellation points, + assuming that prior knowledge on the bits is available. + + This class is deprecated as the functionality has been integrated + into :class:`~sionna.mapping.SymbolLogits2LLRs`. Parameters ---------- @@ -733,9 +754,18 @@ class SymbolLogits2LLRs(SymbolLogits2LLRsWithPrior): Input ----- + (logits, prior): + Tuple: + logits : [...,n, num_points], tf.float Logits on constellation points. + prior : [num_bits_per_symbol] or [...n, num_bits_per_symbol], tf.float + Prior for every bit as LLRs. + It can be provided either as a tensor of shape `[num_bits_per_symbol]` for the + entire input batch, or as a tensor that is "broadcastable" + to `[..., n, num_bits_per_symbol]`. + Output ------ : [...,n, num_bits_per_symbol], tf.float @@ -747,17 +777,28 @@ class SymbolLogits2LLRs(SymbolLogits2LLRsWithPrior): is computed according to .. math:: - LLR(i) = \ln\left(\frac{\Pr\left(b_i=1\lvert y\right)}{\Pr\left(b_i=0\lvert y\right)}\right) =\ln\left(\frac{ - \sum_{c\in\mathcal{C}_{i,1}} - e^{z_c} + LLR(i) = \ln\left(\frac{\Pr\left(b_i=1\lvert \mathbf{z},\mathbf{p}\right)}{\Pr\left(b_i=0\lvert \mathbf{z},\mathbf{p}\right)}\right) =\ln\left(\frac{ + \sum_{c\in\mathcal{C}_{i,1}} \Pr\left(c\lvert\mathbf{p}\right) + e^{z_c} }{ - \sum_{c\in\mathcal{C}_{i,0}} - e^{z_c} + \sum_{c\in\mathcal{C}_{i,0}} \Pr\left(c\lvert\mathbf{p}\right) + e^{z_c} }\right) where :math:`\mathcal{C}_{i,1}` and :math:`\mathcal{C}_{i,0}` are the sets of :math:`2^K` constellation points for which the :math:`i\text{th}` bit is - equal to 1 and 0, respectively. :math:`\mathbf{z} = \left[z_{c_0},\dots,z_{c_{2^K-1}}\right]` is the vector of logits on the constellation points. The definition of the LLR has been + equal to 1 and 0, respectively. :math:`\mathbf{z} = \left[z_{c_0},\dots,z_{c_{2^K-1}}\right]` is the vector of logits on the constellation points, :math:`\mathbf{p} = \left[p_0,\dots,p_{K-1}\right]` + is the vector of LLRs that serves as prior knowledge on the :math:`K` bits that are mapped to + a constellation point, + and :math:`\Pr(c\lvert\mathbf{p})` is the prior probability on the constellation symbol :math:`c`: + + .. math:: + \Pr\left(c\lvert\mathbf{p}\right) = \prod_{k=0}^{K-1} \Pr\left(b_k = \ell(c)_k \lvert\mathbf{p} \right) + = \prod_{k=0}^{K-1} \text{sigmoid}\left(p_k \ell(c)_k\right) + + where :math:`\ell(c)_k` is the :math:`k^{th}` bit label of :math:`c`, where 0 is + replaced by -1. + The definition of the LLR has been chosen such that it is equivalent with that of logits. This is different from many textbooks in communications, where the LLR is defined as :math:`LLR(i) = \ln\left(\frac{\Pr\left(b_i=0\lvert y\right)}{\Pr\left(b_i=1\lvert y\right)}\right)`. @@ -766,34 +807,38 @@ class SymbolLogits2LLRs(SymbolLogits2LLRsWithPrior): are approximated like .. math:: + \begin{align} LLR(i) &\approx\ln\left(\frac{ - \max_{c\in\mathcal{C}_{i,1}} + \max_{c\in\mathcal{C}_{i,1}} \Pr\left(c\lvert\mathbf{p}\right) e^{z_c} }{ - \max_{c\in\mathcal{C}_{i,0}} + \max_{c\in\mathcal{C}_{i,0}} \Pr\left(c\lvert\mathbf{p}\right) e^{z_c} - }\right)\\ - &= \max_{c\in\mathcal{C}_{i,1}} z_c - - \max_{c\in\mathcal{C}_{i,0}} z_c + }\right) . + \end{align} """ - def call(self, inputs): - logits = inputs - - # Settings all priors to 0 - num_bits_per_symbol = self.num_bits_per_symbol - null_prior = tf.zeros([num_bits_per_symbol], logits.dtype) - - return super().call([logits, null_prior]) - -class DemapperWithPrior(Layer): + def __init__(self, + method, + num_bits_per_symbol, + hard_out=False, + dtype=tf.float32, + **kwargs): + super().__init__(method=method, + num_bits_per_symbol=num_bits_per_symbol, + hard_out=False, + with_prior=True, + dtype=tf.float32, + **kwargs) + +class Demapper(Layer): # pylint: disable=line-too-long r""" - DemapperWithPrior(demapping_method, constellation_type=None, num_bits_per_symbol=None, constellation=None, hard_out=False, dtype=tf.complex64, **kwargs) + Demapper(demapping_method, constellation_type=None, num_bits_per_symbol=None, constellation=None, hard_out=False, with_prior=False, dtype=tf.complex64, **kwargs) Computes log-likelihood ratios (LLRs) or hard-decisions on bits - for a tensor of received symbols, assuming prior - knowledge on the bits is available. + for a tensor of received symbols. + If the flag ``with_prior`` is set, prior knowledge on the bits is assumed to be available. This class defines a layer implementing different demapping functions. All demapping functions are fully differentiable when soft-decisions @@ -821,13 +866,18 @@ class DemapperWithPrior(Layer): If `True`, the demapper provides hard-decided bits instead of soft-values. Defaults to `False`. + with_prior : bool + If `True`, it is assumed that prior knowledge on the bits is available. + This prior information is given as LLRs as an additional input to the layer. + Defaults to `False`. + dtype : One of [tf.complex64, tf.complex128] tf.DType (dtype) The dtype of `y`. Defaults to tf.complex64. The output dtype is the corresponding real dtype (tf.float32 or tf.float64). Input ----- - (y, prior, no) : + (y,no) or (y, prior, no) : Tuple: y : [...,n], tf.complex @@ -838,6 +888,7 @@ class DemapperWithPrior(Layer): It can be provided either as a tensor of shape `[num_bits_per_symbol]` for the entire input batch, or as a tensor that is "broadcastable" to `[..., n, num_bits_per_symbol]`. + Only required if the ``with_prior`` flag is set. no : Scalar or [...,n], tf.float The noise variance estimate. It can be provided either as scalar @@ -867,8 +918,8 @@ class DemapperWithPrior(Layer): sets of constellation points for which the :math:`i\text{th}` bit is equal to 1 and 0, respectively. :math:`\mathbf{p} = \left[p_0,\dots,p_{K-1}\right]` is the vector of LLRs that serves as prior knowledge on the :math:`K` bits that are mapped to - a constellation point, and :math:`\Pr(c\lvert\mathbf{p})` is the prior probability on the constellation symbol - :math:`c`: + a constellation point and is set to :math:`\mathbf{0}` if no prior knowledge is assumed to be available, + and :math:`\Pr(c\lvert\mathbf{p})` is the prior probability on the constellation symbol :math:`c`: .. math:: \Pr\left(c\lvert\mathbf{p}\right) = \prod_{k=0}^{K-1} \text{sigmoid}\left(p_k \ell(c)_k\right) @@ -904,9 +955,11 @@ def __init__(self, num_bits_per_symbol=None, constellation=None, hard_out=False, + with_prior=False, dtype=tf.complex64, **kwargs): super().__init__(dtype=dtype, **kwargs) + self._with_prior = with_prior # Create constellation object @@ -917,18 +970,22 @@ def __init__(self, dtype=dtype) num_bits_per_symbol = self._constellation.num_bits_per_symbol - self._logits2llrs = SymbolLogits2LLRsWithPrior( demapping_method, - num_bits_per_symbol, - hard_out, - dtype.real_dtype, - **kwargs) + self._logits2llrs = SymbolLogits2LLRs(demapping_method, + num_bits_per_symbol, + hard_out, + with_prior, + dtype.real_dtype, + **kwargs) @property def constellation(self): return self._constellation def call(self, inputs): - y, prior, no = inputs + if self._with_prior: + y, prior, no = inputs + else: + y, no = inputs # Reshape constellation points to [1,...1,num_points] points_shape = [1]*y.shape.rank + self.constellation.points.shape @@ -945,7 +1002,10 @@ def call(self, inputs): # Compute exponents exponents = -squared_dist/no - llr = self._logits2llrs([exponents, prior]) + if self._with_prior: + llr = self._logits2llrs([exponents, prior]) + else: + llr = self._logits2llrs(exponents) # Reshape LLRs to [...,n*num_bits_per_symbol] out_shape = tf.concat([tf.shape(y)[:-1], @@ -955,18 +1015,21 @@ def call(self, inputs): return llr_reshaped -class Demapper(DemapperWithPrior): +class DemapperWithPrior(Demapper): # pylint: disable=line-too-long r""" - Demapper(demapping_method, constellation_type=None, num_bits_per_symbol=None, constellation=None, hard_out=False, dtype=tf.complex64, **kwargs) + DemapperWithPrior(demapping_method, constellation_type=None, num_bits_per_symbol=None, constellation=None, hard_out=False, dtype=tf.complex64, **kwargs) Computes log-likelihood ratios (LLRs) or hard-decisions on bits - for a tensor of received symbols. + for a tensor of received symbols, assuming that prior knowledge on the bits is available. This class defines a layer implementing different demapping functions. All demapping functions are fully differentiable when soft-decisions are computed. + This class is deprecated as the functionality has been integrated + into :class:`~sionna.mapping.Demapper`. + Parameters ---------- demapping_method : One of ["app", "maxlog"], str @@ -995,12 +1058,18 @@ class Demapper(DemapperWithPrior): Input ----- - (y, no) : + (y, prior, no) : Tuple: y : [...,n], tf.complex The received symbols. + prior : [num_bits_per_symbol] or [...,num_bits_per_symbol], tf.float + Prior for every bit as LLRs. + It can be provided either as a tensor of shape `[num_bits_per_symbol]` for the + entire input batch, or as a tensor that is "broadcastable" + to `[..., n, num_bits_per_symbol]`. + no : Scalar or [...,n], tf.float The noise variance estimate. It can be provided either as scalar for the entire input batch or as a tensor that is "broadcastable" to @@ -1017,19 +1086,27 @@ class Demapper(DemapperWithPrior): is computed according to .. math:: - LLR(i) = \ln\left(\frac{\Pr\left(b_i=1\lvert y\right)}{\Pr\left(b_i=0\lvert y\right)}\right) =\ln\left(\frac{ - \sum_{c\in\mathcal{C}_{i,1}} \exp\left( - -\frac{1}{N_o}\left|y-c\right|^2 - \right) + LLR(i) = \ln\left(\frac{\Pr\left(b_i=1\lvert y,\mathbf{p}\right)}{\Pr\left(b_i=0\lvert y,\mathbf{p}\right)}\right) =\ln\left(\frac{ + \sum_{c\in\mathcal{C}_{i,1}} \Pr\left(c\lvert\mathbf{p}\right) + \exp\left(-\frac{1}{N_o}\left|y-c\right|^2\right) }{ - \sum_{c\in\mathcal{C}_{i,0}} \exp\left( - -\frac{1}{N_o}\left|y-c\right|^2 - \right) + \sum_{c\in\mathcal{C}_{i,0}} \Pr\left(c\lvert\mathbf{p}\right) + \exp\left(-\frac{1}{N_o}\left|y-c\right|^2\right) }\right) where :math:`\mathcal{C}_{i,1}` and :math:`\mathcal{C}_{i,0}` are the sets of constellation points for which the :math:`i\text{th}` bit is - equal to 1 and 0, respectively. The definition of the LLR has been + equal to 1 and 0, respectively. :math:`\mathbf{p} = \left[p_0,\dots,p_{K-1}\right]` + is the vector of LLRs that serves as prior knowledge on the :math:`K` bits that are mapped to + a constellation point, + and :math:`\Pr(c\lvert\mathbf{p})` is the prior probability on the constellation symbol :math:`c`: + + .. math:: + \Pr\left(c\lvert\mathbf{p}\right) = \prod_{k=0}^{K-1} \text{sigmoid}\left(p_k \ell(c)_k\right) + + where :math:`\ell(c)_k` is the :math:`k^{th}` bit label of :math:`c`, where 0 is + replaced by -1. + The definition of the LLR has been chosen such that it is equivalent with that of logits. This is different from many textbooks in communications, where the LLR is defined as :math:`LLR(i) = \ln\left(\frac{\Pr\left(b_i=0\lvert y\right)}{\Pr\left(b_i=1\lvert y\right)}\right)`. @@ -1038,36 +1115,45 @@ class Demapper(DemapperWithPrior): are approximated like .. math:: - LLR(i) \approx\ln\left(\frac{ - \max_{c\in\mathcal{C}_{i,1}} \exp\left( - -\frac{1}{N_o}\left|y-c\right|^2 - \right) + \begin{align} + LLR(i) &\approx\ln\left(\frac{ + \max_{c\in\mathcal{C}_{i,1}} \Pr\left(c\lvert\mathbf{p}\right) + \exp\left(-\frac{1}{N_o}\left|y-c\right|^2\right) }{ - \max_{c\in\mathcal{C}_{i,0}} \exp\left( - -\frac{1}{N_o}\left|y-c\right|^2 - \right) - }\right) - = \frac{1}{N_o}\left(\min_{c\in\mathcal{C}_{i,0}}|y-c|^2- - \min_{c\in\mathcal{C}_{i,1}}|y-c|^2\right) + \max_{c\in\mathcal{C}_{i,0}} \Pr\left(c\lvert\mathbf{p}\right) + \exp\left(-\frac{1}{N_o}\left|y-c\right|^2\right) + }\right)\\ + &= \max_{c\in\mathcal{C}_{i,0}} + \left(\ln\left(\Pr\left(c\lvert\mathbf{p}\right)\right)-\frac{|y-c|^2}{N_o}\right) - + \max_{c\in\mathcal{C}_{i,1}}\left( \ln\left(\Pr\left(c\lvert\mathbf{p}\right)\right) - \frac{|y-c|^2}{N_o}\right) . + \end{align} """ - - def call(self, inputs): - y, no = inputs - - # Settings all priors to 0 - num_bits_per_symbol = self.constellation.num_bits_per_symbol - null_prior = tf.zeros([num_bits_per_symbol], y.dtype.real_dtype) - - return super().call([y, null_prior, no]) - -class SymbolDemapperWithPrior(Layer): + def __init__(self, + demapping_method, + constellation_type=None, + num_bits_per_symbol=None, + constellation=None, + hard_out=False, + dtype=tf.complex64, + **kwargs): + super().__init__(demapping_method=demapping_method, + constellation_type=constellation_type, + num_bits_per_symbol=num_bits_per_symbol, + constellation=constellation, + hard_out=hard_out, + with_prior=True, + dtype=dtype, + **kwargs) + +class SymbolDemapper(Layer): # pylint: disable=line-too-long r""" - SymbolDemapperWithPrior(constellation_type=None, num_bits_per_symbol=None, constellation=None, hard_out=False, dtype=tf.complex64, **kwargs) + SymbolDemapper(constellation_type=None, num_bits_per_symbol=None, constellation=None, hard_out=False, with_prior=False, dtype=tf.complex64, **kwargs) Computes normalized log-probabilities (logits) or hard-decisions on symbols - for a tensor of received symbols and assuming prior knowldge on the transmitted constellation points is available. + for a tensor of received symbols. + If the ``with_prior`` flag is set, prior knowldge on the transmitted constellation points is assumed to be available. The demapping function is fully differentiable when soft-values are computed. @@ -1090,13 +1176,18 @@ class SymbolDemapperWithPrior(Layer): If `True`, the demapper provides hard-decided symbols instead of soft-values. Defaults to `False`. + with_prior : bool + If `True`, it is assumed that prior knowledge on the constellation points is available. + This prior information is given as log-probabilities (logits) as an additional input to the layer. + Defaults to `False`. + dtype : One of [tf.complex64, tf.complex128] tf.DType (dtype) The dtype of `y`. Defaults to tf.complex64. The output dtype is the corresponding real dtype (tf.float32 or tf.float64). Input ----- - (y, prior, no) : + (y, no) or (y, prior, no) : Tuple: y : [...,n], tf.complex @@ -1107,6 +1198,7 @@ class SymbolDemapperWithPrior(Layer): It can be provided either as a tensor of shape `[num_points]` for the entire input batch, or as a tensor that is "broadcastable" to `[..., n, num_points]`. + Only required if the ``with_prior`` flag is set. no : Scalar or [...,n], tf.float The noise variance estimate. It can be provided either as scalar @@ -1127,18 +1219,21 @@ class SymbolDemapperWithPrior(Layer): .. math:: \ln\left(\Pr\left(c \lvert y,\mathbf{p}\right)\right) = \ln\left( \frac{\exp\left(-\frac{|y-c|^2}{N_0} + p_c \right)}{\sum_{c'\in\mathcal{C}} \exp\left(-\frac{|y-c'|^2}{N_0} + p_{c'} \right)} \right) - where :math:`\mathcal{C}` is the set of constellation points used for modulation, and :math:`\mathbf{p} = \left\{p_c \lvert c \in \mathcal{C}\right\}` the prior information on constellation points given as log-probabilities. + where :math:`\mathcal{C}` is the set of constellation points used for modulation, + and :math:`\mathbf{p} = \left\{p_c \lvert c \in \mathcal{C}\right\}` the prior information on constellation points given as log-probabilities + and which is set to :math:`\mathbf{0}` if no prior information on the constellation points is assumed to be available. """ - def __init__(self, constellation_type=None, num_bits_per_symbol=None, constellation=None, hard_out=False, + with_prior=False, dtype=tf.complex64, **kwargs): super().__init__(dtype=dtype, **kwargs) self._hard_out = hard_out + self._with_prior = with_prior # Create constellation object self._constellation = Constellation.create_or_check_constellation( @@ -1148,7 +1243,10 @@ def __init__(self, dtype=dtype) def call(self, inputs): - y, prior, no = inputs + if self._with_prior: + y, prior, no = inputs + else: + y, no = inputs points = sn.utils.expand_to_rank(self._constellation.points, tf.rank(y)+1, axis=0) @@ -1158,23 +1256,28 @@ def call(self, inputs): no = sn.utils.expand_to_rank(no, tf.rank(d), axis=-1) exp = -d**2 / no - prior = sn.utils.expand_to_rank(prior, tf.rank(exp), axis=0) + if self._with_prior: + prior = sn.utils.expand_to_rank(prior, tf.rank(exp), axis=0) + exp = exp + prior if self._hard_out: - return tf.argmax(exp + prior, axis=-1, output_type=tf.int32) + return tf.argmax(exp, axis=-1, output_type=tf.int32) else: - return tf.nn.log_softmax(exp + prior, axis=-1) + return tf.nn.log_softmax(exp, axis=-1) -class SymbolDemapper(SymbolDemapperWithPrior): +class SymbolDemapperWithPrior(SymbolDemapper): # pylint: disable=line-too-long r""" - SymbolDemapper(constellation_type=None, num_bits_per_symbol=None, constellation=None, hard_out=False, dtype=tf.complex64, **kwargs) + SymbolDemapperWithPrior(constellation_type=None, num_bits_per_symbol=None, constellation=None, hard_out=False, dtype=tf.complex64, **kwargs) Computes normalized log-probabilities (logits) or hard-decisions on symbols - for a tensor of received symbols. + for a tensor of received symbols, assuming that prior knowledge on the constellation points is available. The demapping function is fully differentiable when soft-values are computed. + This class is deprecated as the functionality has been integrated + into :class:`~sionna.mapping.SymbolDemapper`. + Parameters ---------- constellation_type : One of ["qam", "pam", "custom"], str @@ -1200,12 +1303,18 @@ class SymbolDemapper(SymbolDemapperWithPrior): Input ----- - (y, no) : + (y, prior, no) : Tuple: y : [...,n], tf.complex The received symbols. + prior : [num_points] or [...,num_points], tf.float + Prior for every symbol as log-probabilities (logits). + It can be provided either as a tensor of shape `[num_points]` for the + entire input batch, or as a tensor that is "broadcastable" + to `[..., n, num_points]`. + no : Scalar or [...,n], tf.float The noise variance estimate. It can be provided either as scalar for the entire input batch or as a tensor that is "broadcastable" to @@ -1223,19 +1332,25 @@ class SymbolDemapper(SymbolDemapperWithPrior): The normalized log-probability for the constellation point :math:`c` is computed according to .. math:: - \ln\left(\Pr\left(c \lvert y\right)\right) = \ln\left( \frac{\exp\left(-\frac{|y-c|^2}{N_0} \right)}{\sum_{c'\in\mathcal{C}} \exp\left(-\frac{|y-c'|^2}{N_0} \right)} \right) + \ln\left(\Pr\left(c \lvert y,\mathbf{p}\right)\right) = \ln\left( \frac{\exp\left(-\frac{|y-c|^2}{N_0} + p_c \right)}{\sum_{c'\in\mathcal{C}} \exp\left(-\frac{|y-c'|^2}{N_0} + p_{c'} \right)} \right) - where :math:`\mathcal{C}` is the set of constellation points used for modulation. + where :math:`\mathcal{C}` is the set of constellation points used for modulation, + and :math:`\mathbf{p} = \left\{p_c \lvert c \in \mathcal{C}\right\}` the prior information on constellation points given as log-probabilities. """ - - def call(self, inputs): - y, no = inputs - - # Settings all priors to 0 - num_points = self._constellation.points.shape[0] - null_prior = tf.zeros([num_points], y.dtype.real_dtype) - - return super().call([y, null_prior, no]) + def __init__(self, + constellation_type=None, + num_bits_per_symbol=None, + constellation=None, + hard_out=False, + dtype=tf.complex64, + **kwargs): + super().__init__(constellation_type=constellation_type, + num_bits_per_symbol=num_bits_per_symbol, + constellation=constellation, + hard_out=hard_out, + with_prior=True, + dtype=dtype, + **kwargs) class LLRs2SymbolLogits(Layer): # pylint: disable=line-too-long @@ -1403,8 +1518,7 @@ def __init__(self, constellation, dtype=const_dtype) - def call(self, logits): - + def __call__(self, logits): p = tf.math.softmax(logits, axis=-1) p_c = tf.complex(p, tf.cast(0.0, self.dtype)) points = self._constellation.points @@ -1415,3 +1529,162 @@ def call(self, logits): mean = tf.squeeze(mean, axis=-1) return mean, var + +class QAM2PAM: + r"""Transforms QAM symbol indices to PAM symbol indices. + + For indices in a QAM constellation, computes the corresponding indices + for the two PAM constellations corresponding the real and imaginary + components of the QAM constellation. + + Parameters + ---------- + num_bits_per_symbol : int + The number of bits per QAM constellation symbol, e.g., 4 for QAM16. + + Input + ----- + ind_qam : Tensor, tf.int + Indices in the QAM constellation + + Output + ------- + ind_pam1 : Tensor, tf.int + Indices for the first component of the corresponding PAM modulation + + ind_pam2 : Tensor, tf.int + Indices for the first component of the corresponding PAM modulation + """ + def __init__(self, num_bits_per_symbol): + base = [2**i for i in range(num_bits_per_symbol//2-1, -1, -1)] + base = np.array(base) + pam1_ind = np.zeros([2**num_bits_per_symbol], dtype=np.int32) + pam2_ind = np.zeros([2**num_bits_per_symbol], dtype=np.int32) + for i in range(0, 2**num_bits_per_symbol): + b = np.array(list(np.binary_repr(i,num_bits_per_symbol)), + dtype=np.int32) + pam1_ind[i] = np.sum(b[0::2]*base) + pam2_ind[i] = np.sum(b[1::2]*base) + self._pam1_ind = tf.constant(pam1_ind, dtype=tf.int32) + self._pam2_ind = tf.constant(pam2_ind, dtype=tf.int32) + + def __call__(self, ind_qam): + + ind_pam1 = tf.gather(self._pam1_ind, ind_qam, axis=0) + ind_pam2 = tf.gather(self._pam2_ind, ind_qam, axis=0) + + return ind_pam1, ind_pam2 + +class PAM2QAM: + r"""Transforms PAM symbol indices/logits to QAM symbol indices/logits. + + For two PAM constellation symbol indices or logits, corresponding to + the real and imaginary components of a QAM constellation, + compute the QAM symbol index or logits. + + Parameters + ---------- + num_bits_per_symbol : int + Number of bits per QAM constellation symbol, e.g., 4 for QAM16 + + hard_in_out : bool + Determines if inputs and outputs are indices or logits over + constellation symbols. + Defaults to `True`. + + Input + ----- + pam1 : Tensor, tf.int, or [...,2**(num_bits_per_symbol/2)], tf.float + Indices or logits for the first PAM constellation + + pam2 : Tensor, tf.int, or [...,2**(num_bits_per_symbol/2)], tf.float + Indices or logits for the second PAM constellation + + Output + ------- + qam : Tensor, tf.int, or [...,2**num_bits_per_symbol], tf.float + Indices or logits for the corresponding QAM constellation + """ + def __init__(self, num_bits_per_symbol, hard_in_out=True): + num_pam_symbols = 2**(num_bits_per_symbol//2) + base = np.array([2**i for i in range(num_bits_per_symbol-1, -1, -1)]) + + # Create an array of QAM symbol indices, index by two PAM indices + ind = np.zeros([num_pam_symbols, num_pam_symbols], np.int32) + for i in range(0, num_pam_symbols): + for j in range(0, num_pam_symbols): + b1 = np.array(list(np.binary_repr(i,num_bits_per_symbol//2)), + dtype=np.int16) + b2 = np.array(list(np.binary_repr(j,num_bits_per_symbol//2)), + dtype=np.int16) + b = np.zeros([num_bits_per_symbol], np.int32) + b[0::2] = b1 + b[1::2] = b2 + ind[i, j] = np.sum(b*base) + self._qam_ind = tf.constant(ind, dtype=tf.int32) + self._hard_in_out = hard_in_out + + def __call__(self, pam1, pam2): + + # PAM indices to QAM indices + if self._hard_in_out: + shape = tf.shape(pam1) + ind_pam1 = tf.reshape(pam1, [-1, 1]) + ind_pam2 = tf.reshape(pam2, [-1, 1]) + ind_pam = tf.concat([ind_pam1, ind_pam2], axis=-1) + ind_qam = tf.gather_nd(self._qam_ind, ind_pam) + ind_qam = tf.reshape(ind_qam, shape) + return ind_qam + + # PAM logits to QAM logits + else: + # Compute all combination of sums of logits + logits_mat = tf.expand_dims(pam1, -1) + tf.expand_dims(pam2, -2) + + # Flatten to a vector + logits = sn.utils.flatten_last_dims(logits_mat) + + # Gather symbols in the correct order + gather_ind = tf.reshape(self._qam_ind, [-1]) + logits = tf.gather(logits, gather_ind, axis=-1) + return logits + +class SymbolInds2Bits(Layer): + # pylint: disable=line-too-long + r"""SymbolInds2Bits(num_bits_per_symbol, dtype=tf.float32, **kwargs) + + Transforms symbol indices to their binary representations. + + Parameters + ---------- + num_bits_per_symbol : int + Number of bits per constellation symbol + + dtype: tf.DType + Output dtype. Defaults to `tf.float32`. + + Input + ----- + : Tensor, tf.int + Symbol indices + + Output + ----- + : input.shape + [num_bits_per_symbol], dtype + Binary representation of symbol indices + """ + def __init__(self, + num_bits_per_symbol, + dtype=tf.float32, + **kwargs): + super().__init__(dtype=dtype, **kwargs) + num_symbols = 2**num_bits_per_symbol + b = np.zeros([num_symbols, num_bits_per_symbol]) + for i in range(0, num_symbols): + b[i,:] = np.array(list(np.binary_repr(i, num_bits_per_symbol)), + dtype=np.int16) + self._bit_labels = tf.constant(b, self.dtype) + + def call(self, inputs): + symbol_ind = inputs + return tf.gather(self._bit_labels, symbol_ind) diff --git a/sionna/mimo/__init__.py b/sionna/mimo/__init__.py index 63331cbf..32ebb107 100644 --- a/sionna/mimo/__init__.py +++ b/sionna/mimo/__init__.py @@ -7,7 +7,7 @@ """ from .equalization import lmmse_equalizer, zf_equalizer, mf_equalizer -from .detection import MaximumLikelihoodDetector, MaximumLikelihoodDetectorWithPrior +from .detection import EPDetector, KBestDetector, LinearDetector, MaximumLikelihoodDetector, MaximumLikelihoodDetectorWithPrior, MMSEPICDetector from .precoding import zero_forcing_precoder from .stream_management import StreamManagement -from .utils import complex2real_vector, real2complex_vector, complex2real_matrix, real2complex_matrix, complex2real_covariance, real2complex_covariance, complex2real_channel, real2complex_channel, whiten_channel +from .utils import List2LLR, List2LLRSimple, complex2real_vector, real2complex_vector, complex2real_matrix, real2complex_matrix, complex2real_covariance, real2complex_covariance, complex2real_channel, real2complex_channel, whiten_channel diff --git a/sionna/mimo/detection.py b/sionna/mimo/detection.py index c8df5904..2543e681 100644 --- a/sionna/mimo/detection.py +++ b/sionna/mimo/detection.py @@ -4,22 +4,156 @@ # """Classes and functions related to MIMO channel detection""" +import warnings import numpy as np import tensorflow as tf from tensorflow.keras.layers import Layer +from sionna.utils import expand_to_rank, matrix_sqrt_inv, flatten_last_dims, flatten_dims, split_dim, insert_dims, hard_decisions +from sionna.mapping import Constellation, SymbolLogits2LLRs, LLRs2SymbolLogits, PAM2QAM, Demapper, SymbolDemapper, SymbolInds2Bits, DemapperWithPrior, SymbolLogits2Moments +from sionna.mimo.utils import complex2real_channel, whiten_channel, List2LLR, List2LLRSimple, complex2real_matrix, complex2real_vector, real2complex_vector +from sionna.mimo.equalization import lmmse_equalizer, zf_equalizer, mf_equalizer -from sionna.mimo import real2complex_vector, complex2real_vector, complex2real_matrix, whiten_channel -from sionna.utils import expand_to_rank, matrix_sqrt_inv, hard_decisions, insert_dims -from sionna.mapping import Constellation, SymbolLogits2LLRs, LLRs2SymbolLogits, DemapperWithPrior, SymbolLogits2Moments +class LinearDetector(Layer): + # pylint: disable=line-too-long + r"""LinearDetector(equalizer, output, demapping_method, constellation_type=None, num_bits_per_symbol=None, constellation=None, hard_out=False, dtype=tf.complex64, **kwargs) + + Convenience class that combines an equalizer, + such as :func:`~sionna.mimo.lmmse_equalizer`, and a :class:`~sionna.mapping.Demapper`. + + Parameters + ---------- + equalizer : str, one of ["lmmse", "zf", "mf"], or an equalizer function + The equalizer to be used. Either one of the existing equalizers + :func:`~sionna.mimo.lmmse_equalizer`, :func:`~sionna.mimo.zf_equalizer`, or + :func:`~sionna.mimo.mf_equalizer` can be used, or a custom equalizer + callable provided that has the same input/output specification. + + output : One of ["bit", "symbol"], str + The type of output, either LLRs on bits or logits on constellation symbols. + + demapping_method : One of ["app", "maxlog"], str + The demapping method used. + + constellation_type : One of ["qam", "pam", "custom"], str + For "custom", an instance of :class:`~sionna.mapping.Constellation` + must be provided. + + num_bits_per_symbol : int + The number of bits per constellation symbol, e.g., 4 for QAM16. + Only required for ``constellation_type`` in ["qam", "pam"]. + + constellation : Constellation + An instance of :class:`~sionna.mapping.Constellation` or `None`. + In the latter case, ``constellation_type`` + and ``num_bits_per_symbol`` must be provided. + + hard_out : bool + If `True`, the detector computes hard-decided bit values or + constellation point indices instead of soft-values. + Defaults to `False`. + + dtype : One of [tf.complex64, tf.complex128] tf.DType (dtype) + The dtype of ``y``. Defaults to tf.complex64. + The output dtype is the corresponding real dtype (tf.float32 or tf.float64). + + Input + ------ + (y, h, s) : + Tuple: + + y : [...,M], tf.complex + 1+D tensor containing the received signals + h : [...,M,num_streams], tf.complex + 2+D tensor containing the channel matrices -class MaximumLikelihoodDetectorWithPrior(Layer): + s : [...,M,M], tf.complex + 2+D tensor containing the noise covariance matrices + + Output + ------ + One of: + + : [..., num_streams, num_bits_per_symbol], tf.float + LLRs or hard-decisions for every bit of every stream, if ``output`` equals `"bit"` + + : [..., num_streams, num_points], tf.float or [..., num_streams], tf.int + Logits or hard-decisions for constellation symbols for every stream, if ``output`` equals `"symbol"` + Hard-decisions correspond to the symbol indices. + + Note + ---- + If you want to use this layer in Graph mode with XLA, i.e., within + a function that is decorated with ``@tf.function(jit_compile=True)``, + you might need to set ``sionna.Config.xla_compat=true``. This depends on the + chosen equalizer function. See :py:attr:`~sionna.Config.xla_compat`. + """ + def __init__(self, + equalizer, + output, + demapping_method, + constellation_type=None, + num_bits_per_symbol=None, + constellation=None, + hard_out=False, + dtype=tf.complex64, + **kwargs): + super().__init__(dtype=dtype, **kwargs) + self._output = output + self._hard_out = hard_out + + # Determine the equalizer to use + if isinstance(equalizer, str): + assert equalizer in ["lmmse", "zf", "mf"], "Unknown equalizer." + if equalizer=="lmmse": + self._equalizer = lmmse_equalizer + elif equalizer=="zf": + self._equalizer = zf_equalizer + else: + self._equalizer = mf_equalizer + else: + self._equalizer = equalizer + + assert output in ("bit", "symbol"), "Unknown output" + assert demapping_method in ("app","maxlog"), "Unknown demapping method" + + constellation = Constellation.create_or_check_constellation( + constellation_type, + num_bits_per_symbol, + constellation, + dtype=dtype) + self._constellation = constellation + + # Determine the demapper to use + if output=="bit": + self._demapper = Demapper(demapping_method, + constellation=constellation, + hard_out=hard_out, + dtype=dtype) + else: + self._demapper = SymbolDemapper(constellation=constellation, + hard_out=hard_out, + dtype=dtype) + + def call(self, inputs): + x_hat, no_eff = self._equalizer(*inputs) + z = self._demapper([x_hat, no_eff]) + + # Reshape to the expected output shape + num_streams = tf.shape(inputs[1])[-1] + if self._output == 'bit': + num_bits_per_symbol = self._constellation.num_bits_per_symbol + z = split_dim(z, [num_streams, num_bits_per_symbol], tf.rank(z)-1) + + return z + +class MaximumLikelihoodDetector(Layer): # pylint: disable=line-too-long r""" - MaximumLikelihoodDetectorWithPrior(output, demapping_method, k, constellation_type=None, num_bits_per_symbol=None, constellation=None, hard_out=False, dtype=tf.complex64, **kwargs) + MaximumLikelihoodDetector(output, demapping_method, num_streams, constellation_type=None, num_bits_per_symbol=None, constellation=None, hard_out=False, with_prior=False, dtype=tf.complex64, **kwargs) - MIMO maximum-likelihood (ML) detector, assuming prior - knowledge on the bits or constellation points is available. + MIMO maximum-likelihood (ML) detector. + If the ``with_prior`` flag is set, prior knowledge on the bits or constellation points is assumed to be available. This layer implements MIMO maximum-likelihood (ML) detection assuming the following channel model: @@ -35,8 +169,8 @@ class MaximumLikelihoodDetectorWithPrior(Layer): It is assumed that :math:`\mathbb{E}\left[\mathbf{n}\right]=\mathbf{0}` and :math:`\mathbb{E}\left[\mathbf{n}\mathbf{n}^{\mathsf{H}}\right]=\mathbf{S}`, where :math:`\mathbf{S}` has full rank. - It is assumed that prior information of the transmitted signal :math:`\mathbf{x}` is available, - provided either as LLRs on the bits modulated onto :math:`\mathbf{x}` or as logits on the individual + If the ``with_prior`` flag is set, it is assumed that prior information of the transmitted signal :math:`\mathbf{x}` is available, + provided either as LLRs on the bits mapped onto :math:`\mathbf{x}` or as logits on the individual constellation points forming :math:`\mathbf{x}`. Prior to demapping, the received signal is whitened: @@ -76,13 +210,14 @@ class MaximumLikelihoodDetectorWithPrior(Layer): of the :math:`k\text{th}` user is equal to 1 and 0, respectively. :math:`\Pr\left( \mathbf{x} \right)` is the prior distribution of the vector of constellation points :math:`\mathbf{x}`. Assuming that the constellation points and - bit levels are independant, it is computed from the prior of the bits according to + bit levels are independent, it is computed from the prior of the bits according to .. math:: \Pr\left( \mathbf{x} \right) = \prod_{k=1}^K \prod_{i=1}^{I} \sigma \left( LLR_p(k,i) \right) where :math:`LLR_p(k,i)` is the prior knowledge of the :math:`i\text{th}` bit of the - :math:`k\text{th}` user given as an LLR, and :math:`\sigma\left(\cdot\right)` is the sigmoid function. + :math:`k\text{th}` user given as an LLR and which is set to :math:`0` if no prior knowledge is assumed to be available, + and :math:`\sigma\left(\cdot\right)` is the sigmoid function. The definition of the LLR has been chosen such that it is equivalent with that of logit. This is different from many textbooks in communications, where the LLR is defined as :math:`LLR(k,i) = \ln\left(\frac{\Pr\left(b_{k,i}=0\lvert \mathbf{y},\mathbf{H}\right)}{\Pr\left(b_{k,i}=1\lvert \mathbf{y},\mathbf{H}\right)}\right)`. @@ -144,8 +279,8 @@ class MaximumLikelihoodDetectorWithPrior(Layer): demapping_method : One of ["app", "maxlog"], str The demapping method used. - k : tf.int - Number of transmit streams. + num_streams : tf.int + Number of transmitted streams constellation_type : One of ["qam", "pam", "custom"], str For "custom", an instance of :class:`~sionna.mapping.Constellation` @@ -165,25 +300,32 @@ class MaximumLikelihoodDetectorWithPrior(Layer): constellation point indices instead of soft-values. Defaults to `False`. + with_prior : bool + If `True`, it is assumed that prior knowledge on the bits or constellation points is available. + This prior information is given as LLRs (for bits) or log-probabilities (for constellation points) as an + additional input to the layer. + Defaults to `False`. + dtype : One of [tf.complex64, tf.complex128] tf.DType (dtype) The dtype of ``y``. Defaults to tf.complex64. The output dtype is the corresponding real dtype (tf.float32 or tf.float64). Input ------ - (y, h, prior, s) : + (y, h, s) or (y, h, prior, s) : Tuple: y : [...,M], tf.complex 1+D tensor containing the received signals. - h : [...,M,K], tf.complex + h : [...,M,num_streams], tf.complex 2+D tensor containing the channel matrices. - prior : [...,K,num_bits_per_symbol] or [...,K,num_points], tf.float + prior : [...,num_streams,num_bits_per_symbol] or [...,num_streams,num_points], tf.float Prior of the transmitted signals. If ``output`` equals "bit", then LLRs of the transmitted bits are expected. If ``output`` equals "symbol", then logits of the transmitted constellation points are expected. + Only required if the ``with_prior`` flag is set. s : [...,M,M], tf.complex 2+D tensor containing the noise covariance matrices. @@ -192,10 +334,10 @@ class MaximumLikelihoodDetectorWithPrior(Layer): ------ One of: - : [..., K, num_bits_per_symbol], tf.float + : [..., num_streams, num_bits_per_symbol], tf.float LLRs or hard-decisions for every bit of every stream, if ``output`` equals `"bit"`. - : [..., K, num_points], tf.float or [..., K], tf.int + : [..., num_streams, num_points], tf.float or [..., num_streams], tf.int Logits or hard-decisions for constellation symbols for every stream, if ``output`` equals `"symbol"`. Hard-decisions correspond to the symbol indices. @@ -210,11 +352,12 @@ class MaximumLikelihoodDetectorWithPrior(Layer): def __init__(self, output, demapping_method, - k, + num_streams, constellation_type=None, num_bits_per_symbol=None, constellation=None, hard_out=False, + with_prior=False, dtype=tf.complex64, **kwargs): super().__init__(dtype=dtype, **kwargs) @@ -229,6 +372,7 @@ def __init__(self, self._output = output self._demapping_method = demapping_method self._hard_out = hard_out + self._with_prior = with_prior # Determine the reduce function for LLR computation if self._demapping_method == "app": @@ -244,13 +388,13 @@ def __init__(self, dtype=dtype) # Utility function to compute - # vecs : [num_vecs, K] The list of all possible transmitted vectors. - # vecs_ind : [num_vecs, K] The list of all possible transmitted vectors + # vecs : [num_vecs, num_streams] The list of all possible transmitted vectors. + # vecs_ind : [num_vecs, num_streams] The list of all possible transmitted vectors # constellation indices - # c : [num_vecs/num_points, K, num_points] Which is such that `c[:,k,s]` + # c : [num_vecs/num_points, num_streams, num_points] Which is such that `c[:,k,s]` # gives the symbol indices in the first dimension of `vecs` for which # the `k`th stream transmitted the `s`th constellation point. - vecs, vecs_ind, c = self._build_vecs(k) + vecs, vecs_ind, c = self._build_vecs(num_streams) self._vecs = tf.cast(vecs, dtype) self._vecs_ind = tf.cast(vecs_ind, tf.int32) self._c = tf.cast(c, tf.int32) @@ -273,7 +417,7 @@ def __init__(self, def constellation(self): return self._constellation - def _build_vecs(self, k): + def _build_vecs(self, num_streams): """ Utility function for building the list of all possible transmitted vectors of constellation points and the symbol indices corresponding to @@ -281,15 +425,15 @@ def _build_vecs(self, k): Input ------ - k : int - Number of transmit streams. + num_streams : int + Number of transmitted streams Output ------- vecs : [num_vecs, K], tf.complex List of all possible transmitted vectors. - c : [num_vecs/num_points, K, num_points], int + c : [num_vecs/num_points, num_streams, num_points], int `c[:,k,s]` gives the symbol indices in the first dimension of `vecs` for which the `k`th stream transmitted the `s`th symbol. """ @@ -334,9 +478,9 @@ def _build_vecs_(n): # Building the list of possible vectors for the `k` streams. # [num_vecs, K] - vecs, vecs_ind = _build_vecs_(k) + vecs, vecs_ind = _build_vecs_(num_streams) - tx_ind = np.arange(k) + tx_ind = np.arange(num_streams) tx_ind = np.expand_dims(tx_ind, axis=0) tx_ind = np.tile(tx_ind, [vecs_ind.shape[0], 1]) vecs_ind = np.stack([tx_ind, vecs_ind], axis=-1) @@ -345,11 +489,11 @@ def _build_vecs_(n): # For every constellation point `p` and for every stream `j`, we gather # the list of vector indices from `vecs` corresponding the vectors for # which the `jth` stream transmitted `p`. - # [num_vecs/num_points, K, num_points] + # [num_vecs/num_points, num_streams, num_points] c = [] for p in points: c_ = [] - for j in range(k): + for j in range(num_streams): c_.append(np.where(vecs[:,j]==p)[0]) c_ = np.stack(c_, axis=-1) c.append(c_) @@ -358,13 +502,16 @@ def _build_vecs_(n): return vecs, vecs_ind, c def call(self, inputs): - y, h, prior, s = inputs - - # If operating on bits, computes prior on symbols from the prior - # on bits - if self._output == 'bit': - # [..., K, num_points] - prior = self._llrs2logits(prior) + if self._with_prior: + y, h, prior, s = inputs + + # If operating on bits, computes prior on symbols from the prior + # on bits + if self._output == 'bit': + # [..., K, num_points] + prior = self._llrs2logits(prior) + else: + y, h, s = inputs # Compute square-root of interference covariance matrix s_inv = matrix_sqrt_inv(s) @@ -400,19 +547,20 @@ def call(self, inputs): exponents = -tf.reduce_sum(tf.square(tf.abs(diff)), axis=-1) # Add prior - # [..., num_vecs, K] - prior = expand_to_rank(prior, tf.rank(exponents), axis=0) - prior_rank = tf.rank(prior) - transpose_ind = tf.concat([[prior_rank-2, prior_rank-1], - tf.range(prior_rank-2)], axis=0) - prior = tf.transpose(prior, transpose_ind) - prior = tf.gather_nd(prior, self._vecs_ind) - transpose_ind = tf.concat([ tf.range(2, prior_rank), - [0, 1]], axis=0) - prior = tf.transpose(prior, transpose_ind) - # [..., num_vecs] - prior = tf.reduce_sum(prior, axis=-1) - exponents = exponents + prior + if self._with_prior: + # [..., num_vecs, K] + prior = expand_to_rank(prior, tf.rank(exponents), axis=0) + prior_rank = tf.rank(prior) + transpose_ind = tf.concat([[prior_rank-2, prior_rank-1], + tf.range(prior_rank-2)], axis=0) + prior = tf.transpose(prior, transpose_ind) + prior = tf.gather_nd(prior, self._vecs_ind) + transpose_ind = tf.concat([ tf.range(2, prior_rank), + [0, 1]], axis=0) + prior = tf.transpose(prior, transpose_ind) + # [..., num_vecs] + prior = tf.reduce_sum(prior, axis=-1) + exponents = exponents + prior # Gather exponents for all symbols # [..., num_vecs/num_points, K, num_points] @@ -427,16 +575,20 @@ def call(self, inputs): return self._logits2llr(logits) else: if self._hard_out: - return tf.argmax(logits, axis=-1) + return tf.argmax(logits, axis=-1, output_type=tf.int32) else: return logits -class MaximumLikelihoodDetector(MaximumLikelihoodDetectorWithPrior): +class MaximumLikelihoodDetectorWithPrior(MaximumLikelihoodDetector): # pylint: disable=line-too-long r""" - MaximumLikelihoodDetector(output, demapping_method, k, constellation_type=None, num_bits_per_symbol=None, constellation=None, hard_out=False, dtype=tf.complex64, **kwargs) + MaximumLikelihoodDetectorWithPrior(output, demapping_method, num_streams, constellation_type=None, num_bits_per_symbol=None, constellation=None, hard_out=False, dtype=tf.complex64, **kwargs) - MIMO maximum-likelihood (ML) detector. + MIMO maximum-likelihood (ML) detector, assuming prior + knowledge on the bits or constellation points is available. + + This class is deprecated as the functionality has been integrated + into :class:`~sionna.mimo.MaximumLikelihoodDetector`. This layer implements MIMO maximum-likelihood (ML) detection assuming the following channel model: @@ -452,6 +604,9 @@ class MaximumLikelihoodDetector(MaximumLikelihoodDetectorWithPrior): It is assumed that :math:`\mathbb{E}\left[\mathbf{n}\right]=\mathbf{0}` and :math:`\mathbb{E}\left[\mathbf{n}\mathbf{n}^{\mathsf{H}}\right]=\mathbf{S}`, where :math:`\mathbf{S}` has full rank. + It is assumed that prior information of the transmitted signal :math:`\mathbf{x}` is available, + provided either as LLRs on the bits modulated onto :math:`\mathbf{x}` or as logits on the individual + constellation points forming :math:`\mathbf{x}`. Prior to demapping, the received signal is whitened: @@ -472,20 +627,32 @@ class MaximumLikelihoodDetector(MaximumLikelihoodDetectorWithPrior): of the :math:`k\text{th}` user is then computed according to .. math:: - LLR(k,i) = \ln\left(\frac{\Pr\left(b_{k,i}=1\lvert \mathbf{y},\mathbf{H}\right)}{\Pr\left(b_{k,i}=0\lvert \mathbf{y},\mathbf{H}\right)}\right) =\ln\left(\frac{ - \sum_{\mathbf{x}\in\mathcal{C}_{k,i,1}} \exp\left( - -\left\lVert\tilde{\mathbf{y}}-\tilde{\mathbf{H}}\mathbf{x}\right\rVert^2 - \right) - }{ - \sum_{\mathbf{x}\in\mathcal{C}_{k,i,0}} \exp\left( - -\left\lVert\tilde{\mathbf{y}}-\tilde{\mathbf{H}}\mathbf{x}\right\rVert^2 - \right) - }\right) + \begin{align} + LLR(k,i)&= \ln\left(\frac{\Pr\left(b_{k,i}=1\lvert \mathbf{y},\mathbf{H}\right)}{\Pr\left(b_{k,i}=0\lvert \mathbf{y},\mathbf{H}\right)}\right)\\ + &=\ln\left(\frac{ + \sum_{\mathbf{x}\in\mathcal{C}_{k,i,1}} \exp\left( + -\left\lVert\tilde{\mathbf{y}}-\tilde{\mathbf{H}}\mathbf{x}\right\rVert^2 + \right) \Pr\left( \mathbf{x} \right) + }{ + \sum_{\mathbf{x}\in\mathcal{C}_{k,i,0}} \exp\left( + -\left\lVert\tilde{\mathbf{y}}-\tilde{\mathbf{H}}\mathbf{x}\right\rVert^2 + \right) \Pr\left( \mathbf{x} \right) + }\right) + \end{align} where :math:`\mathcal{C}_{k,i,1}` and :math:`\mathcal{C}_{k,i,0}` are the sets of vectors of constellation points for which the :math:`i\text{th}` bit - of the :math:`k\text{th}` user is equal to 1 and 0, respectively. The definition of the LLR has been - chosen such that it is equivalent with that of logit. This is + of the :math:`k\text{th}` user is equal to 1 and 0, respectively. + :math:`\Pr\left( \mathbf{x} \right)` is the prior distribution of the vector of + constellation points :math:`\mathbf{x}`. Assuming that the constellation points and + bit levels are independent, it is computed from the prior of the bits according to + + .. math:: + \Pr\left( \mathbf{x} \right) = \prod_{k=1}^K \prod_{i=1}^{I} \sigma \left( LLR_p(k,i) \right) + + where :math:`LLR_p(k,i)` is the prior knowledge of the :math:`i\text{th}` bit of the + :math:`k\text{th}` user given as an LLR, and :math:`\sigma\left(\cdot\right)` is the sigmoid function. + The definition of the LLR has been chosen such that it is equivalent with that of logit. This is different from many textbooks in communications, where the LLR is defined as :math:`LLR(k,i) = \ln\left(\frac{\Pr\left(b_{k,i}=0\lvert \mathbf{y},\mathbf{H}\right)}{\Pr\left(b_{k,i}=1\lvert \mathbf{y},\mathbf{H}\right)}\right)`. @@ -495,16 +662,16 @@ class MaximumLikelihoodDetector(MaximumLikelihoodDetectorWithPrior): .. math:: \begin{align} LLR(k,i) \approx&\ln\left(\frac{ - \max_{\mathbf{x}\in\mathcal{C}_{k,i,1}} \exp\left( + \max_{\mathbf{x}\in\mathcal{C}_{k,i,1}} \left( \exp\left( -\left\lVert\tilde{\mathbf{y}}-\tilde{\mathbf{H}}\mathbf{x}\right\rVert^2 - \right) + \right) \Pr\left( \mathbf{x} \right) \right) }{ - \max_{\mathbf{x}\in\mathcal{C}_{k,i,0}} \exp\left( + \max_{\mathbf{x}\in\mathcal{C}_{k,i,0}} \left( \exp\left( -\left\lVert\tilde{\mathbf{y}}-\tilde{\mathbf{H}}\mathbf{x}\right\rVert^2 - \right) + \right) \Pr\left( \mathbf{x} \right) \right) }\right)\\ - = &\min_{\mathbf{x}\in\mathcal{C}_{k,i,0}}\left\lVert\tilde{\mathbf{y}}-\tilde{\mathbf{H}}\mathbf{x}\right\rVert^2- - \min_{\mathbf{x}\in\mathcal{C}_{k,i,1}}\left\lVert\tilde{\mathbf{y}}-\tilde{\mathbf{H}}\mathbf{x}\right\rVert^2. + = &\min_{\mathbf{x}\in\mathcal{C}_{k,i,0}} \left( \left\lVert\tilde{\mathbf{y}}-\tilde{\mathbf{H}}\mathbf{x}\right\rVert^2 - \ln \left(\Pr\left( \mathbf{x} \right) \right) \right) - + \min_{\mathbf{x}\in\mathcal{C}_{k,i,1}} \left( \left\lVert\tilde{\mathbf{y}}-\tilde{\mathbf{H}}\mathbf{x}\right\rVert^2 - \ln \left( \Pr\left( \mathbf{x} \right) \right) \right). \end{align} **ML detection of symbols:** @@ -518,30 +685,25 @@ class MaximumLikelihoodDetector(MaximumLikelihoodDetectorWithPrior): \begin{align} \text{logit}(k,c) &= \ln\left(\sum_{\mathbf{x} : x_k = c} \exp\left( -\left\lVert\tilde{\mathbf{y}}-\tilde{\mathbf{H}}\mathbf{x}\right\rVert^2 - \right)\right)\\ - &= \ln\left( \Pr\left(x_k = c \lvert \mathbf{y}, \mathbf{H} \right) \right) + C + \right)\Pr\left( \mathbf{x} \right)\right). \end{align} - where :math:`C` is a constant. - With the "maxlog" demapping method, the logit for the constellation point :math:`c \in \mathcal{C}` of the :math:`k\text{th}` user is approximated like .. math:: \text{logit}(k,c) \approx \max_{\mathbf{x} : x_k = c} \left( - -\left\lVert\tilde{\mathbf{y}}-\tilde{\mathbf{H}}\mathbf{x}\right\rVert^2 + -\left\lVert\tilde{\mathbf{y}}-\tilde{\mathbf{H}}\mathbf{x}\right\rVert^2 + \ln \left( \Pr\left( \mathbf{x} \right) \right) \right). When hard decisions are requested, this layer returns for the :math:`k` th stream .. math:: - \hat{c}_k = \underset{c \in \mathcal{C}}{\text{argmax}} \Pr\left(x_k = c \lvert \mathbf{y}, \mathbf{H} \right) + \hat{c}_k = \underset{c \in \mathcal{C}}{\text{argmax}} \left( \sum_{\mathbf{x} : x_k = c} \exp\left( + -\left\lVert\tilde{\mathbf{y}}-\tilde{\mathbf{H}}\mathbf{x}\right\rVert^2 + \right)\Pr\left( \mathbf{x} \right) \right) where :math:`\mathcal{C}` is the set of constellation points. - This is not the same as returning the vector :math:`\hat{\mathbf{x}} = \left[ x_0,\dots,x_{K-1} \right]` such that - - .. math:: - \hat{\mathbf{x}} = \min_{\mathbf{x} \in \mathcal{C}^K} \lVert \mathbf{y} - \mathbf{H}\mathbf{x} \rVert^2. Parameters ----------- @@ -551,8 +713,8 @@ class MaximumLikelihoodDetector(MaximumLikelihoodDetectorWithPrior): demapping_method : One of ["app", "maxlog"], str The demapping method used. - k : tf.int - Number of transmit streams. + num_streams : tf.int + Number of transmitted streams constellation_type : One of ["qam", "pam", "custom"], str For "custom", an instance of :class:`~sionna.mapping.Constellation` @@ -578,15 +740,20 @@ class MaximumLikelihoodDetector(MaximumLikelihoodDetectorWithPrior): Input ------ - (y, h, s) : + (y, h, prior, s) : Tuple: y : [...,M], tf.complex 1+D tensor containing the received signals. - h : [...,M,K], tf.complex + h : [...,M,num_streams], tf.complex 2+D tensor containing the channel matrices. + prior : [...,num_streams,num_bits_per_symbol] or [...,num_streams,num_points], tf.float + Prior of the transmitted signals. + If ``output`` equals "bit", then LLRs of the transmitted bits are expected. + If ``output`` equals "symbol", then logits of the transmitted constellation points are expected. + s : [...,M,M], tf.complex 2+D tensor containing the noise covariance matrices. @@ -594,10 +761,10 @@ class MaximumLikelihoodDetector(MaximumLikelihoodDetectorWithPrior): ------ One of: - : [..., K, num_bits_per_symbol], tf.float + : [..., num_streams, num_bits_per_symbol], tf.float LLRs or hard-decisions for every bit of every stream, if ``output`` equals `"bit"`. - : [..., K, num_points], tf.float or [..., K], tf.int + : [..., num_streams, num_points], tf.float or [..., num_streams], tf.int Logits or hard-decisions for constellation symbols for every stream, if ``output`` equals `"symbol"`. Hard-decisions correspond to the symbol indices. @@ -612,66 +779,995 @@ class MaximumLikelihoodDetector(MaximumLikelihoodDetectorWithPrior): def __init__(self, output, demapping_method, - k, + num_streams, constellation_type=None, num_bits_per_symbol=None, constellation=None, hard_out=False, dtype=tf.complex64, **kwargs): - super().__init__( output, - demapping_method, - k, - constellation_type, - num_bits_per_symbol, - constellation, - hard_out, - dtype, + super().__init__( output=output, + demapping_method=demapping_method, + num_streams=num_streams, + constellation_type=constellation_type, + num_bits_per_symbol=num_bits_per_symbol, + constellation=constellation, + hard_out=hard_out, + with_prior=True, + dtype=dtype, **kwargs) - self._num_tx = k +class KBestDetector(Layer): + # pylint: disable=line-too-long + r"""KBestDetector(output, num_streams, k, constellation_type=None, num_bits_per_symbol=None, constellation=None, hard_out=False, use_real_rep=False, list2llr=None, dtype=tf.complex64) + + MIMO K-Best detector + + This layer implements K-Best MIMO detection as described + in (Eq. 4-5) [FT2015]_. It can either generate hard decisions (for symbols + or bits) or compute LLRs. + + The algorithm operates in either the complex or real-valued domain. + Although both options produce identical results, the former has the advantage + that it can be applied to arbitrary non-QAM constellations. It also reduces + the number of streams (or depth) by a factor of two. + + The way soft-outputs (i.e., LLRs) are computed is determined by the + ``list2llr`` function. The default solution + :class:`~sionna.mimo.List2LLRSimple` assigns a predetermined + value to all LLRs without counter-hypothesis. + + This layer assumes the following channel model: + + .. math:: + \mathbf{y} = \mathbf{H}\mathbf{x} + \mathbf{n} + + where :math:`\mathbf{y}\in\mathbb{C}^M` is the received signal vector, + :math:`\mathbf{x}\in\mathcal{C}^S` is the vector of transmitted symbols which + are uniformly and independently drawn from the constellation :math:`\mathcal{C}`, + :math:`\mathbf{H}\in\mathbb{C}^{M\times S}` is the known channel matrix, + and :math:`\mathbf{n}\in\mathbb{C}^M` is a complex Gaussian noise vector. + It is assumed that :math:`\mathbb{E}\left[\mathbf{n}\right]=\mathbf{0}` and + :math:`\mathbb{E}\left[\mathbf{n}\mathbf{n}^{\mathsf{H}}\right]=\mathbf{S}`, + where :math:`\mathbf{S}` has full rank. + + In a first optional step, the channel model is converted to its real-valued equivalent, + see :func:`~sionna.mimo.complex2real_channel`. We assume in the sequel the complex-valued + representation. Then, the channel is whitened using :func:`~sionna.mimo.whiten_channel`: + + .. math:: + \tilde{\mathbf{y}} &= \mathbf{S}^{-\frac{1}{2}}\mathbf{y}\\ + &= \mathbf{S}^{-\frac{1}{2}}\mathbf{H}\mathbf{x} + \mathbf{S}^{-\frac{1}{2}}\mathbf{n}\\ + &= \tilde{\mathbf{H}}\mathbf{x} + \tilde{\mathbf{n}}. + + Next, the columns of :math:`\tilde{\mathbf{H}}` are sorted according + to their norm in descending order. Then, the QR decomposition of the + resulting channel matrix is computed: + + .. math:: + \tilde{\mathbf{H}} = \mathbf{Q}\mathbf{R} + + where :math:`\mathbf{Q}\in\mathbb{C}^{M\times S}` is unitary and + :math:`\mathbf{R}\in\mathbb{C}^{S\times S}` is upper-triangular. + The channel outputs are then pre-multiplied by :math:`\mathbf{Q}^{\mathsf{H}}`. + This leads to the final channel model on which the K-Best detection algorithm operates: + + .. math:: + \bar{\mathbf{y}} = \mathbf{R}\bar{\mathbf{x}} + \bar{\mathbf{n}} + + where :math:`\bar{\mathbf{y}}\in\mathbb{C}^S`, + :math:`\bar{\mathbf{x}}\in\mathbb{C}^S`, and :math:`\bar{\mathbf{n}}\in\mathbb{C}^S` + with :math:`\mathbb{E}\left[\bar{\mathbf{n}}\right]=\mathbf{0}` and + :math:`\mathbb{E}\left[\bar{\mathbf{n}}\bar{\mathbf{n}}^{\mathsf{H}}\right]=\mathbf{I}`. + + **LLR Computation** + + The K-Best algorithm produces :math:`K` candidate solutions :math:`\bar{\mathbf{x}}_k\in\mathcal{C}^S` + and their associated distance metrics :math:`d_k=\lVert \bar{\mathbf{y}} - \mathbf{R}\bar{\mathbf{x}}_k \rVert^2` + for :math:`k=1,\dots,K`. If the real-valued channel representation is used, the distance + metrics are scaled by 0.5 to account for the reduced noise power in each complex dimension. + A hard-decision is simply the candidate with the shortest distance. + Various ways to compute LLRs from this list (and possibly + additional side-information) are possible. The (sub-optimal) default solution + is :class:`~sionna.mimo.List2LLRSimple`. Custom solutions can be provided. + + Parameters + ----------- + output : One of ["bit", "symbol"], str + The type of output, either bits or symbols. Whether soft- or + hard-decisions are returned can be configured with the + ``hard_out`` flag. + + num_streams : tf.int + Number of transmitted streams + + k : tf.int + The number of paths to keep. Cannot be larger than the + number of constellation points to the power of the number of + streams. + + constellation_type : One of ["qam", "pam", "custom"], str + For "custom", an instance of :class:`~sionna.mapping.Constellation` + must be provided. + + num_bits_per_symbol : int + The number of bits per constellation symbol, e.g., 4 for QAM16. + Only required for ``constellation_type`` in ["qam", "pam"]. + + constellation : Constellation + An instance of :class:`~sionna.mapping.Constellation` or `None`. + In the latter case, ``constellation_type`` + and ``num_bits_per_symbol`` must be provided. + + hard_out : bool + If `True`, the detector computes hard-decided bit values or + constellation point indices instead of soft-values. + Defaults to `False`. The detector cannot compute soft-symbols. + + use_real_rep : bool + If `True`, the detector use the real-valued equivalent representation + of the channel. Note that this only works with a QAM constellation. + Defaults to `False`. + + list2llr: `None` or instance of :class:`~sionna.mimo.List2LLR` + The function to be used to compute LLRs from a list of candidate solutions. + If `None`, the default solution :class:`~sionna.mimo.List2LLRSimple` + is used. + + dtype : One of [tf.complex64, tf.complex128] tf.DType (dtype) + The dtype of ``y``. Defaults to tf.complex64. + The output dtype is the corresponding real dtype (tf.float32 or tf.float64). + + Input + ----- + (y, h, s) : + Tuple: + + y : [...,M], tf.complex + 1+D tensor containing the received signals + + h : [...,M,num_streams], tf.complex + 2+D tensor containing the channel matrices + + s : [...,M,M], tf.complex + 2+D tensor containing the noise covariance matrices + + Output + ------ + One of: + + : [...,num_streams,num_bits_per_symbol], tf.float + LLRs or hard-decisions for every bit of every stream, if ``output`` equals `"bit"` + + : [...,num_streams,2**num_points], tf.float or [...,num_streams], tf.int + Logits or hard-decisions for constellation symbols for every stream, if ``output`` equals `"symbol"` + Hard-decisions correspond to the symbol indices. + + Note + ---- + If you want to use this layer in Graph mode with XLA, i.e., within + a function that is decorated with ``@tf.function(jit_compile=True)``, + you must set ``sionna.Config.xla_compat=true``. + See :py:attr:`~sionna.Config.xla_compat`. + """ + def __init__(self, + output, + num_streams, + k, + constellation_type=None, + num_bits_per_symbol=None, + constellation=None, + hard_out=False, + use_real_rep=False, + list2llr="default", + dtype=tf.complex64, + **kwargs): + super().__init__(dtype=dtype, **kwargs) + assert dtype in [tf.complex64, tf.complex128],\ + "dtype must be tf.complex64 or tf.complex128." + + assert output in ("bit", "symbol"), "Unknown output" + + err_msg = "You must provide either constellation or " + \ + "constellation_type and num_bits_per_symbol." + if constellation is None: + assert constellation_type is not None and \ + num_bits_per_symbol is not None, err_msg + else: + assert constellation_type is None and \ + num_bits_per_symbol is None, err_msg + + if constellation is not None: + assert constellation.points.dtype==dtype, \ + "Constellation has wrong dtype." + + self._output = output + self._hard_out = hard_out + self._use_real_rep = use_real_rep + + if self._use_real_rep: + # Real-valued representation is used + err_msg = "Only QAM can be used for the real-valued representation" + if constellation_type is not None: + assert constellation_type=="qam", err_msg + else: + assert constellation._constellation_type=="qam", err_msg + + # Double the number of streams to dectect + self._num_streams = 2*num_streams + + # Half the number of bits for the PAM constellation + if num_bits_per_symbol is None: + n = constellation.num_bits_per_symbol//2 + self._num_bits_per_symbol = n + else: + self._num_bits_per_symbol = num_bits_per_symbol//2 + + # Geerate a PAM constellation with 0.5 energy + c = Constellation("pam", + self._num_bits_per_symbol, + normalize=False, + dtype=dtype) + c._points /= tf.cast(np.std(c._points)*np.sqrt(2), c._points.dtype) + self._constellation = tf.cast(c.points, dtype.real_dtype) + + self._pam2qam = PAM2QAM(2*self._num_bits_per_symbol) + + else: + # Complex-valued representation is used + # Number of streams is equal to number of transmitters + self._num_streams = num_streams + + # Create constellation or take the one provided + c = Constellation.create_or_check_constellation( + constellation_type, + num_bits_per_symbol, + constellation, + dtype=dtype) + self._constellation = c.points + self._num_bits_per_symbol = c.num_bits_per_symbol + + # Number of constellation symbols + self._num_symbols = self._constellation.shape[0] + + # Number of best paths to keep + self._k = np.minimum(k, self._num_symbols**self._num_streams) + if self._k < k: + msg = "KBestDetector: " + \ + f"The provided value of k={k} is larger than " + \ + "the possible maximum number of paths. " + \ + f"It has been set to k={self._k}." + warnings.warn(msg) + + # Compute the number of previous paths a layer needs to consider + num_paths = [1] # The first layer considers a single path + for l in range(1, self._num_streams+1): + # The lth layer considers min(k, num_symbols**l) paths + num_paths.append(np.minimum(self._k, self._num_symbols**l)) + self._num_paths = tf.constant(tf.stack(num_paths, 0), tf.int32) + + # The symbols and indices for all paths will be stored in tensors + # of shape [batch_size, k, num_streams]. However, only + # a subset of the available entries are updated by each stream. + # To enable XLA, we need to compute the relevant indices of the tensors + # that will be updated through tf.tensor_scatter_nd_update. + indices = np.zeros([self._num_streams, self._k*self._num_streams, 2], + np.int32) + for l in range(0, self._num_streams): + ind = np.zeros([self._num_paths[l+1], self._num_streams]) + ind[:, :l+1] = 1 + ind = np.stack(np.where(ind), -1) + indices[l,:ind.shape[0],:ind.shape[1]] = ind + self._indices = tf.constant(indices, dtype=tf.int32) + + if self._output=="bit": + if self._hard_out is False: + if list2llr=="default": + self.list2llr = List2LLRSimple(self._num_bits_per_symbol) + else: + self.list2llr = list2llr + else: + if self._use_real_rep: + n = 2*self._num_bits_per_symbol + else: + n = self._num_bits_per_symbol + self._symbolinds2bits = SymbolInds2Bits(n, + dtype=dtype.real_dtype) + else: + assert self._hard_out is True, \ + "Soft-symbols are not supported for this detector." + + @property + def list2llr(self): + return self._list2llr + + @list2llr.setter + def list2llr(self, value): + assert isinstance(value, List2LLR) + self._list2llr = value + + def _preprocessing(self, inputs): + + y, h, s = inputs + + # Convert to real-valued representation if desired + if self._use_real_rep: + y, h, s = complex2real_channel(y, h, s) + + # Whiten channel + y, h = whiten_channel(y, h, s, return_s=False) # pylint: disable=W0632 + + # Order columns of H in order of decreasing norm + h_norm = tf.reduce_sum(tf.abs(h)**2, axis=1) + column_order = tf.argsort(h_norm, axis=-1, direction="DESCENDING") + h = tf.gather(h, column_order, axis=-1, batch_dims=1) + + # Compute QR decomposition of sorted channel + # r is upper triangular + q, r = tf.linalg.qr(h) + + # Project y on Q' + y = tf.squeeze(tf.matmul(q, tf.expand_dims(y, -1), adjoint_a=True), + -1) + + return y, r, column_order + + def _select_best_paths(self, dists, path_syms, path_inds): + + # Determine the number of paths to keep (either all or k) + num_paths = tf.shape(path_syms)[1] + k = tf.minimum(num_paths, self._k) + + # Get the k paths with the shortest distance + dists, ind = tf.math.top_k(-dists, k=k, sorted=True) + dists = -dists + + # Select the same best paths for the symbols and symbol indices + path_syms = tf.gather(path_syms, ind, axis=1, batch_dims=1) + path_inds = tf.gather(path_inds, ind, axis=1, batch_dims=1) + + return dists, path_syms, path_inds + + def _next_layer(self, y, r, dists, path_syms, path_inds, stream): + + batch_size = tf.shape(y)[0] + + # Streams are processed in reverse order + stream_ind = self._num_streams-1-stream + + # Current number of considered paths + num_paths = tf.gather(self._num_paths, stream) + + # Store input tensors for scatter update later on + dists_o = dists + path_syms_o = path_syms + path_inds_o = path_inds + + # Extract relevant values from input tensor + dists = dists[..., :num_paths] + path_syms = path_syms[..., :num_paths, :stream] + path_inds = path_inds[..., :num_paths, :stream] + + # Each path creates num_symbols branches + dists = tf.repeat(dists, repeats=self._num_symbols, axis=1) + path_syms = tf.repeat(path_syms, repeats=self._num_symbols, axis=1) + path_inds = tf.repeat(path_inds, repeats=self._num_symbols, axis=1) + + # Append to each path the symbols corresponding to the branch + syms = tf.reshape(self._constellation, [1,-1]) + syms = tf.repeat(syms, self._k, 0) + syms = tf.reshape(syms, [1, -1, 1]) + syms = tf.repeat(syms, batch_size, 0) + syms = syms[:,:num_paths*self._num_symbols] + path_syms = tf.concat([path_syms, syms], axis=-1) + + # Do the same for the symbol indices + inds = tf.reshape(tf.range(0, self._num_symbols), [1, -1]) + inds = tf.repeat(inds, self._k, 0) + inds = tf.reshape(inds, [1, -1, 1]) + inds = tf.repeat(inds, batch_size, 0) + inds = inds[:,:num_paths*self._num_symbols] + path_inds = tf.concat([path_inds, inds], axis=-1) + + # Compute partial distances + # Extract the row of r corresponding to layer and reverse the order + y = tf.expand_dims(y[:, stream_ind], axis=-1) + r = tf.expand_dims(tf.reverse(r[:, stream_ind, stream_ind:], [-1]), 1) + delta = tf.pow(tf.abs(y - tf.reduce_sum(r*path_syms, axis=-1)), 2) + + # Update distances + dists += delta + + # Get k best paths + dists, path_syms, path_inds = self._select_best_paths(dists, path_syms, path_inds) + + # Scatter updates of dists + tensor = tf.transpose(dists_o, perm=[1, 0]) + updates = tf.transpose(dists, perm=[1, 0]) + indices = tf.expand_dims(tf.range(tf.shape(updates)[0], dtype=tf.int32), -1) + dists = tf.tensor_scatter_nd_update(tensor, indices, updates) + dists = tf.transpose(dists, perm=[1, 0]) + + # Scatter update of path_syms + tensor = tf.transpose(path_syms_o, [1, 2, 0]) + updates = tf.transpose(path_syms, [1, 2, 0]) + updates = tf.reshape(updates, [-1, batch_size]) + indices = self._indices[stream, :self._num_paths[stream+1]*(stream+1)] + path_syms = tf.tensor_scatter_nd_update(tensor, indices, updates) + path_syms = tf.transpose(path_syms, perm=[2, 0, 1]) + + # Scatter update of path_inds + tensor = tf.transpose(path_inds_o, [1, 2, 0]) + updates = tf.transpose(path_inds, [1, 2, 0]) + updates = tf.reshape(updates, [-1, batch_size]) + path_inds = tf.tensor_scatter_nd_update(tensor, indices, updates) + path_inds = tf.transpose(path_inds, perm=[2, 0, 1]) + + return dists, path_syms, path_inds + + def _unsort(self, column_order, tensor, transpose=True): + # Undo the column sorting + # If transpose=True, the unsorting is done along the last dimension + # Otherwise, sorting is done along the second-last index + unsort_inds = tf.argsort(column_order, axis=-1) + if transpose: + tensor = tf.transpose(tensor, perm=[0, 2, 1]) + tensor = tf.gather(tensor, unsort_inds, axis=-2, batch_dims=1) + if transpose: + tensor = tf.transpose(tensor, perm=[0, 2, 1]) + return tensor + + def build(self, input_shape): + assert input_shape[1][-2]>=input_shape[1][-1], \ + "The number of receive antennas cannot be smaller \ + than the number of streams" + + def call(self, inputs): + + # Flatten the batch dimensions + y, h, s = inputs + batch_shape = tf.shape(y)[:-1] + num_batch_dims = len(batch_shape) + if num_batch_dims > 1: + y = flatten_dims(y, num_batch_dims, 0) + h = flatten_dims(h, num_batch_dims, 0) + s = flatten_dims(s, num_batch_dims, 0) + inputs = (y,h,s) + + # Initialization + # (i) (optional) Convert to real-valued representation + # (ii) Whiten channel + # (iii) Sort columns of H by decreasing column norm + # (iv) QR Decomposition of H + # (v) Project y onto Q' + y, r, column_order = self._preprocessing(inputs) + + batch_size = tf.shape(y)[0] + + # Tensor to keep track of the aggregate distances of all paths + dists = tf.zeros([batch_size, self._k], y.dtype.real_dtype) + + # Tensor to store constellation symbols of all paths + path_syms = tf.zeros([batch_size, self._k, self._num_streams], y.dtype) + + # Tensor to store constellation symbol indices of all paths + path_inds = tf.zeros([batch_size, self._k, self._num_streams],tf.int32) + + # Sequential K-Best algorithm + for stream in range(0, self._num_streams): + dists, path_syms, path_inds = self._next_layer(y, + r, + dists, + path_syms, + path_inds, + stream) + + # Reverse order as detection started with the last symbol first + path_syms = tf.reverse(path_syms, axis=[-1]) + path_inds = tf.reverse(path_inds, axis=[-1]) + + # Processing for hard-decisions + if self._hard_out: + path_inds = self._unsort(column_order, path_inds) + hard_dec = path_inds[:,0,:] + + # Real-valued representation + if self._use_real_rep: + hard_dec = \ + self._pam2qam(hard_dec[...,:self._num_streams//2], + hard_dec[...,self._num_streams//2:]) + + # Hard decisions on bits + if self._output=="bit": + hard_dec = self._symbolinds2bits(hard_dec) + + # Reshape batch dimensions + if num_batch_dims > 1: + hard_dec = split_dim(hard_dec, batch_shape, 0) + + return hard_dec + + # Processing for soft-decisions + else: + # Real-valued representation + if self._use_real_rep: + llr = self.list2llr([y, r, dists, path_inds, path_syms]) + llr = self._unsort(column_order, llr, transpose=False) + + # Combine LLRs from PAM symbols in the correct order + llr1 = llr[:,:self._num_streams//2] + llr2 = llr[:,self._num_streams//2:] + llr1 = tf.expand_dims(llr1, -1) + llr2 = tf.expand_dims(llr2, -1) + llr = tf.concat([llr1, llr2], -1) + llr = tf.reshape(llr, [-1, self._num_streams//2, + 2*self._num_bits_per_symbol]) + + # Complex-valued representation + else: + llr = self.list2llr([y, r, dists, path_inds, path_syms]) + llr = self._unsort(column_order, llr, transpose=False) + + # Reshape batch dimensions + if num_batch_dims > 1: + llr = split_dim(llr, batch_shape, 0) + + return llr + +class EPDetector(Layer): + # pylint: disable=line-too-long + r"""EPDetector(output, num_bits_per_symbol, hard_out=False, l=10, beta=0.9, dtype=tf.complex64) + + MIMO Expectation Propagation (EP) detector + + This layer implements Expectation Propagation (EP) MIMO detection as described + in [EP2014]_. It can generate hard- or soft-decisions for symbols or bits. + + This layer assumes the following channel model: + + .. math:: + \mathbf{y} = \mathbf{H}\mathbf{x} + \mathbf{n} + + where :math:`\mathbf{y}\in\mathbb{C}^M` is the received signal vector, + :math:`\mathbf{x}\in\mathcal{C}^S` is the vector of transmitted symbols which + are uniformly and independently drawn from the constellation :math:`\mathcal{C}`, + :math:`\mathbf{H}\in\mathbb{C}^{M\times S}` is the known channel matrix, + and :math:`\mathbf{n}\in\mathbb{C}^M` is a complex Gaussian noise vector. + It is assumed that :math:`\mathbb{E}\left[\mathbf{n}\right]=\mathbf{0}` and + :math:`\mathbb{E}\left[\mathbf{n}\mathbf{n}^{\mathsf{H}}\right]=\mathbf{S}`, + where :math:`\mathbf{S}` has full rank. + + The channel model is first whitened using :func:`~sionna.mimo.whiten_channel` + and then converted to its real-valued equivalent, + see :func:`~sionna.mimo.complex2real_channel`, prior to MIMO detection. + + The computation of LLRs is done by converting the symbol logits + that naturally arise in the algorithm to LLRs using + :func:`~sionna.mapping.PAM2QAM`. Custom conversions of symbol logits to LLRs + can be implemented by using the soft-symbol output. + + Parameters + ----------- + output : One of ["bit", "symbol"], str + The type of output, either bits or symbols. Whether soft- or + hard-decisions are returned can be configured with the + ``hard_out`` flag. + + num_bits_per_symbol : int + The number of bits per QAM constellation symbol, e.g., 4 for QAM16. + + hard_out : bool + If `True`, the detector computes hard-decided bit values or + constellation point indices instead of soft-values. + Defaults to `False`. + + l : int + Number of iterations. Defaults to 10. + + beta : float + Parameter :math:`\beta\in[0,1]` for update smoothing. + Defaults to 0.9. + + dtype : One of [tf.complex64, tf.complex128] tf.DType (dtype) + Precision used for internal computations. Defaults to ``tf.complex64``. + Especially for large MIMO setups, the precision can make a significant + performance difference. + + Input + ----- + (y, h, s) : + Tuple: + + y : [...,M], tf.complex + 1+D tensor containing the received signals + + h : [...,M,num_streams], tf.complex + 2+D tensor containing the channel matrices + + s : [...,M,M], tf.complex + 2+D tensor containing the noise covariance matrices + + Output + ------ + One of: + + : [...,num_streams,num_bits_per_symbol], tf.float + LLRs or hard-decisions for every bit of every stream, if ``output`` equals `"bit"` + + : [...,num_streams,2**num_bits_per_symbol], tf.float or [...,num_streams], tf.int + Logits or hard-decisions for constellation symbols for every stream, if ``output`` equals `"symbol"` + + Note + ---- + For numerical stability, we do not recommend to use this function in Graph + mode with XLA, i.e., within a function that is decorated with + ``@tf.function(jit_compile=True)``. + However, it is possible to do so by setting + ``sionna.Config.xla_compat=true``. + See :py:attr:`~sionna.Config.xla_compat`. + """ + def __init__(self, + output, + num_bits_per_symbol, + hard_out=False, + l=10, + beta=0.9, + dtype=tf.complex64, + **kwargs): + super().__init__(dtype=dtype, **kwargs) + assert dtype in [tf.complex64, tf.complex128], \ + "Invalid dtype" + self._cdtype = tf.dtypes.as_dtype(dtype) + self._rdtype = self._cdtype.real_dtype + + # Variable used to avoid numerical instabilities + # See paragraph after Eq. (38) + if self.dtype=="complex64": + self._prec = 1e-6 + else: + self._prec = 1e-12 + + assert output in ("bit", "symbol"), "Unknown output" + self._output = output + + self._hard_out = hard_out + + if self._output=="symbol": + self._pam2qam = PAM2QAM(num_bits_per_symbol, hard_out) + else: + self._symbollogits2llrs = SymbolLogits2LLRs("maxlog", + num_bits_per_symbol//2, + hard_out=hard_out) + self._demapper = Demapper("maxlog", "pam", num_bits_per_symbol//2) + + assert l>=1, "l must be a positive integer" + self._l = l + + assert 0.0<= beta <=1.0, "beta must be in [0,1]" + self._beta = beta + + # Create PAM constellations for real-valued detection + self._num_bits_per_symbol = num_bits_per_symbol//2 + points = Constellation("pam", int(self._num_bits_per_symbol)).points + + # Scale constellation points to half the energy because QAM is assumed + self._points = tf.cast(points/np.sqrt(2.0), self._rdtype) + + # Average symbol energy + self._es = tf.constant(np.var(self._points), self._rdtype) + + def compute_sigma_mu(self, h_t_h, h_t_y, no, lam, gam): + """Equations (28) and (29)""" + + # Prepare inputs + lam = tf.linalg.diag(lam) + gam = tf.expand_dims(gam, axis=-1) + + # Computations + sigma = tf.linalg.inv(h_t_h + no*lam) + mu = tf.squeeze(tf.matmul(sigma, h_t_y + no*gam), axis=-1) + sigma *= no + sigma = tf.linalg.diag_part(sigma) + + return sigma, mu + + def compute_v_x_obs(self, sigma, mu, lam, gam): + """Equations (31) and (32)""" + + v_obs = tf.maximum(1/(1/sigma-lam), self._prec) + x_obs = v_obs*(mu/sigma-gam) + + return v_obs, x_obs + + def compute_v_x(self, v_obs, x_obs): + """Equation (33)""" + + # Compute probability mass function for the symbols + x_obs = tf.expand_dims(x_obs, -1) + v_obs = tf.expand_dims(v_obs, -1) + + points = expand_to_rank(self._points, tf.rank(x_obs), axis=0) + logits = -tf.pow(x_obs-points, 2) / (tf.cast(2, self._rdtype)*v_obs) + pmf = tf.math.softmax(logits) + + # Compute mean and variance of all symbols + x = tf.reduce_sum(points * pmf, axis=-1, keepdims=True) + v = tf.reduce_sum((points-x)**2 * pmf, axis=-1) + v = tf.maximum(v, self._prec) + x = tf.squeeze(x, axis=-1) + + return v, x, logits + + def update_lam_gam(self, v, v_obs, x, x_obs, lam, gam): + """Equations (35), (36), (37), (38)""" + + # Save old values of lam, and gam + lam_old = lam + gam_old = gam + + # Compute potential new values (35), (36) + lam = 1/v - 1/v_obs + gam = x/v - x_obs/v_obs + + # Only update nonnegative values + lam_new = tf.where(lam<0, lam_old, lam) + gam_new = tf.where(lam<0, gam_old, gam) + + # Damp updates (37), (38) + lam_damp = (1-self._beta)*lam_new + self._beta*lam_old + gam_damp = (1-self._beta)*gam_new + self._beta*gam_old + + return lam_damp, gam_damp def call(self, inputs): + + # Flatten the batch dimensions y, h, s = inputs + batch_shape = tf.shape(y)[:-1] + num_batch_dims = len(batch_shape) + if num_batch_dims > 1: + y = flatten_dims(y, num_batch_dims, 0) + h = flatten_dims(h, num_batch_dims, 0) + s = flatten_dims(s, num_batch_dims, 0) + inputs = (y,h,s) + + # Number of transmit streams + n_t = tf.shape(h)[-1] + + # Whiten channel + y, h, s = whiten_channel(y, h, s) + + # Convert channel to real-valued representation + y, h, s = complex2real_channel(y,h,s) + + # Convert all inputs to desired dtypes + y = tf.cast(y, self._rdtype) + h = tf.cast(h, self._rdtype) + no = tf.cast(0.5, self._rdtype) + + # Gather relevant parameters + batch_dims = tf.shape(y)[:-1] + n_t_r = tf.shape(h)[-1] + + # Initialize gamma and lambda (Paragraph after Eq. (29)) + gam = tf.zeros(tf.concat([batch_dims, [n_t_r]], axis=0), y.dtype) + lam = tf.ones(tf.concat([batch_dims, [n_t_r]], axis=0), y.dtype) + lam /= tf.cast(self._es, y.dtype) + + # Precompute values that are repeatedly needed + h_t_h = tf.matmul(h, h, transpose_a=True) + y = tf.expand_dims(y, axis=-1) + h_t_y = tf.matmul(h, y, transpose_a=True) + no = expand_to_rank(no, tf.rank(h), axis=-1) + + for _ in range(self._l): + sigma, mu = self.compute_sigma_mu(h_t_h, h_t_y, no, lam, gam) + v_obs, x_obs = self.compute_v_x_obs(sigma, mu, lam, gam) + v, x, logits = self.compute_v_x(v_obs, x_obs) + lam, gam = self.update_lam_gam(v, v_obs, x, x_obs, lam, gam) + + # Extract the logits for the 2 PAM constellations for each streams + pam1_logits = logits[...,:n_t,:] + pam2_logits = logits[...,n_t:,:] + + if self._output=="symbol" and self._hard_out: + # Take hard decisions on PAM symbol;s + pam1_ind = tf.argmax(pam1_logits, axis=-1, output_type=tf.int32) + pam2_ind = tf.argmax(pam2_logits, axis=-1, output_type=tf.int32) + + # Transform to QAM indices + qam_ind = self._pam2qam(pam1_ind, pam2_ind) + + # Reshape batch dimensions + if num_batch_dims > 1: + qam_ind = split_dim(qam_ind, batch_shape, 0) + + return qam_ind + + elif self._output=="symbol" and not self._hard_out: + qam_logits = self._pam2qam(pam1_logits, pam2_logits) + + # Reshape batch dimensions + if num_batch_dims > 1: + qam_logits = split_dim(qam_logits, batch_shape, 0) + + return qam_logits + + elif self._output=="bit": + # Compute LLRs for both PAM constellations + llr1 = self._symbollogits2llrs(pam1_logits) + llr2 = self._symbollogits2llrs(pam2_logits) + + # Put LLRs in the correct order and shape + llr = tf.stack([llr1, llr2], -1) + llr = flatten_last_dims(llr) + + # Reshape batch dimensions + if num_batch_dims > 1: + llr = split_dim(llr, batch_shape, 0) + + return llr + +class MMSEPICDetector(Layer): + # pylint: disable=line-too-long + r"""MMSEPICDetector(output, demapping_method="maxlog", num_iter=1, constellation_type=None, num_bits_per_symbol=None, constellation=None, hard_out=False, dtype=tf.complex64, **kwargs) + + Minimum mean square error (MMSE) with parallel interference cancellation (PIC) detector + + This layer implements the MMSE PIC detector, as proposed in [CST2011]_. + For ``num_iter``>1, this implementation performs MMSE PIC self-iterations. + MMSE PIC self-iterations can be understood as a concatenation of MMSE PIC + detectors from [CST2011]_, which forward intrinsic LLRs to the next + self-iteration. + + Compared to [CST2011]_, this implementation also accepts priors on the + constellation symbols as an alternative to priors on the bits. + + This layer assumes the following channel model: + + .. math:: + \mathbf{y} = \mathbf{H}\mathbf{x} + \mathbf{n} - prior_shape = tf.concat([tf.shape(y)[:-1], - [self._num_tx, self._constellation.num_bits_per_symbol]], axis=-1) - prior = tf.zeros(prior_shape, tf.as_dtype(self._dtype).real_dtype) - return super().call([y, h, prior, s]) + where :math:`\mathbf{y}\in\mathbb{C}^M` is the received signal vector, + :math:`\mathbf{x}\in\mathcal{C}^S` is the vector of transmitted symbols which + are uniformly and independently drawn from the constellation :math:`\mathcal{C}`, + :math:`\mathbf{H}\in\mathbb{C}^{M\times S}` is the known channel matrix, + and :math:`\mathbf{n}\in\mathbb{C}^M` is a complex Gaussian noise vector. + It is assumed that :math:`\mathbb{E}\left[\mathbf{n}\right]=\mathbf{0}` and + :math:`\mathbb{E}\left[\mathbf{n}\mathbf{n}^{\mathsf{H}}\right]=\mathbf{S}`, + where :math:`\mathbf{S}` has full rank. -""" -This layer implements the soft-input soft-output minimum mean squared error (MMSE) parallel interference cancellation -detector (SISO MMSE PIC), as proposed in [CST2011]_. For num_iter>1, this implementation performs MMSE PIC self-iterations, -which can lead to (minor) additional performance gains. MMSE PIC self-iterations can be understood as a concatenation of -MMSE PIC detectors from [CST2011]_, which forward intrinsic LLRs to the next (self-)iteration. + The algorithm starts by computing the soft symbols + :math:`\bar{x}_s=\mathbb{E}\left[ x_s \right]` and + variances :math:`v_s=\mathbb{E}\left[ |e_s|^2\right]` from the priors, + where :math:`e_s = x_s - \bar{x}_s`, for all :math:`s=1,\dots,S`. -In addition to [CST2011]_, this implementation also accepts symbol logit priors. However, for consistency, -the input symbol logits are mapped to LLRs and the symbol logit outputs are also computed from the MMSE PIC output LLRs. + Next, for each stream, the interference caused by all other streams is cancelled + from the observation :math:`\mathbf{y}`, leading to + + .. math:: + \hat{\mathbf{y}}_s = \mathbf{y} - \sum_{j\neq s} \mathbf{h}_j x_j = \mathbf{h}_s x_s + \tilde{\mathbf{n}}_s,\quad s=1,\dots,S -Based on previous results, classical iterative detection and decoding (IDD) showed best performance, if the MMSE PIC -data detector outputs extrinsic LLRs to the decoder (also implemented here) and the decoder provides the MMSE PIC with -intrinsic LLRs. + where :math:`\tilde{\mathbf{n}}_s=\sum_{j\neq s} \mathbf{h}_j e_j + \mathbf{n}`. -[CST2011]_ C. Studer, S. Fateh, and D. Seethaler, "ASIC Implementation of Soft-Input Soft-Output -MIMO Detection Using MMSE Parallel Interference Cancellation," IEEE Journal of Solid-State Circuits, -vol. 46, no. 7, pp. 1754–1765, July 2011. https://ieeexplore.ieee.org/document/5779722 -""" + Then, a linear MMSE filter :math:`\mathbf{w}_s` is computed to reduce the resdiual noise + for each observation :math:`\hat{\mathbf{y}}_s`, which is given as -class SiSoMmsePicDetector(Layer): + .. math:: + \mathbf{w}_s = \mathbf{h}_s^{\mathsf{H}}\left( \mathbf{H} \mathbf{D}_s\mathbf{H}^{\mathsf{H}} +\mathbf{S} \right)^{-1} + + where :math:`\mathbf{D}_s \in \mathbb{C}^{S\times S}` is diagonal with entries + + .. math:: + \left[\mathbf{D}_s\right]_{i,i} = \begin{cases} + v_i & i\neq s \\ + 1 & i=s. + \end{cases} + + The filtered observations + + .. math:: + \tilde{z}_s = \mathbf{w}_s^{\mathsf{H}} \hat{\mathbf{y}}_s = \tilde{\mu}_s x_s + \mathbf{w}_s^{\mathsf{H}}\tilde{\mathbf{n}}_s + + where :math:`\tilde{\mu}_s=\mathbf{w}_s^{\mathsf{H}} \mathbf{h}_s`, are then demapped to either symbol logits or LLRs, assuming that the remaining noise is Gaussian with variance + + .. math:: + \nu_s^2 = \mathop{\text{Var}}\left[\tilde{z}_s\right] = \mathbf{w}_s^{\mathsf{H}} \left(\sum_{j\neq s} \mathbf{h}_j \mathbf{h}_j^{\mathsf{H}} v_j +\mathbf{S} \right)\mathbf{w}_s. + + The resulting soft-symbols can then be used for the next self-iteration of the algorithm. + + Note that this algorithm can be substantially simplified as described in [CST2011]_ to avoid + the computation of different matrix inverses for each stream. This is the version which is + implemented. + + Parameters + ----------- + output : One of ["bit", "symbol"], str + The type of output, either LLRs on bits or logits on constellation + symbols. + + demapping_method : One of ["app", "maxlog"], str + The demapping method used. + Defaults to "maxlog". + + num_iter : int + Number of MMSE PIC iterations. + Defaults to 1. + + constellation_type : One of ["qam", "pam", "custom"], str + For "custom", an instance of :class:`~sionna.mapping.Constellation` + must be provided. + + num_bits_per_symbol : int + The number of bits per constellation symbol, e.g., 4 for QAM16. + Only required for ``constellation_type`` in ["qam", "pam"]. + + constellation : Constellation + An instance of :class:`~sionna.mapping.Constellation` or `None`. + In the latter case, ``constellation_type`` + and ``num_bits_per_symbol`` must be provided. + + hard_out : bool + If `True`, the detector computes hard-decided bit values or + constellation point indices instead of soft-values. + Defaults to `False`. + + dtype : One of [tf.complex64, tf.complex128] tf.DType (dtype) + The dtype of ``y``. Defaults to tf.complex64. + The output dtype is the corresponding real dtype + (tf.float32 or tf.float64). + + Input + ----- + (y, h, prior, s) : + Tuple: + + y : [...,M], tf.complex + 1+D tensor containing the received signals + + h : [...,M,S], tf.complex + 2+D tensor containing the channel matrices + + prior : [...,S,num_bits_per_symbol] or [...,S,num_points], tf.float + Prior of the transmitted signals. + If ``output`` equals "bit", then LLRs of the transmitted bits are expected. + If ``output`` equals "symbol", then logits of the transmitted constellation points are expected. + + s : [...,M,M], tf.complex + 2+D tensor containing the noise covariance matrices + + Output + ------ + One of: + + : [...,S,num_bits_per_symbol], tf.float + LLRs or hard-decisions for every bit of every stream, if ``output`` equals `"bit"` + + : [...,S,2**num_bits_per_symbol], tf.float or [...,S], tf.int + Logits or hard-decisions for constellation symbols for every stream, if ``output`` equals `"symbol"` + + Note + ---- + For numerical stability, we do not recommend to use this function in Graph + mode with XLA, i.e., within a function that is decorated with + ``@tf.function(jit_compile=True)``. + However, it is possible to do so by setting + ``sionna.Config.xla_compat=true``. + See :py:attr:`~sionna.Config.xla_compat`. + """ def __init__(self, + output, demapping_method="maxlog", num_iter=1, - output="bit", constellation_type=None, num_bits_per_symbol=None, constellation=None, hard_out=False, dtype=tf.complex64, - epsilon = 1e-4, **kwargs): super().__init__(dtype=dtype, **kwargs) - assert type(num_iter) is int, "num_iter must be an integer" + assert isinstance(num_iter, int), "num_iter must be an integer" assert output in ("bit", "symbol"), "Unknown output" assert demapping_method in ("app", "maxlog"), "Unknown demapping method" @@ -680,6 +1776,10 @@ def __init__(self, self._num_iter = num_iter self._output = output + self._epsilon = 1e-4 + self._realdtype = dtype.real_dtype + self._demapping_method = demapping_method + self._hard_out = hard_out # Create constellation object self._constellation = Constellation.create_or_check_constellation( @@ -688,47 +1788,66 @@ def __init__(self, constellation, dtype=dtype) - self._epsilon = epsilon - self._realdtype = dtype.real_dtype - - self._demapping_method = demapping_method - self._hard_out = hard_out + # Soft symbol mapping + self._llr_2_symbol_logits = LLRs2SymbolLogits( + self._constellation.num_bits_per_symbol, + dtype=self._realdtype) - # soft symbol mapping - self._llr2symbolLogits = LLRs2SymbolLogits(self._constellation.num_bits_per_symbol, dtype=self._realdtype) # soft if self._output == "symbol": - self._llr2symbolLogits_output = LLRs2SymbolLogits(self._constellation.num_bits_per_symbol, dtype=self._realdtype, hard_out=hard_out) # soft or hard - self._symbolLogits2LLRs = SymbolLogits2LLRs(method=demapping_method, num_bits_per_symbol=self._constellation.num_bits_per_symbol) - self._symbolLogits2moments = SymbolLogits2Moments(constellation=self._constellation, dtype=self._realdtype) + self._llr_2_symbol_logits_output = LLRs2SymbolLogits( + self._constellation.num_bits_per_symbol, + dtype=self._realdtype, + hard_out=hard_out) + self._symbol_logits_2_llrs = SymbolLogits2LLRs( + method=demapping_method, + num_bits_per_symbol=self._constellation.num_bits_per_symbol) + self._symbol_logits_2_moments = SymbolLogits2Moments( + constellation=self._constellation, + dtype=self._realdtype) # soft output demapping - self._bit_demapper = DemapperWithPrior(demapping_method=demapping_method, constellation=constellation, dtype=dtype) + self._bit_demapper = DemapperWithPrior( + demapping_method=demapping_method, + constellation=self._constellation, + dtype=dtype) def call(self, inputs): y, h, prior, s = inputs - # y is unwhitened receive signal [..., M] - # h the channel estimate [..., M, K] - # prior is either the soft input LLRs [..., K, num_bits_per_symbol] or symbol logits [..., K, Q] - # s the noise covariance matrix [..., M, M] - - ## preprocessing + # y is unwhitened receive signal + # [..., M] + # h the channel estimate + # [..., M, K] + # prior is either the soft input LLRs + # [..., K, num_bits_per_symbol] or symbol logits [..., K, Q] + # s the noise covariance matrix + # [..., M, M] + + ## Preprocessing # Whiten channel + # y : [..., M] + # s : [..., M, M] y, h = whiten_channel(y, h, s, return_s=False) # pylint: disable=unbalanced-tuple-unpacking # matched filtering of y - y_mf = insert_dims(tf.linalg.matvec(h, y, adjoint_a=True), num_dims=1, axis=-1) # y_mf is [..., K, 1] + # [..., K, 1] + y_mf = insert_dims(tf.linalg.matvec(h, y, adjoint_a=True), + num_dims=1, axis=-1) ## Step 1: compute Gramm matrix - g = tf.matmul(h, h, adjoint_a=True) # g is [..., K, K] + # [..., K, K] + g = tf.matmul(h, h, adjoint_a=True) - # For XLA compatibility, this implementation performs the MIMO equalization in the real-valued domain - hr = complex2real_matrix(h) # hr is [..., 2M, 2K] - gr = tf.matmul(hr, hr, adjoint_a=True) # gr is [..., 2K, 2K] + # For XLA compatibility, this implementation performs the MIMO + # equalization in the real-valued domain + # [..., 2M, 2K] + hr = complex2real_matrix(h) + # [..., 2K, 2K] + gr = tf.matmul(hr, hr, adjoint_a=True) - # compute a priori LLRs + # Compute a priori LLRs if self._output == "symbol": - llr_a = self._symbolLogits2LLRs(prior) + llr_a = self._symbol_logits_2_llrs(prior) else: llr_a = prior # llr_a is [..., K, num_bits_per_symbol] @@ -738,17 +1857,17 @@ def mmse_pic_self_iteration(llr_d, llr_a, it): # MMSE PIC takes in a priori LLRs llr_a = llr_d - # Step 2: compute soft symbol estimates and variances using built-in Sionna utility functions - # Notice that there are more efficient direct computation approaches available - # For an example, refer to https://ieeexplore.ieee.org/abstract/document/4025128 or to - # https://github.com/rwiesmayr/sionna/blob/main/sionna/ofdm/equalization.py for a Sionna implementation - x_hat, var_x = self._symbolLogits2moments(self._llr2symbolLogits(llr_a)) # both are [..., K] + # Step 2: compute soft symbol estimates and variances + # x_hat, var_x : [..., K] + x_logits = self._llr_2_symbol_logits(llr_a) + x_hat, var_x = self._symbol_logits_2_moments(x_logits) # Step 3: perform parallel interference cancellation - # H^H y_hat_i = y_mf - sum_j!=i gj x_hat_j = y + g_i x_hat_i - sum_j g_j x_hat_j + # H^H y_hat_i = y_mf - sum_j!=i gj x_hat_j = y + g_i x_hat_i + # - sum_j g_j x_hat_j + # [..., K, K] y_mf_pic = y_mf + g * insert_dims(x_hat, num_dims=1, axis=-2) \ - - tf.linalg.matmul(g, insert_dims(x_hat, num_dims=1, axis=-1)) - # y_mf_pic is [..., K, K] + - tf.linalg.matmul(g, insert_dims(x_hat, num_dims=1, axis=-1)) # Step 4: compute A^-1 matrix # Calculate MMSE Filter (efficiently) @@ -756,66 +1875,84 @@ def mmse_pic_self_iteration(llr_d, llr_a, it): # A = H^H H \Lambda + N_0 I_Mt # \Lambda_ii is a diagonal matrix with \Lambda_ii = E_i = error_var - # stack error variances and make it real (imaginary part is zero anyway) - var_x = tf.cast(tf.concat([var_x, var_x], axis=-1), dtype=self._realdtype) + # Stack error variances and make it real + # Note: Imaginary part is zero + var_x = tf.cast(tf.concat([var_x, var_x], axis=-1), + dtype=self._realdtype) var_x_row_vec = insert_dims(var_x, num_dims=1, axis=-2) + # [..., 2K, 2K] a = gr * var_x_row_vec - # a is [..., 2K, 2K] - i = expand_to_rank(tf.eye(tf.shape(a)[-1], dtype=a.dtype), tf.rank(a), 0) + i = expand_to_rank(tf.eye(tf.shape(a)[-1], dtype=a.dtype), + tf.rank(a), 0) a = a + i - a_inv = tf.linalg.inv(a) # a is non-hermitian! that's why we can't use sn.utils.matrix_inv - # XLA can't invert complex matrices, that's why we work with the real valued domain + # a is non-hermitian! that's why we can't use sn.utils.matrix_inv + # XLA can't invert complex matrices, that's why we work with the + # real valued domain + a_inv = tf.linalg.inv(a) # Step 5: compute unbiased MMSE filter and outputs, calculate A\H^H - # calculate bias mu_i = diag(A^-1 H^H H) = diag(A^-1 G) - # diagonal elements of matrix matrix multiplication simplified to sum and dot-product + # Calculate bias mu_i = diag(A^-1 H^H H) = diag(A^-1 G) + # Diagonal elements of matrix matrix multiplication simplified + # to sum and dot-product + # [..., 2K] mu = tf.reduce_sum(a_inv * tf.linalg.matrix_transpose(gr), axis=-1) - # mu is [..., 2K] - - # make y_mf_pic columns real (after transposition, the last dimension corresponds to vectors) - y_mf_pic_trans = complex2real_vector(tf.linalg.matrix_transpose(y_mf_pic)) # is [..., K, 2K] - # stack them such that y_mf_pic_trans is [..., 2K, 2K] - y_mf_pic_trans = tf.concat([y_mf_pic_trans, y_mf_pic_trans], axis=-2) - # efficient parallel equalization after PIC (z_i = i'th row of a_inv * y_MF_PIC_i) + # Make y_mf_pic columns real (after transposition, + # the last dimension corresponds to vectors) + # [..., K, 2K] + y_mf_pic_trans = tf.linalg.matrix_transpose(y_mf_pic) + y_mf_pic_trans = complex2real_vector(y_mf_pic_trans) + # stack them such that y_mf_pic_trans has shape [..., 2K, 2K] + y_mf_pic_trans = tf.concat([y_mf_pic_trans, y_mf_pic_trans], + axis=-2) + + # Efficient parallel equalization after PIC + # z_i = i'th row of a_inv * y_MF_PIC_i # boils down to tf.reduce_sum(a_inv * y_mf_pic_trans, axis=-1) # divide by mu_i for unbiasedness - x_hat = real2complex_vector(tf.reduce_sum(a_inv * y_mf_pic_trans, axis=-1) / tf.cast(mu, dtype=a_inv.dtype)) - # x_hat is [..., K] - - # compute post equalization signal error estimate: rho_i = mu_i / (1 - var_x_i * mu_i) - # 1 - var_x_i * mu_i can become numerically 0 (or even slightly smaller than zero due to limited numerical precision) - var_x = tf.divide(mu, tf.maximum(1 - var_x * mu, self._epsilon)) # is [..., 2K] - var_x, _ = tf.split(var_x, 2, -1) # real variances map to the same complex valued variances in this model + # [..., K] + x_hat = real2complex_vector(tf.reduce_sum(a_inv * y_mf_pic_trans, + axis=-1) / tf.cast(mu, dtype=a_inv.dtype)) + + # Compute post equalization signal error estimate: + # rho_i = mu_i / (1 - var_x_i * mu_i) + # 1 - var_x_i * mu_i can become numerically 0, or even slightly + # smaller than zero due to limited numerical precision + # [..., 2K] + var_x = tf.divide(mu, tf.maximum(1 - var_x * mu, self._epsilon)) + # real variances map to the same complex valued variances in this + # model + var_x, _ = tf.split(var_x, 2, -1) no_eff = 1. / var_x # Step 6: LLR demapping (extrinsic LLRs) - # notice that there are more efficient direct computation approaches available - # For an example, refer to https://ieeexplore.ieee.org/document/1371654 or to - # https://github.com/rwiesmayr/sionna/blob/main/sionna/ofdm/equalization.py for a Sionna implementation - llr_d = tf.reshape(self._bit_demapper([x_hat, llr_a, no_eff]), llr_shape) - # llr_d is [..., K, num_bits_per_symbols] + # [..., K, num_bits_per_symbols] + llr_d = tf.reshape(self._bit_demapper([x_hat, llr_a, no_eff]), + llr_shape) return llr_d, llr_a, it - # stopping condition (required for tf.while_loop) + # Stopping condition (required for tf.while_loop) def dec_stop(llr_d, llr_a, it): # pylint: disable=W0613 return tf.less(it, self._num_iter) # start decoding iterations it = tf.constant(0) null_prior = tf.zeros(llr_shape, dtype=self._realdtype) - llr_d, llr_a, _ = tf.while_loop(dec_stop, mmse_pic_self_iteration, (llr_a, null_prior, it), - parallel_iterations=1, - maximum_iterations=self._num_iter) + llr_d, llr_a, _ = tf.while_loop(dec_stop, + mmse_pic_self_iteration, + (llr_a, null_prior, it), + parallel_iterations=1, + maximum_iterations=self._num_iter) llr_e = llr_d - llr_a if self._output == "symbol": - # convert back to symbols if requested. This llr2symbol mapper also performs hard-decisions, if specified - out = self._llr2symbolLogits_output(llr_e) # output symbol logits computed on extrinsic LLRs + # convert back to symbols if requested. + # output symbol logits computed on extrinsic LLRs + out = self._llr_2_symbol_logits_output(llr_e) else: # output extrinsic LLRs out = llr_e diff --git a/sionna/mimo/equalization.py b/sionna/mimo/equalization.py index e1764b54..08e565d6 100644 --- a/sionna/mimo/equalization.py +++ b/sionna/mimo/equalization.py @@ -119,7 +119,7 @@ def lmmse_equalizer(y, h, s, whiten_interference=True): y, h = whiten_channel(y, h, s, return_s=False) # pylint: disable=unbalanced-tuple-unpacking # Compute G - i = expand_to_rank(tf.eye(tf.shape(h)[-1], dtype=s.dtype), tf.rank(s), 0) + i = expand_to_rank(tf.eye(h.shape[-1], dtype=s.dtype), tf.rank(s), 0) g = tf.matmul(h, h, adjoint_a=True) + i g = tf.matmul(matrix_inv(g), h, adjoint_b=True) @@ -349,7 +349,7 @@ def mf_equalizer(y, h, s): # Compute residual error variance gsg = tf.matmul(tf.matmul(g, s), g, adjoint_b=True) gh = tf.matmul(g, h) - i = expand_to_rank(tf.eye(tf.shape(gsg)[-2], dtype=gsg.dtype), tf.rank(gsg), 0) + i = expand_to_rank(tf.eye(gsg.shape[-2], dtype=gsg.dtype), tf.rank(gsg), 0) no_eff = tf.abs(tf.linalg.diag_part(tf.matmul(i-gh, i-gh, adjoint_b=True) + gsg)) return x_hat, no_eff diff --git a/sionna/mimo/stream_management.py b/sionna/mimo/stream_management.py index 7d0c0931..e5c12af3 100644 --- a/sionna/mimo/stream_management.py +++ b/sionna/mimo/stream_management.py @@ -14,7 +14,7 @@ class StreamManagement(): ---------- rx_tx_association : [num_rx, num_tx], np.int A binary NumPy array where ``rx_tx_association[i,j]=1`` means - that receiver `i` gets one ore multiple streams from + that receiver `i` gets one or multiple streams from transmitter `j`. num_streams_per_tx : int diff --git a/sionna/mimo/utils.py b/sionna/mimo/utils.py index 65f8c392..3f5313a3 100644 --- a/sionna/mimo/utils.py +++ b/sionna/mimo/utils.py @@ -4,8 +4,11 @@ # """Utility functions and layers for the MIMO package.""" +import numpy as np import tensorflow as tf -from sionna.utils import matrix_sqrt_inv, expand_to_rank +from tensorflow.keras.layers import Layer +from abc import ABC, abstractmethod +from sionna.utils import matrix_sqrt_inv, expand_to_rank, insert_dims def complex2real_vector(z): # pylint: disable=line-too-long @@ -47,7 +50,7 @@ def real2complex_vector(z): Input ----- - : [...,2M], tf.real + : [...,2M], tf.float Output ------ @@ -112,7 +115,7 @@ def real2complex_matrix(z): Input ----- - : [...,2M,2K], tf.real + : [...,2M,2K], tf.float Output ------ @@ -180,7 +183,7 @@ def real2complex_covariance(q): Input ----- - : [...,2M,2M], tf.real + : [...,2M,2M], tf.float Output ------ @@ -263,13 +266,13 @@ def real2complex_channel(y, h, s): Input ----- - y : [...,2M], tf.real + y : [...,2M], tf.float 1+D tensor containing the real-valued received signals. - h : [...,2M,2K], tf.real + h : [...,2M,2K], tf.float 2+D tensor containing the real-valued channel matrices. - s : [...,2M,2M], tf.real + s : [...,2M,2M], tf.float 2+D tensor containing the real-valued noise covariance matrices. Output @@ -311,13 +314,13 @@ def whiten_channel(y, h, s, return_s=True): Input ----- - y : [...,M], tf.real or tf.complex + y : [...,M], tf.float or tf.complex 1+D tensor containing the received signals. - h : [...,M,K], tf.real or tf.complex + h : [...,M,K], tf.float or tf.complex 2+D tensor containing the channel matrices. - s : [...,M,M], tf.real or complex + s : [...,M,M], tf.float or complex 2+D tensor containing the noise covariance matrices. return_s : bool @@ -326,13 +329,13 @@ def whiten_channel(y, h, s, return_s=True): Output ------ - : [...,M], tf.real or tf.complex + : [...,M], tf.float or tf.complex 1+D tensor containing the whitened received signals. - : [...,M,K], tf.real or tf.complex + : [...,M,K], tf.float or tf.complex 2+D tensor containing the whitened channel matrices. - : [...,M,M], tf.real or tf.complex + : [...,M,M], tf.float or tf.complex 2+D tensor containing the whitened noise covariance matrices. Only returned if ``return_s`` is `True`. """ @@ -354,3 +357,216 @@ def whiten_channel(y, h, s, return_s=True): return yw, hw, sw else: return yw, hw + + +class List2LLR(ABC): + # pylint: disable=line-too-long + r"""List2LLR() + + Abstract class defining a callable to compute LLRs from a list of + candidate vectors (or paths) provided by a MIMO detector. + + The following channel model is assumed + + .. math:: + \bar{\mathbf{y}} = \mathbf{R}\bar{\mathbf{x}} + \bar{\mathbf{n}} + + where :math:`\bar{\mathbf{y}}\in\mathbb{C}^S` are the channel outputs, + :math:`\mathbf{R}\in\mathbb{C}^{S\times S}` is an upper-triangular matrix, + :math:`\bar{\mathbf{x}}\in\mathbb{C}^S` is the transmitted vector whose entries + are uniformly and independently drawn from the constellation :math:`\mathcal{C}`, + and :math:`\bar{\mathbf{n}}\in\mathbb{C}^S` is white noise + with :math:`\mathbb{E}\left[\bar{\mathbf{n}}\right]=\mathbf{0}` and + :math:`\mathbb{E}\left[\bar{\mathbf{n}}\bar{\mathbf{n}}^{\mathsf{H}}\right]=\mathbf{I}`. + + It is assumed that a MIMO detector such as :class:`~sionna.mimo.KBestDetector` + produces :math:`K` candidate solutions :math:`\bar{\mathbf{x}}_k\in\mathcal{C}^S` + and their associated distance metrics :math:`d_k=\lVert \bar{\mathbf{y}} - \mathbf{R}\bar{\mathbf{x}}_k \rVert^2` + for :math:`k=1,\dots,K`. This layer can also be used with the real-valued representation of the channel. + + Input + ----- + (y, r, dists, path_inds, path_syms) : + Tuple: + + y : [...,M], tf.complex or tf.float + Channel outputs of the whitened channel + + r : [...,num_streams, num_streams], same dtype as ``y`` + Upper triangular channel matrix of the whitened channel + + dists : [...,num_paths], tf.float + Distance metric for each path (or candidate) + + path_inds : [...,num_paths,num_streams], tf.int32 + Symbol indices for every stream of every path (or candidate) + + path_syms : [...,num_path,num_streams], same dtype as ``y`` + Constellation symbol for every stream of every path (or candidate) + + Output + ------ + llr : [...num_streams,num_bits_per_symbol], tf.float + LLRs for all bits of every stream + + Note + ---- + An implementation of this class does not need to make use of all of + the provided inputs which enable various different implementations. + """ + @abstractmethod + def __call__(self, inputs): + raise NotImplementedError + +class List2LLRSimple(Layer, List2LLR): + # pylint: disable=line-too-long + r"""List2LLRSimple(num_bits_per_symbol, llr_clip_val=20.0, **kwargs) + + Computes LLRs from a list of candidate vectors (or paths) provided by a MIMO detector. + + The following channel model is assumed: + + .. math:: + \bar{\mathbf{y}} = \mathbf{R}\bar{\mathbf{x}} + \bar{\mathbf{n}} + + where :math:`\bar{\mathbf{y}}\in\mathbb{C}^S` are the channel outputs, + :math:`\mathbf{R}\in\mathbb{C}^{S\times S}` is an upper-triangular matrix, + :math:`\bar{\mathbf{x}}\in\mathbb{C}^S` is the transmitted vector whose entries + are uniformly and independently drawn from the constellation :math:`\mathcal{C}`, + and :math:`\bar{\mathbf{n}}\in\mathbb{C}^S` is white noise + with :math:`\mathbb{E}\left[\bar{\mathbf{n}}\right]=\mathbf{0}` and + :math:`\mathbb{E}\left[\bar{\mathbf{n}}\bar{\mathbf{n}}^{\mathsf{H}}\right]=\mathbf{I}`. + + It is assumed that a MIMO detector such as :class:`~sionna.mimo.KBestDetector` + produces :math:`K` candidate solutions :math:`\bar{\mathbf{x}}_k\in\mathcal{C}^S` + and their associated distance metrics :math:`d_k=\lVert \bar{\mathbf{y}} - \mathbf{R}\bar{\mathbf{x}}_k \rVert^2` + for :math:`k=1,\dots,K`. This layer can also be used with the real-valued representation of the channel. + + The LLR for the :math:`i\text{th}` bit of the :math:`k\text{th}` stream is computed as + + .. math:: + \begin{align} + LLR(k,i) &= \log\left(\frac{\Pr(b_{k,i}=1|\bar{\mathbf{y}},\mathbf{R})}{\Pr(b_{k,i}=0|\bar{\mathbf{y}},\mathbf{R})}\right)\\ + &\approx \min_{j \in \mathcal{C}_{k,i,0}}d_j - \min_{j \in \mathcal{C}_{k,i,1}}d_j + \end{align} + + where :math:`\mathcal{C}_{k,i,1}` and :math:`\mathcal{C}_{k,i,0}` are the set of indices + in the list of candidates for which the :math:`i\text{th}` bit of the :math:`k\text{th}` + stream is equal to 1 and 0, respectively. The LLRs are clipped to :math:`\pm LLR_\text{clip}` + which can be configured through the parameter ``llr_clip_val``. + + If :math:`\mathcal{C}_{k,i,0}` is empty, :math:`LLR(k,i)=LLR_\text{clip}`; + if :math:`\mathcal{C}_{k,i,1}` is empty, :math:`LLR(k,i)=-LLR_\text{clip}`. + + Parameters + ---------- + num_bits_per_symbol : int + Number of bits per constellation symbol + + llr_clip_val : float + The absolute values of LLRs are clipped to this value. + Defaults to 20.0. Can also be a trainable variable. + + Input + ----- + (y, r, dists, path_inds, path_syms) : + Tuple: + + y : [...,M], tf.complex or tf.float + Channel outputs of the whitened channel + + r : [...,num_streams, num_streams], same dtype as ``y`` + Upper triangular channel matrix of the whitened channel + + dists : [...,num_paths], tf.float + Distance metric for each path (or candidate) + + path_inds : [...,num_paths,num_streams], tf.int32 + Symbol indices for every stream of every path (or candidate) + + path_syms : [...,num_path,num_streams], same dtype as ``y`` + Constellation symbol for every stream of every path (or candidate) + + Output + ------ + llr : [...num_streams,num_bits_per_symbol], tf.float + LLRs for all bits of every stream + """ + def __init__(self, + num_bits_per_symbol, + llr_clip_val=20.0, + **kwargs): + super().__init__(**kwargs) + + # Array composed of binary representations of all symbols indices + num_points = 2**num_bits_per_symbol + a = np.zeros([num_points, num_bits_per_symbol]) + for i in range(num_points): + a[i, :] = np.array(list(np.binary_repr(i, num_bits_per_symbol)), + dtype=np.int32) + + # Compute symbol indices for which the bits are 0 or 1, e.g.,: + # The ith column of c0 provides all symbol indices for which + # the ith bit is 0. + c0 = np.zeros([int(num_points/2), num_bits_per_symbol]) + c1 = np.zeros([int(num_points/2), num_bits_per_symbol]) + for i in range(num_bits_per_symbol): + c0[:,i] = np.where(a[:,i]==0)[0] + c1[:,i] = np.where(a[:,i]==1)[0] + + # Convert to tensor and add dummy dimensions needed for broadcasting + self._c0 = expand_to_rank(tf.constant(c0, tf.int32), 5, 0) + self._c1 = expand_to_rank(tf.constant(c1, tf.int32), 5, 0) + + # Assign this absolute value to all LLRs without counter-hypothesis + self.llr_clip_val = llr_clip_val + + @property + def llr_clip_val(self): + return self._llr_clip_val + + @llr_clip_val.setter + def llr_clip_val(self, value): + self._llr_clip_val = value + + def __call__(self, inputs): + + # dists : [batch_size, num_paths] + # path_inds : [batch_size, num_paths, num_streams] + dists, path_inds = inputs[2:4] + + # Scaled by 0.5 to account for the reduced noise power in each complex + # dimension if real channel representation is used. + if inputs[0].dtype.is_floating: + dists = dists/2.0 + + # Compute for every symbol in every path which bits are 0 or 1 + # b0/b1: [batch_size, num_path, num_streams, num_bits_per_symbol] + # The reduce_any op is forced to run in XLA mode to be able to + # work with very large tensors. There seems to an int32 indexing issue + # for all TF reduce CUDA kernels. + path_inds = insert_dims(path_inds, 2, axis=-1) + b0 = tf.equal(path_inds, self._c0) + b1 = tf.equal(path_inds, self._c1) + b0 = tf.function(tf.reduce_any, jit_compile=True)(b0, axis=-2) + b1 = tf.function(tf.reduce_any, jit_compile=True)(b1, axis=-2) + + # Compute distances for all bits in all paths, set distance to inf + # if the bit does not have the correct value + dists = expand_to_rank(dists, tf.rank(b0), axis=-1) + d0 = tf.where(b0, dists, tf.constant(np.inf, dists.dtype)) + d1 = tf.where(b1, dists, tf.constant(np.inf, dists.dtype)) + + # Compute minimum distance for each bit in each stream + # l0/l1: [batch_size, num_streams, num_bits_per_symbol] + l0 = tf.reduce_min(d0, axis=1) + l1 = tf.reduce_min(d1, axis=1) + + # Compute LLRs + llr = l0-l1 + + # Clip LLRs + llr = tf.clip_by_value(llr, -self.llr_clip_val, self.llr_clip_val) + + return llr + diff --git a/sionna/ofdm/__init__.py b/sionna/ofdm/__init__.py index 7e73f106..ac684a65 100644 --- a/sionna/ofdm/__init__.py +++ b/sionna/ofdm/__init__.py @@ -10,7 +10,7 @@ from .pilot_pattern import PilotPattern, EmptyPilotPattern, KroneckerPilotPattern from .modulator import OFDMModulator from .demodulator import OFDMDemodulator -from .channel_estimation import LSChannelEstimator, NearestNeighborInterpolator, LinearInterpolator -from .equalization import LMMSEEqualizer -from .detection import MaximumLikelihoodDetector, MaximumLikelihoodDetectorWithPrior +from .channel_estimation import LSChannelEstimator, NearestNeighborInterpolator, LinearInterpolator, LMMSEInterpolator, BaseChannelEstimator, BaseChannelInterpolator, tdl_freq_cov_mat, tdl_time_cov_mat +from .equalization import OFDMEqualizer, LMMSEEqualizer, ZFEqualizer, MFEqualizer +from .detection import OFDMDetector, OFDMDetectorWithPrior, MaximumLikelihoodDetector, MaximumLikelihoodDetectorWithPrior, LinearDetector, KBestDetector, EPDetector, MMSEPICDetector from .precoding import ZFPrecoder diff --git a/sionna/ofdm/channel_estimation.py b/sionna/ofdm/channel_estimation.py index 64746db8..7886ed4c 100644 --- a/sionna/ofdm/channel_estimation.py +++ b/sionna/ofdm/channel_estimation.py @@ -7,49 +7,33 @@ import tensorflow as tf from tensorflow.keras.layers import Layer import numpy as np -from sionna.utils import flatten_last_dims, expand_to_rank +from sionna.channel.tr38901 import models +from sionna.utils import flatten_last_dims, expand_to_rank, matrix_inv from sionna.ofdm import ResourceGrid, RemoveNulledSubcarriers - - -class LSChannelEstimator(Layer): +from sionna import PI, SPEED_OF_LIGHT +from scipy.special import jv +import itertools +from abc import ABC, abstractmethod +import json +from importlib_resources import files + +class BaseChannelEstimator(ABC, Layer): # pylint: disable=line-too-long - r"""LSChannelEstimator(resource_grid, interpolation_type="nn", dtype=tf.complex64, **kwargs) - - Layer implementing least-squares (LS) channel estimation for OFDM MIMO systems. - - After LS channel estimation at the pilot positions, the channel estimates - and error variances are interpolated accross the entire resource grid using - a specified interpolation function. - - For simplicity, we describe the underlying algorithm for a vectorized observation, - where we have a nonzero pilot for all elements to be estimated. - The actually implementation works on a full OFDM resource grid with sparse - pilot patterns. We consider the following model: - - .. math:: - - \mathbf{y} = \mathbf{h}\odot\mathbf{p} + \mathbf{n} + r"""BaseChannelEstimator(resource_grid, interpolation_type="nn", interpolator=None, dtype=tf.complex64, **kwargs) - where :math:`\mathbf{y}\in\mathbb{C}^{M}` is the received signal vector, - :math:`\mathbf{p}\in\mathbb{C}^M` is the vector of pilot symbols, - :math:`\mathbf{H}\in\mathbb{C}^{M}` is the channel vector to be estimated, - and :math:`\mathbf{n}\in\mathbb{C}^M` is a zero-mean noise vector whose - elements have variance :math:`N_0`. The operator :math:`\odot` denotes - element-wise multiplication. + Abstract layer for implementing an OFDM channel estimator. - The channel estimate :math:`\hat{\mathbf{h}}` and error variances - :math:`\sigma^2_i`, :math:`i=0,\dots,M-1`, are computed as + Any layer that implements an OFDM channel estimator must implement this + class and its + :meth:`~sionna.ofdm.BaseChannelEstimator.estimate_at_pilot_locations` + abstract method. - .. math:: - - \hat{\mathbf{h}} &= \mathbf{y} \odot - \frac{\mathbf{p}^\star}{\left|\mathbf{p}\right|^2} - = \mathbf{h} + \tilde{\mathbf{h}}\\ - \sigma^2_i &= \mathbb{E}\left[\tilde{h}_i \tilde{h}_i^\star \right] - = \frac{N_0}{\left|p_i\right|^2}. - - The channel estimates and error variances are then interpolated accross - the entire resource grid. + This class extracts the pilots from the received resource grid ``y``, calls + the :meth:`~sionna.ofdm.BaseChannelEstimator.estimate_at_pilot_locations` + method to estimate the channel for the pilot-carrying resource elements, + and then interpolates the channel to compute channel estimates for the + data-carrying resouce elements using the interpolation method specified by + ``interpolation_type`` or the ``interpolator`` object. Parameters ---------- @@ -57,11 +41,21 @@ class LSChannelEstimator(Layer): An instance of :class:`~sionna.ofdm.ResourceGrid`. interpolation_type : One of ["nn", "lin", "lin_time_avg"], string - The interpolation to be used. Currently only the - :class:`~sionna.ofdm.NearestNeighborInterpolator` (`"nn`") and - :class:`~sionna.ofdm.LinearInterpolator` - without (`"lin"`) and with averaging across OFDM - symbols (`"lin_time_avg"`) are supported. + The interpolation method to be used. + It is ignored if ``interpolator`` is not `None`. + Available options are :class:`~sionna.ofdm.NearestNeighborInterpolator` (`"nn`") + or :class:`~sionna.ofdm.LinearInterpolator` without (`"lin"`) or with + averaging across OFDM symbols (`"lin_time_avg"`). + Defaults to "nn". + + interpolator : BaseChannelInterpolator + An instance of :class:`~sionna.ofdm.BaseChannelInterpolator`, + such as :class:`~sionna.ofdm.LMMSEInterpolator`, + or `None`. In the latter case, the interpolator specfied + by ``interpolation_type`` is used. + Otherwise, the ``interpolator`` is used and ``interpolation_type`` + is ignored. + Defaults to `None`. dtype : tf.Dtype Datatype for internal calculations and the output dtype. @@ -73,37 +67,44 @@ class LSChannelEstimator(Layer): Tuple: y : [batch_size, num_rx, num_rx_ant, num_ofdm_symbols,fft_size], tf.complex - The observed signals. + Observed resource grid no : [batch_size, num_rx, num_rx_ant] or only the first n>=0 dims, tf.float - The variance of the AWGN. + Variance of the AWGN Output ------ - h_ls : [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, num_ofdm_symbols,fft_size], tf.complex - The channel estimates accross the entire resource grid for all - transmitters and streams. + h_hat : [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, num_ofdm_symbols,fft_size], tf.complex + Channel estimates accross the entire resource grid for all + transmitters and streams - err_var : Same shape as ``h_ls``, tf.float - The channel estimation error variance accross the entire resource grid - for all transmitters and streams. + err_var : Same shape as ``h_hat``, tf.float + Channel estimation error variance accross the entire resource grid + for all transmitters and streams """ - def __init__(self, resource_grid, interpolation_type="nn", dtype=tf.complex64, **kwargs): + def __init__(self, resource_grid, interpolation_type="nn", interpolator=None, dtype=tf.complex64, **kwargs): super().__init__(dtype=dtype, **kwargs) + assert isinstance(resource_grid, ResourceGrid),\ "You must provide a valid instance of ResourceGrid." self._pilot_pattern = resource_grid.pilot_pattern self._removed_nulled_scs = RemoveNulledSubcarriers(resource_grid) - assert interpolation_type in ["nn", "lin", "lin_time_avg", None], \ + assert interpolation_type in ["nn","lin","lin_time_avg",None], \ "Unsupported `interpolation_type`" self._interpolation_type = interpolation_type - if self._interpolation_type=="nn": + + if interpolator is not None: + assert isinstance(interpolator, BaseChannelInterpolator), \ + "`interpolator` must implement the BaseChannelInterpolator interface" + self._interpol = interpolator + elif self._interpolation_type == "nn": self._interpol = NearestNeighborInterpolator(self._pilot_pattern) - if self._interpolation_type=="lin": + elif self._interpolation_type == "lin": self._interpol = LinearInterpolator(self._pilot_pattern) - if self._interpolation_type=="lin_time_avg": - self._interpol = LinearInterpolator(self._pilot_pattern, time_avg=True) + elif self._interpolation_type == "lin_time_avg": + self._interpol = LinearInterpolator(self._pilot_pattern, + time_avg=True) # Precompute indices to gather received pilot signals num_pilot_symbols = self._pilot_pattern.num_pilot_symbols @@ -111,19 +112,43 @@ def __init__(self, resource_grid, interpolation_type="nn", dtype=tf.complex64, * pilot_ind = tf.argsort(mask, axis=-1, direction="DESCENDING") self._pilot_ind = pilot_ind[...,:num_pilot_symbols] - def call(self, inputs): + @abstractmethod + def estimate_at_pilot_locations(self, y_pilots, no): """ - y_ has shape - [batch_size, num_rx, num_rx_ant, num_ofdm_symbols, fft_size] - no has a shape that can be broadcast to the shape [num_tx, num_streams,] + Estimates the channel for the pilot-carrying resource elements. + + This is an abstract method that must be implemented by a concrete + OFDM channel estimator that implement this class. + + Input + ----- + y_pilots : [batch_size, num_rx, num_rx_ant, num_tx, num_streams, num_pilot_symbols], tf.complex + Observed signals for the pilot-carrying resource elements + + no : [batch_size, num_rx, num_rx_ant] or only the first n>=0 dims, tf.float + Variance of the AWGN + + Output + ------ + h_hat : [batch_size, num_rx, num_rx_ant, num_tx, num_streams, num_pilot_symbols], tf.complex + Channel estimates for the pilot-carrying resource elements + + err_var : Same shape as ``h_hat``, tf.float + Channel estimation error variance for the pilot-carrying + resource elements """ + pass + + def call(self, inputs): + + y, no = inputs + # y has shape: # [batch_size, num_rx, num_rx_ant, num_ofdm_symbols,.. # ... fft_size] # # no can have shapes [], [batch_size], [batch_size, num_rx] # or [batch_size, num_rx, num_rx_ant] - y, no = inputs # Removed nulled subcarriers (guards, dc) y_eff = self._removed_nulled_scs(y) @@ -138,6 +163,120 @@ def call(self, inputs): # ..., num_pilot_symbols] y_pilots = tf.gather(y_eff_flat, self._pilot_ind, axis=-1) + # Compute LS channel estimates + # Note: Some might be Inf because pilots=0, but we do not care + # as only the valid estimates will be considered during interpolation. + # We do a save division to replace Inf by 0. + # Broadcasting from pilots here is automatic since pilots have shape + # [num_tx, num_streams, num_pilot_symbols] + h_hat, err_var = self.estimate_at_pilot_locations(y_pilots, no) + + # Interpolate channel estimates over the resource grid + if self._interpolation_type is not None: + h_hat, err_var = self._interpol(h_hat, err_var) + err_var = tf.maximum(err_var, tf.cast(0, err_var.dtype)) + + return h_hat, err_var + + +class LSChannelEstimator(BaseChannelEstimator, Layer): + # pylint: disable=line-too-long + r"""LSChannelEstimator(resource_grid, interpolation_type="nn", interpolator=None, dtype=tf.complex64, **kwargs) + + Layer implementing least-squares (LS) channel estimation for OFDM MIMO systems. + + After LS channel estimation at the pilot positions, the channel estimates + and error variances are interpolated accross the entire resource grid using + a specified interpolation function. + + For simplicity, the underlying algorithm is described for a vectorized observation, + where we have a nonzero pilot for all elements to be estimated. + The actual implementation works on a full OFDM resource grid with sparse + pilot patterns. The following model is assumed: + + .. math:: + + \mathbf{y} = \mathbf{h}\odot\mathbf{p} + \mathbf{n} + + where :math:`\mathbf{y}\in\mathbb{C}^{M}` is the received signal vector, + :math:`\mathbf{p}\in\mathbb{C}^M` is the vector of pilot symbols, + :math:`\mathbf{h}\in\mathbb{C}^{M}` is the channel vector to be estimated, + and :math:`\mathbf{n}\in\mathbb{C}^M` is a zero-mean noise vector whose + elements have variance :math:`N_0`. The operator :math:`\odot` denotes + element-wise multiplication. + + The channel estimate :math:`\hat{\mathbf{h}}` and error variances + :math:`\sigma^2_i`, :math:`i=0,\dots,M-1`, are computed as + + .. math:: + + \hat{\mathbf{h}} &= \mathbf{y} \odot + \frac{\mathbf{p}^\star}{\left|\mathbf{p}\right|^2} + = \mathbf{h} + \tilde{\mathbf{h}}\\ + \sigma^2_i &= \mathbb{E}\left[\tilde{h}_i \tilde{h}_i^\star \right] + = \frac{N_0}{\left|p_i\right|^2}. + + The channel estimates and error variances are then interpolated accross + the entire resource grid. + + Parameters + ---------- + resource_grid : ResourceGrid + An instance of :class:`~sionna.ofdm.ResourceGrid`. + + interpolation_type : One of ["nn", "lin", "lin_time_avg"], string + The interpolation method to be used. + It is ignored if ``interpolator`` is not `None`. + Available options are :class:`~sionna.ofdm.NearestNeighborInterpolator` (`"nn`") + or :class:`~sionna.ofdm.LinearInterpolator` without (`"lin"`) or with + averaging across OFDM symbols (`"lin_time_avg"`). + Defaults to "nn". + + interpolator : BaseChannelInterpolator + An instance of :class:`~sionna.ofdm.BaseChannelInterpolator`, + such as :class:`~sionna.ofdm.LMMSEInterpolator`, + or `None`. In the latter case, the interpolator specfied + by ``interpolation_type`` is used. + Otherwise, the ``interpolator`` is used and ``interpolation_type`` + is ignored. + Defaults to `None`. + + dtype : tf.Dtype + Datatype for internal calculations and the output dtype. + Defaults to `tf.complex64`. + + Input + ----- + (y, no) : + Tuple: + + y : [batch_size, num_rx, num_rx_ant, num_ofdm_symbols,fft_size], tf.complex + Observed resource grid + + no : [batch_size, num_rx, num_rx_ant] or only the first n>=0 dims, tf.float + Variance of the AWGN + + Output + ------ + h_ls : [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, num_ofdm_symbols,fft_size], tf.complex + Channel estimates accross the entire resource grid for all + transmitters and streams + + err_var : Same shape as ``h_ls``, tf.float + Channel estimation error variance accross the entire resource grid + for all transmitters and streams + """ + + def estimate_at_pilot_locations(self, y_pilots, no): + + # y_pilots : [batch_size, num_rx, num_rx_ant, num_tx, num_streams, + # num_pilot_symbols], tf.complex + # The observed signals for the pilot-carrying resource elements. + + # no : [batch_size, num_rx, num_rx_ant] or only the first n>=0 dims, + # tf.float + # The variance of the AWGN. + # Compute LS channel estimates # Note: Some might be Inf because pilots=0, but we do not care # as only the valid estimates will be considered during interpolation. @@ -156,21 +295,56 @@ def call(self, inputs): # Compute error variance, broadcastable to the shape of h_ls err_var = tf.math.divide_no_nan(no, tf.abs(pilots)**2) - # Interpolate channel estimates over the resource grid - if self._interpolation_type is not None: - h_ls = self._interpol(h_ls) - err_var = tf.maximum(self._interpol(err_var), - tf.cast(0, err_var.dtype)) - return h_ls, err_var -class NearestNeighborInterpolator(): + +class BaseChannelInterpolator(ABC): # pylint: disable=line-too-long - """Nearest-neighbor channel estimate interpolation on a resource grid. + r"""BaseChannelInterpolator() + + Abstract layer for implementing an OFDM channel interpolator. + + Any layer that implements an OFDM channel interpolator must implement this + callable class. + + A channel interpolator is used by an OFDM channel estimator + (:class:`~sionna.ofdm.BaseChannelEstimator`) to compute channel estimates + for the data-carrying resource elements from the channel estimates for the + pilot-carrying resource elements. + + Input + ----- + h_hat : [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, num_pilot_symbols], tf.complex + Channel estimates for the pilot-carrying resource elements + + err_var : [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, num_pilot_symbols], tf.complex + Channel estimation error variances for the pilot-carrying resource elements + + Output + ------ + h_hat : [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, num_ofdm_symbols, fft_size], tf.complex + Channel estimates accross the entire resource grid for all + transmitters and streams + + err_var : Same shape as ``h_hat``, tf.float + Channel estimation error variance accross the entire resource grid + for all transmitters and streams + """ + + @abstractmethod + def __call__(self, h_hat, err_var): + pass + + +class NearestNeighborInterpolator(BaseChannelInterpolator): + # pylint: disable=line-too-long + r"""NearestNeighborInterpolator(pilot_pattern) + + Nearest-neighbor channel estimate interpolation on a resource grid. This class assigns to each element of an OFDM resource grid one of - ``num_pilots`` provided measurements, e.g., channel estimates or error - variances, according to the nearest neighbor method. It is assumed + ``num_pilots`` provided channel estimates and error + variances according to the nearest neighbor method. It is assumed that the measurements were taken at the nonzero positions of a :class:`~sionna.ofdm.PilotPattern`. @@ -184,19 +358,25 @@ class NearestNeighborInterpolator(): Parameters ---------- pilot_pattern : PilotPattern - An instance of :class:`~sionna.ofdm.PilotPattern`. + An instance of :class:`~sionna.ofdm.PilotPattern` Input ----- - : [k, l ,m, num_tx, num_streams_per_tx, num_pilot_symbols], tf.DType - Tensor of quantities to be interpolated according to - a :class:`~sionna.ofdm.PilotPattern`. This can be channel estimates - as well as the related error variances. + h_hat : [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, num_pilot_symbols], tf.complex + Channel estimates for the pilot-carrying resource elements + + err_var : [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, num_pilot_symbols], tf.complex + Channel estimation error variances for the pilot-carrying resource elements Output ------ - : [k, l, m, num_tx, num_streams_per_tx, num_ofdm_symbols, num_effective_subcarriers], tf.complex - The interpolated input tensor. + h_hat : [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, num_ofdm_symbols, fft_size], tf.complex + Channel estimates accross the entire resource grid for all + transmitters and streams + + err_var : Same shape as ``h_hat``, tf.float + Channel estimation error variances accross the entire resource grid + for all transmitters and streams """ def __init__(self, pilot_pattern): super().__init__() @@ -243,7 +423,7 @@ def __init__(self, pilot_pattern): # ..., num_effective_subcarriers] self._gather_ind = tf.reshape(gather_ind, mask_shape) - def __call__(self, inputs): + def _interpolate(self, inputs): # inputs has shape: # [k, l, m, num_tx, num_streams_per_tx, num_pilots] @@ -265,14 +445,22 @@ def __call__(self, inputs): return outputs + def __call__(self, h_hat, err_var): + + h_hat = self._interpolate(h_hat) + err_var = self._interpolate(err_var) + return h_hat, err_var -class LinearInterpolator(): + +class LinearInterpolator(BaseChannelInterpolator): # pylint: disable=line-too-long - r"""Linear channel estimate interpolation on a resource grid. + r"""LinearInterpolator(pilot_pattern, time_avg=False) + + Linear channel estimate interpolation on a resource grid. This class computes for each element of an OFDM resource grid - a channel estimate based on ``num_pilots`` provided measurements, - e.g., channel estimates or error variances, through linear interpolation. + a channel estimate based on ``num_pilots`` provided channel estimates and + error variances through linear interpolation. It is assumed that the measurements were taken at the nonzero positions of a :class:`~sionna.ofdm.PilotPattern`. @@ -282,7 +470,7 @@ class LinearInterpolator(): Parameters ---------- pilot_pattern : PilotPattern - An instance of :class:`~sionna.ofdm.PilotPattern`. + An instance of :class:`~sionna.ofdm.PilotPattern` time_avg : bool If enabled, measurements will be averaged across OFDM symbols @@ -291,15 +479,21 @@ class LinearInterpolator(): Input ----- - : [k, l ,m, num_tx, num_streams_per_tx, num_pilot_symbols], tf.DType - Tensor of quantities to be interpolated according to - a :class:`~sionna.ofdm.PilotPattern`. This can be channel estimates - as well as the related error variances. + h_hat : [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, num_pilot_symbols], tf.complex + Channel estimates for the pilot-carrying resource elements + + err_var : [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, num_pilot_symbols], tf.complex + Channel estimation error variances for the pilot-carrying resource elements Output ------ - : [k, l, m, num_tx, num_streams_per_tx, num_ofdm_symbols, num_effective_subcarriers], tf.complex - The interpolated input tensor. + h_hat : [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, num_ofdm_symbols, fft_size], tf.complex + Channel estimates accross the entire resource grid for all + transmitters and streams + + err_var : Same shape as ``h_hat``, tf.float + Channel estimation error variances accross the entire resource grid + for all transmitters and streams """ def __init__(self, pilot_pattern, time_avg=False): super().__init__() @@ -480,7 +674,7 @@ def __init__(self, pilot_pattern, time_avg=False): self._num_pilot_ofdm_symbols = expand_to_rank(n, 7, axis=0) - def _interpolate(self, inputs, x, x0, x1, y0_ind, y1_ind): + def _interpolate_1d(self, inputs, x, x0, x1, y0_ind, y1_ind): # Gather the right values for y0 and y1 y0 = tf.gather(inputs, y0_ind, axis=2, batch_dims=2) y1 = tf.gather(inputs, y1_ind, axis=2, batch_dims=2) @@ -493,7 +687,7 @@ def _interpolate(self, inputs, x, x0, x1, y0_ind, y1_ind): slope = tf.math.divide_no_nan(y1-y0, tf.cast(x1-x0, dtype=y0.dtype)) return tf.cast(x-x0, dtype=y0.dtype)*slope + y0 - def __call__(self, inputs): + def _interpolate(self, inputs): # # Prepare inputs # @@ -514,12 +708,12 @@ def __call__(self, inputs): # h_hat_freq has shape: # [k, l, m, num_tx, num_streams_per_tx, num_ofdm_symbols,... # ...num_effective_subcarriers] - h_hat_freq = self._interpolate(inputs, - self._x_freq, - self._x_0_freq, - self._x_1_freq, - self._y_0_freq_ind, - self._y_1_freq_ind) + h_hat_freq = self._interpolate_1d(inputs, + self._x_freq, + self._x_0_freq, + self._x_1_freq, + self._y_0_freq_ind, + self._y_1_freq_ind) # # Time-domain interpolation # @@ -528,7 +722,7 @@ def __call__(self, inputs): if self._time_avg: num_ofdm_symbols = h_hat_freq.shape[-2] h_hat_freq = tf.reduce_sum(h_hat_freq, axis=-2, keepdims=True) - h_hat_freq /= tf.cast(self._num_pilot_ofdm_symbols, h_hat_freq.dtype) + h_hat_freq /= tf.cast(self._num_pilot_ofdm_symbols,h_hat_freq.dtype) h_hat_freq = tf.repeat(h_hat_freq, [num_ofdm_symbols], axis=-2) # Transpose h_hat_freq to bring batch_dims for gather last. New shape: @@ -539,11 +733,1362 @@ def __call__(self, inputs): # h_hat_time has shape: # [k, l, m, num_tx, num_streams_per_tx, num_ofdm_symbols,... # ...num_effective_subcarriers] - h_hat_time = self._interpolate(h_hat_time, - self._x_time, - self._x_0_time, - self._x_1_time, - self._y_0_time_ind, - self._y_1_time_ind) + h_hat_time = self._interpolate_1d(h_hat_time, + self._x_time, + self._x_0_time, + self._x_1_time, + self._y_0_time_ind, + self._y_1_time_ind) return h_hat_time + + def __call__(self, h_hat, err_var): + + h_hat = self._interpolate(h_hat) + err_var = self._interpolate(err_var) + return h_hat, err_var + + +class LMMSEInterpolator1D: + # pylint: disable=line-too-long + r"""LMMSEInterpolator1D(pilot_mask, cov_mat) + + This class performs the linear interpolation across the inner dimension of the input ``h_hat``. + + The two inner dimensions of the input ``h_hat`` form a matrix :math:`\hat{\mathbf{H}} \in \mathbb{C}^{N \times M}`. + LMMSE interpolation is performed across the inner dimension as follows: + + .. math:: + \tilde{\mathbf{h}}_n = \mathbf{A}_n \hat{\mathbf{h}}_n + + where :math:`1 \leq n \leq N` and :math:`\hat{\mathbf{h}}_n` is + the :math:`n^{\text{th}}` (transposed) row of :math:`\hat{\mathbf{H}}`. + :math:`\mathbf{A}_n` is the :math:`M \times M` interpolation LMMSE matrix: + + .. math:: + \mathbf{A}_n = \mathbf{R} \mathbf{\Pi}_n \left( \mathbf{\Pi}_n^\intercal \mathbf{R} \mathbf{\Pi}_n + \tilde{\mathbf{\Sigma}}_n \right)^{-1} \mathbf{\Pi}_n^\intercal. + + where :math:`\mathbf{R}` is the :math:`M \times M` covariance matrix across the inner dimension of the quantity which is estimated, + :math:`\mathbf{\Pi}_n` the :math:`M \times K_n` matrix that spreads :math:`K_n` + values to a vector of size :math:`M` according to the ``pilot_mask`` for the :math:`n^{\text{th}}` row, + and :math:`\tilde{\mathbf{\Sigma}}_n \in \mathbb{R}^{K_n \times K_n}` is the regularized channel estimation error covariance. + The :math:`i^{\text{th}}`` diagonal element of :math:`\tilde{\mathbf{\Sigma}}_n` is such that: + + .. math:: + + \left[ \tilde{\mathbf{\Sigma}}_n \right]_{i,i} = \text{max} \left\{ \right\} + + built from ``err_var`` and assumed to be diagonal. + + The returned channel estimates are + + .. math:: + \begin{bmatrix} + {\tilde{\mathbf{h}}_1}^\intercal\\ + \vdots\\ + {\tilde{\mathbf{h}}_N}^\intercal + \end{bmatrix}. + + The returned channel estimation error variances are the diaginal coefficients of + + .. math:: + \text{diag} \left( \mathbf{R} - \mathbf{A}_n \mathbf{\Xi}_n \mathbf{R} \right), 1 \leq n \leq N + + where :math:`\mathbf{\Xi}_n` is the diagonal matrix of size :math:`M \times M` that zeros the + columns corresponding to rows not carrying any pilots. + Note that interpolation is not performed for rows not carrying any pilots. + + **Remark**: The interpolation matrix differs across rows as different + rows may carry pilots on different elements and/or have different + estimation error variances. + + Parameters + ---------- + pilot_mask : [:math:`N`, :math:`M`] : int + Mask indicating the allocation of resource elements. + 0 : Data, + 1 : Pilot, + 2 : Not used, + + cov_mat : [:math:`M`, :math:`M`], tf.complex + Covariance matrix of the channel across the inner dimension. + + last_step : bool + Set to `True` if this is the last interpolation step. + Otherwise, set to `False`. + If `True`, the the output is scaled to ensure its variance is as expected + by the following interpolation step. + + Input + ----- + h_hat : [batch_size, num_rx, num_rx_ant, num_tx, :math:`N`, :math:`M`], tf.complex + Channel estimates. + + err_var : [batch_size, num_rx, num_rx_ant, num_tx, :math:`N`, :math:`M`], tf.complex + Channel estimation error variances. + + Output + ------ + h_hat : [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, :math:`N`, :math:`M`], tf.complex + Channel estimates interpolated across the inner dimension. + + err_var : Same shape as ``h_hat``, tf.float + The channel estimation error variances of the interpolated channel estimates. + """ + + def __init__(self, pilot_mask, cov_mat, last_step): + + self._cdtype = cov_mat.dtype + assert self._cdtype in (tf.complex64, tf.complex128),\ + "`cov_mat` dtype must be one of tf.complex64 or tf.complex128" + self._rdtype = self._cdtype.real_dtype + self._rzero = tf.constant(0.0, self._rdtype) + + # Interpolation is performed along the inner dimension of + # the resource grid, which may be either the subcarriers + # or the OFDM symbols dimension. + # This dimension is referred to as the inner dimension. + # The other dimension of the resource grid is referred to + # as the outer dimension. + + # Size of the inner dimension. + inner_dim_size = tf.shape(pilot_mask)[-1] + self._inner_dim_size = inner_dim_size + + # Size of the outer dimension. + outer_dim_size = tf.shape(pilot_mask)[-2] + self._outer_dim_size = outer_dim_size + + self._cov_mat = cov_mat + self._last_step = last_step + + # Computation of the interpolation matrix is done solving the + # least-square problem: + # + # X = min_Z |AZ - B|_F^2 + # + # where A = (\Pi_T R \Pi + S) and + # B = R \Pi + # where R is the channel covariance matrix, S the error + # diagonal covariance matrix, and \Pi the matrix that spreads the pilots + # according to the pilot pattern along the inner axis. + + # Extracting the locations of pilots from the pilot mask + num_tx = tf.shape(pilot_mask)[0] + num_streams_per_tx = tf.shape(pilot_mask)[1] + + # List of indices of pilots in the inner dimension for every + # transmit antenna, stream, and outer dimension element. + pilot_indices = [] + # Maximum number of pilots carried by an inner dimension. + max_num_pil = 0 + # Indices used to add the error variance to the diagonal + # elements of the covariance matrix restricted + # to the elements carrying pilots. + # These matrices are computed below. + add_err_var_indices = np.zeros([num_tx, num_streams_per_tx, + outer_dim_size, inner_dim_size, 5], int) + for tx in range(num_tx): + pilot_indices.append([]) + for st in range(num_streams_per_tx): + pilot_indices[-1].append([]) + for oi in range(outer_dim_size): + pilot_indices[-1][-1].append([]) + num_pil = 0 # Number of pilots on this outer dim + for ii in range(inner_dim_size): + # Check if this RE is carrying a pilot + # for this stream + if pilot_mask[tx,st,oi,ii] == 0: + continue + if pilot_mask[tx,st,oi,ii] == 1: + pilot_indices[tx][st][oi].append(ii) + indices = [tx, st, oi, num_pil, num_pil] + add_err_var_indices[tx, st, oi, ii] = indices + num_pil += 1 + if num_pil > max_num_pil: + max_num_pil = num_pil + # [num_tx, num_streams_per_tx, outer_dim_size, inner_dim_size, 5] + self._add_err_var_indices = tf.cast(add_err_var_indices, tf.int32) + + # Different subcarriers/symbols may carry a different number of pilots. + # To handle such cases, we create a tensor of square matrices of + # size the maximum number of pilots carried by an inner dimension + # and zero-padding is used to handle axes with less pilots than the + # maximum value. The obtained structure is: + # + # |B 0| + # |0 0| + # + pil_cov_mat = np.zeros([num_tx, num_streams_per_tx, outer_dim_size, + max_num_pil, max_num_pil], complex) + for tx,st,oi in itertools.product(range(num_tx), + range(num_streams_per_tx), + range(outer_dim_size)): + pil_ind = pilot_indices[tx][st][oi] + num_pil = len(pil_ind) + tmp = np.take(cov_mat, pil_ind, axis=0) + pil_cov_mat_ = np.take(tmp, pil_ind, axis=1) + pil_cov_mat[tx,st,oi,:num_pil,:num_pil] = pil_cov_mat_ + # [num_tx, num_streams_per_tx, outer_dim_size, max_num_pil, max_num_pil] + self._pil_cov_mat = tf.constant(pil_cov_mat, self._cdtype) + + # Pre-compute the covariance matrix with only the columns corresponding + # to pilots. + b_mat = np.zeros([num_tx, num_streams_per_tx, outer_dim_size, + max_num_pil, inner_dim_size], complex) + for tx,st,oi in itertools.product(range(num_tx), + range(num_streams_per_tx), + range(outer_dim_size)): + pil_ind = pilot_indices[tx][st][oi] + num_pil = len(pil_ind) + b_mat_ = np.take(cov_mat, pil_ind, axis=0) + b_mat[tx,st,oi,:num_pil,:] = b_mat_ + self._b_mat = tf.constant(b_mat, self._cdtype) + + # Indices used to fill with zeros the columns of the interpolation + # matrix not corresponding to zeros. + # The results is a matrix of size inner_dim_size x inner_dim_size + # where rows and columns not correspondong to pilots are set to zero. + pil_loc = np.zeros([num_tx, num_streams_per_tx, outer_dim_size, + inner_dim_size, max_num_pil, 5], dtype=int) + for tx,st,oi,p,ii in itertools.product(range(num_tx), + range(num_streams_per_tx), + range(outer_dim_size), + range(max_num_pil), + range(inner_dim_size)): + if p >= len(pilot_indices[tx][st][oi]): + # An extra dummy subcarrier is added to push there padding + # identity matrix + pil_loc[tx, st, oi, ii, p] = [tx, st, oi, + inner_dim_size, + inner_dim_size] + else: + pil_loc[tx, st, oi, ii, p] = [tx, st, oi, + ii, + pilot_indices[tx][st][oi][p]] + self._pil_loc = tf.cast(pil_loc, tf.int32) + + # Covariance matrix for each stream with only the row corresponding + # to a pilot carrying RE not set to 0. + # This is required to compute the estimation error variances. + err_var_mat = np.zeros([num_tx, num_streams_per_tx, outer_dim_size, + inner_dim_size, inner_dim_size], complex) + for tx,st,oi in itertools.product(range(num_tx), + range(num_streams_per_tx), + range(outer_dim_size)): + pil_ind = pilot_indices[tx][st][oi] + mask = np.zeros([inner_dim_size], complex) + mask[pil_ind] = 1.0 + mask = np.expand_dims(mask, axis=1) + err_var_mat[tx,st,oi] = cov_mat*mask + self._err_var_mat = tf.constant(err_var_mat, self._cdtype) + + def __call__(self, h_hat, err_var): + + # h_hat : [batch_size, num_rx, num_rx_ant, num_tx, + # num_streams_per_tx, outer_dim_size, inner_dim_size] + # err_var : [batch_size, num_rx, num_rx_ant, num_tx, + # num_streams_per_tx, outer_dim_size, inner_dim_size] + + batch_size = tf.shape(h_hat)[0] + num_rx = tf.shape(h_hat)[1] + num_rx_ant = tf.shape(h_hat)[2] + num_tx = tf.shape(h_hat)[3] + num_tx_stream = tf.shape(h_hat)[4] + outer_dim_size = self._outer_dim_size + inner_dim_size = self._inner_dim_size + + ##################################### + # Compute the interpolation matrix + ##################################### + + # Computation of the interpolation matrix is done solving the + # least-square problem: + # + # X = min_Z |AZ - B|_F^2 + # + # where A = (\Pi_T R \Pi + S) and + # B = R \Pi + # where R is the channel covariance matrix, S the error + # diagonal covariance matrix, and \Pi the matrix that spreads the pilots + # according to the pilot pattern along the inner axis. + + # + # Computing A + # + + # Covariance matrices restricted to pilot locations + # [num_tx, num_streams_per_tx, outer_dim_size, max_num_pil, max_num_pil] + pil_cov_mat = self._pil_cov_mat + + # Adding batch, receive, and receive antennas dimensions to the + # covariance matrices restricted to pilot locations and to the + # regularization values + # [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, + # outer_dim_size, max_num_pil, max_num_pil] + pil_cov_mat = expand_to_rank(pil_cov_mat, 8, 0) + pil_cov_mat = tf.tile(pil_cov_mat, [batch_size, num_rx, num_rx_ant, + 1, 1, 1, 1, 1]) + + # Adding the noise variance to the covariance matrices restricted to + # pilots + # [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, + # outer_dim_size, max_num_pil, max_num_pil] + pil_cov_mat_ = tf.transpose(pil_cov_mat, [3, 4, 5, 6, 7, 0, 1, 2]) + err_var_ = tf.complex(err_var, self._rzero) + err_var_ = tf.transpose(err_var_, [3, 4, 5, 6, 0, 1, 2]) + a_mat = tf.tensor_scatter_nd_add(pil_cov_mat_, + self._add_err_var_indices, err_var_) + a_mat = tf.transpose(a_mat, [5, 6, 7, 0, 1, 2, 3, 4]) + + # + # Computing B + # + + # B is pre-computed as it only depend on the channel covariance and + # pilot pattern. + # [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, + # outer_dim_size, max_num_pil, inner_dim_size] + b_mat = self._b_mat + b_mat = expand_to_rank(b_mat, 8, 0) + b_mat = tf.tile(b_mat, [batch_size, num_rx, num_rx_ant, + 1, 1, 1, 1, 1]) + + # + # Computing the interpolation matrix + # + + # Using lstsq to compute the columns of the interpolation matrix + # corresponding to pilots. + # [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, + # outer_dim_size, inner_dim_size, max_num_pil] + ext_mat = tf.linalg.lstsq(a_mat, b_mat, fast=False) + ext_mat = tf.transpose(ext_mat, [0,1,2,3,4,5,7,6], conjugate=True) + + # Filling with zeros the columns not corresponding to pilots. + # An extra dummy outer dim is added to scatter there the coefficients + # of the identity matrix used for padding. + # This dummy dim is then removed. + # [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, + # outer_dim_size, inner_dim_size, inner_dim_size] + ext_mat = tf.transpose(ext_mat, [3, 4, 5, 6, 7, 0, 1, 2]) + ext_mat = tf.scatter_nd(self._pil_loc, ext_mat, + [num_tx, num_tx_stream, + outer_dim_size, + inner_dim_size+1, + inner_dim_size+1, + batch_size, num_rx, num_rx_ant]) + ext_mat = tf.transpose(ext_mat, [5, 6, 7, 0, 1, 2, 3, 4]) + ext_mat = ext_mat[...,:inner_dim_size,:inner_dim_size] + + ################################################ + # Apply interpolation over the inner dimension + ################################################ + + # [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, + # outer_dim_size, inner_dim_size] + h_hat = tf.expand_dims(h_hat, axis=-1) + h_hat = tf.matmul(ext_mat, h_hat) + h_hat = tf.squeeze(h_hat, axis=-1) + + ############################## + # Compute the error variances + ############################## + + # Keep track of the previous estimation error variances for later use + # [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, + # outer_dim_size, inner_dim_size] + err_var_old = err_var + + # [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, + # outer_dim_size, inner_dim_size] + cov_mat = expand_to_rank(self._cov_mat, 8, 0) + err_var = tf.linalg.diag_part(cov_mat) + err_var_mat = expand_to_rank(self._err_var_mat, 8, 0) + err_var_mat = tf.transpose(err_var_mat, [0, 1, 2, 3, 4, 5, 7, 6]) + err_var = err_var - tf.reduce_sum(ext_mat*err_var_mat, axis=-1) + err_var = tf.math.real(err_var) + err_var = tf.maximum(err_var, self._rzero) + + ##################################### + # If this is *not* the last + # interpolation step, scales the + # input `h_hat` to ensure + # it has the variance expected by the + # next interpolation step. + # + # The error variance also `err_var` + # is updated accordingly. + ##################################### + if not self._last_step: + # + # Variance of h_hat + # + # Conjugate transpose of LMMSE matrix + # [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, + # outer_dim_size, inner_dim_size, inner_dim_size] + ext_mat_h = tf.transpose(ext_mat, [0, 1, 2, 3, 4, 5, 7, 6], + conjugate=True) + # First part of the estimate covariance + # [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, + # outer_dim_size, inner_dim_size, inner_dim_size] + h_hat_var_1 = tf.matmul(cov_mat, ext_mat_h) + h_hat_var_1 = tf.transpose(h_hat_var_1, [0, 1, 2, 3, 4, 5, 7, 6]) + # [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, + # outer_dim_size, inner_dim_size] + h_hat_var_1 = tf.reduce_sum(ext_mat*h_hat_var_1, axis=-1) + # Second part of the estimate covariance + # [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, + # outer_dim_size, inner_dim_size] + err_var_old_c = tf.complex(err_var_old, self._rzero) + err_var_old_c = tf.expand_dims(err_var_old_c, axis=-1) + h_hat_var_2 = err_var_old_c*ext_mat_h + h_hat_var_2 = tf.transpose(h_hat_var_2, [0, 1, 2, 3, 4, 5, 7, 6]) + h_hat_var_2 = tf.reduce_sum(ext_mat*h_hat_var_2, axis=-1) + # Variance of h_hat + # [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, + # outer_dim_size, inner_dim_size] + h_hat_var = h_hat_var_1 + h_hat_var_2 + # Scaling factor + # [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, + # outer_dim_size, inner_dim_size] + err_var_c = tf.complex(err_var, self._rzero) + h_var = tf.linalg.diag_part(cov_mat) + s = tf.math.divide_no_nan(2.*h_var, h_hat_var + h_var - err_var_c) + # Apply scaling to estimate + # [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, + # outer_dim_size, inner_dim_size] + h_hat = s*h_hat + # Updated variance + # [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, + # outer_dim_size, inner_dim_size] + err_var = s*(s-1.)*h_hat_var + (1.-s)*h_var + s*err_var_c + err_var = tf.math.real(err_var) + err_var = tf.maximum(err_var, self._rzero) + + return h_hat, err_var + +class SpatialChannelFilter: + # pylint: disable=line-too-long + r"""SpatialChannelFilter(cov_mat, last_step) + + Implements linear minimum mean square error (LMMSE) smoothing. + + We consider the following model: + + .. math:: + + \mathbf{y} = \mathbf{h} + \mathbf{n} + + where :math:`\mathbf{y}\in\mathbb{C}^{M}` is the received signal vector, + :math:`\mathbf{h}\in\mathbb{C}^{M}` is the channel vector to be estimated + with covariance matrix + :math:`\mathbb{E}\left[ \mathbf{h} \mathbf{h}^{\mathsf{H}} \right] = \mathbf{R}`, + and :math:`\mathbf{n}\in\mathbb{C}^M` is a zero-mean noise vector whose + elements have variance :math:`N_0`. + + The channel estimate :math:`\hat{\mathbf{h}}` is computed as + + .. math:: + + \hat{\mathbf{h}} &= \mathbf{A} \mathbf{y} + + where + + .. math:: + + \mathbf{A} = \mathbf{R} \left( \mathbf{R} + N_0 \mathbf{I}_M \right)^{-1} + + where :math:`\mathbf{I}_M` is the :math:`M \times M` identity matrix. + The estimation error is: + + .. math:: + + \tilde{h} = \mathbf{h} - \hat{\mathbf{h}} + + The error variances + + .. math:: + + \sigma^2_i = \mathbb{E}\left[\tilde{h}_i \tilde{h}_i^\star \right], 0 \leq i \leq M-1 + + are the diagonal elements of + + .. math:: + + \mathbb{E}\left[\mathbf{\tilde{h}} \mathbf{\tilde{h}}^{\mathsf{H}} \right] = \mathbf{R} - \mathbf{A}\mathbf{R}. + + + Note + ---- + If you want to use this function in Graph mode with XLA, i.e., within + a function that is decorated with ``@tf.function(jit_compile=True)``, + you must set ``sionna.Config.xla_compat=true``. + See :py:attr:`~sionna.Config.xla_compat`. + + Parameters + ---------- + cov_mat : [num_rx_ant, num_rx_ant], tf.complex + Spatial covariance matrix of the channel + + last_step : bool + Set to `True` if this is the last interpolation step. + Otherwise, set to `False`. + If `True`, the the output is scaled to ensure its variance is as expected + by the following interpolation step. + + Input + ----- + h_hat : [batch_size, num_rx, num_tx, num_streams_per_tx, num_ofdm_symbols, num_subcarriers, num_rx_ant], tf.complex + Channel estimates. + + err_var : [batch_size, num_rx, num_tx, num_streams_per_tx, num_ofdm_symbols, num_subcarriers, num_rx_ant], tf.float + Channel estimation error variances. + + Output + ------ + h_hat : [batch_size, num_rx, num_tx, num_streams_per_tx, num_ofdm_symbols, num_subcarriers, num_rx_ant], tf.complex + Channel estimates smoothed accross the spatial dimension + + err_var : [batch_size, num_rx, num_tx, num_streams_per_tx, num_ofdm_symbols, num_subcarriers, num_rx_ant], tf.float + The channel estimation error variances of the smoothed channel estimates. + """ + + def __init__(self, cov_mat, last_step): + self._rzero = tf.zeros((), cov_mat.dtype.real_dtype) + self._cov_mat = cov_mat + self._last_step = last_step + + # Indices for adding a tensor of vectors [..., num_rx_ant] to the + # diagonal of a tensor of matrices [..., num_rx_ant, num_rx_ant] + num_rx_ant = cov_mat.shape[0] + add_diag_indices = [[rxa, rxa] for rxa in range(num_rx_ant)] + self._add_diag_indices = tf.cast(add_diag_indices, tf.int32) + + def __call__(self, h_hat, err_var): + # h_hat : [batch_size, num_rx, num_tx, num_streams_per_tx, + # num_ofdm_symbols, num_subcarriers, num_rx_ant] + # err_var : [batch_size, num_rx, num_tx, num_streams_per_tx, + # num_ofdm_symbols, num_subcarriers, num_rx_ant] + + # [..., num_rx_ant] + err_var = tf.complex(err_var, self._rzero) + # Keep track of the previous estimation error variances for later use + err_var_old = err_var + + # [num_rx_ant, num_rx_ant] + cov_mat = self._cov_mat + cov_mat_t = tf.transpose(cov_mat) + num_rx_ant = tf.shape(cov_mat)[0] + + ########################################## + # Compute LMMSE matrix + ########################################## + + # [..., num_rx_ant, num_rx_ant] + cov_mat = expand_to_rank(cov_mat, tf.rank(err_var)+1, axis=0) + + # Adding the error variances to the diagonal + # [..., num_rx_ant, num_rx_ant] + lmmse_mat = tf.broadcast_to(cov_mat, tf.concat([tf.shape(err_var), + [num_rx_ant]], axis=0)) + # [num_rx_ant, ...] + err_var_ = tf.transpose(err_var, [6, 0, 1, 2, 3, 4, 5]) + # [num_rx_ant, num_rx_ant, ...] + lmmse_mat = tf.transpose(lmmse_mat, [6, 7, 0, 1, 2, 3, 4, 5]) + lmmse_mat = tf.tensor_scatter_nd_add(lmmse_mat, + self._add_diag_indices, err_var_) + # [..., num_rx_ant, num_rx_ant] + lmmse_mat = tf.transpose(lmmse_mat, [2, 3, 4, 5, 6, 7, 0, 1]) + + # [..., num_rx_ant, num_rx_ant] + lmmse_mat = matrix_inv(lmmse_mat) + lmmse_mat = tf.matmul(cov_mat, lmmse_mat) + + ########################################## + # Apply smoothing + ########################################## + + # [..., num_rx_ant, 1] + h_hat = tf.expand_dims(h_hat, axis=-1) + # [..., num_rx_ant] + h_hat = tf.squeeze(tf.matmul(lmmse_mat, h_hat), axis=-1) + + ########################################## + # Compute the estimation error variances + ########################################## + + # [..., num_rx_ant, num_rx_ant] + cov_mat_t = expand_to_rank(cov_mat_t, tf.rank(lmmse_mat), axis=0) + # [..., num_rx_ant] + err_var = tf.reduce_sum(cov_mat_t*lmmse_mat, axis=-1) + # [..., num_rx_ant] + err_var = tf.linalg.diag_part(cov_mat) - err_var + err_var = tf.math.real(err_var) + err_var = tf.maximum(err_var, self._rzero) + + ########################################## + # If this is *not* the last + # interpolation step, scales the + # input `h_hat` to ensure + # it has the variance expected by the + # next interpolation step. + # + # The error variance also `err_var` + # is updated accordingly. + ########################################## + if not self._last_step: + # + # Variance of h_hat + # + # Conjugate transpose of the LMMSE matrix + # [..., num_rx_ant, num_rx_ant] + lmmse_mat_h = tf.transpose(lmmse_mat, [0, 1, 2, 3, 4, 5, 7, 6], + conjugate=True) + # First part of the estimate covariance + # [..., num_rx_ant, num_rx_ant] + h_hat_var_1 = tf.matmul(cov_mat, lmmse_mat_h) + h_hat_var_1 = tf.transpose(h_hat_var_1, [0, 1, 2, 3, 4, 5, 7, 6]) + # [..., num_rx_ant] + h_hat_var_1 = tf.reduce_sum(lmmse_mat*h_hat_var_1, axis=-1) + # Second part of the estimate covariance + # [..., num_rx_ant, 1] + err_var_old = tf.expand_dims(err_var_old, axis=-1) + # [..., num_rx_ant, num_rx_ant] + h_hat_var_2 = err_var_old*lmmse_mat_h + # [..., num_rx_ant, num_rx_ant] + h_hat_var_2 = tf.transpose(h_hat_var_2, [0, 1, 2, 3, 4, 5, 7, 6]) + # [..., num_rx_ant] + h_hat_var_2 = tf.reduce_sum(lmmse_mat*h_hat_var_2, axis=-1) + # Variance of h_hat + # [..., num_rx_ant] + h_hat_var = h_hat_var_1 + h_hat_var_2 + # Scaling factor + # [..., num_rx_ant] + err_var_c = tf.complex(err_var, self._rzero) + h_var = tf.linalg.diag_part(cov_mat) + s = tf.math.divide_no_nan(2.*h_var, h_hat_var + h_var - err_var_c) + # Apply scaling to estimate + # [..., num_rx_ant] + h_hat = s*h_hat + # Updated variance + # [..., num_rx_ant] + err_var = s*(s-1.)*h_hat_var + (1.-s)*h_var + s*err_var_c + err_var = tf.math.real(err_var) + err_var = tf.maximum(err_var, self._rzero) + + return h_hat, err_var + + +class LMMSEInterpolator(BaseChannelInterpolator): + # pylint: disable=line-too-long + r"""LMMSEInterpolator(pilot_pattern, cov_mat_time, cov_mat_freq, cov_mat_space=None, order='t-f') + + LMMSE interpolation on a resource grid with optional spatial smoothing. + + This class computes for each element of an OFDM resource grid + a channel estimate and error variance + through linear minimum mean square error (LMMSE) interpolation/smoothing. + It is assumed that the measurements were taken at the nonzero positions + of a :class:`~sionna.ofdm.PilotPattern`. + + Depending on the value of ``order``, the interpolation is carried out + accross time (t), i.e., OFDM symbols, frequency (f), i.e., subcarriers, + and optionally space (s), i.e., receive antennas, in any desired order. + + For simplicity, we describe the underlying algorithm assuming that interpolation + across the sub-carriers is performed first, followed by interpolation across + OFDM symbols, and finally by spatial smoothing across receive + antennas. + The algorithm is similar if interpolation and/or smoothing are performed in + a different order. + For clarity, antenna indices are omitted when describing frequency and time + interpolation, as the same process is applied to all the antennas. + + The input ``h_hat`` is first reshaped to a resource grid + :math:`\hat{\mathbf{H}} \in \mathbb{C}^{N \times M}`, by scattering the channel + estimates at pilot locations according to the ``pilot_pattern``. :math:`N` + denotes the number of OFDM symbols and :math:`M` the number of sub-carriers. + + The first pass consists in interpolating across the sub-carriers: + + .. math:: + \hat{\mathbf{h}}_n^{(1)} = \mathbf{A}_n \hat{\mathbf{h}}_n + + where :math:`1 \leq n \leq N` is the OFDM symbol index and :math:`\hat{\mathbf{h}}_n` is + the :math:`n^{\text{th}}` (transposed) row of :math:`\hat{\mathbf{H}}`. + :math:`\mathbf{A}_n` is the :math:`M \times M` matrix such that: + + .. math:: + \mathbf{A}_n = \bar{\mathbf{A}}_n \mathbf{\Pi}_n^\intercal + + where + + .. math:: + \bar{\mathbf{A}}_n = \underset{\mathbf{Z} \in \mathbb{C}^{M \times K_n}}{\text{argmin}} \left\lVert \mathbf{Z}\left( \mathbf{\Pi}_n^\intercal \mathbf{R^{(f)}} \mathbf{\Pi}_n + \mathbf{\Sigma}_n \right) - \mathbf{R^{(f)}} \mathbf{\Pi}_n \right\rVert_{\text{F}}^2 + + and :math:`\mathbf{R^{(f)}}` is the :math:`M \times M` channel frequency covariance matrix, + :math:`\mathbf{\Pi}_n` the :math:`M \times K_n` matrix that spreads :math:`K_n` + values to a vector of size :math:`M` according to the ``pilot_pattern`` for the :math:`n^{\text{th}}` OFDM symbol, + and :math:`\mathbf{\Sigma}_n \in \mathbb{R}^{K_n \times K_n}` is the channel estimation error covariance built from + ``err_var`` and assumed to be diagonal. + Computation of :math:`\bar{\mathbf{A}}_n` is done using an algorithm based on complete orthogonal decomposition. + This is done to avoid matrix inversion for badly conditioned covariance matrices. + + The channel estimation error variances after the first interpolation pass are computed as + + .. math:: + \mathbf{\Sigma}^{(1)}_n = \text{diag} \left( \mathbf{R^{(f)}} - \mathbf{A}_n \mathbf{\Xi}_n \mathbf{R^{(f)}} \right) + + where :math:`\mathbf{\Xi}_n` is the diagonal matrix of size :math:`M \times M` that zeros the + columns corresponding to sub-carriers not carrying any pilots. + Note that interpolation is not performed for OFDM symbols which do not carry pilots. + + **Remark**: The interpolation matrix differs across OFDM symbols as different + OFDM symbols may carry pilots on different sub-carriers and/or have different + estimation error variances. + + Scaling of the estimates is then performed to ensure that their + variances match the ones expected by the next interpolation step, and the error variances are updated accordingly: + + .. math:: + \begin{align} + \left[\hat{\mathbf{h}}_n^{(2)}\right]_m &= s_{n,m} \left[\hat{\mathbf{h}}_n^{(1)}\right]_m\\ + \left[\mathbf{\Sigma}^{(2)}_n\right]_{m,m} &= s_{n,m}\left( s_{n,m}-1 \right) \left[\hat{\mathbf{\Sigma}}^{(1)}_n\right]_{m,m} + \left( 1 - s_{n,m} \right) \left[\mathbf{R^{(f)}}\right]_{m,m} + s_{n,m} \left[\mathbf{\Sigma}^{(1)}_n\right]_{m,m} + \end{align} + + where the scaling factor :math:`s_{n,m}` is such that: + + + .. math:: + \mathbb{E} \left\{ \left\lvert s_{n,m} \left[\hat{\mathbf{h}}_n^{(1)}\right]_m \right\rvert^2 \right\} = \left[\mathbf{R^{(f)}}\right]_{m,m} + \mathbb{E} \left\{ \left\lvert s_{n,m} \left[\hat{\mathbf{h}}^{(1)}_n\right]_m - \left[\mathbf{h}_n\right]_m \right\rvert^2 \right\} + + which leads to: + + .. math:: + \begin{align} + s_{n,m} &= \frac{2 \left[\mathbf{R^{(f)}}\right]_{m,m}}{\left[\mathbf{R^{(f)}}\right]_{m,m} - \left[\mathbf{\Sigma}^{(1)}_n\right]_{m,m} + \left[\hat{\mathbf{\Sigma}}^{(1)}_n\right]_{m,m}}\\ + \hat{\mathbf{\Sigma}}^{(1)}_n &= \mathbf{A}_n \mathbf{R^{(f)}} \mathbf{A}_n^{\mathrm{H}}. + \end{align} + + The second pass consists in interpolating across the OFDM symbols: + + .. math:: + \hat{\mathbf{h}}_m^{(3)} = \mathbf{B}_m \tilde{\mathbf{h}}^{(2)}_m + + where :math:`1 \leq m \leq M` is the sub-carrier index and :math:`\tilde{\mathbf{h}}^{(2)}_m` is + the :math:`m^{\text{th}}` column of + + .. math:: + \hat{\mathbf{H}}^{(2)} = \begin{bmatrix} + {\hat{\mathbf{h}}_1^{(2)}}^\intercal\\ + \vdots\\ + {\hat{\mathbf{h}}_N^{(2)}}^\intercal + \end{bmatrix} + + and :math:`\mathbf{B}_m` is the :math:`N \times N` interpolation LMMSE matrix: + + .. math:: + \mathbf{B}_m = \bar{\mathbf{B}}_m \tilde{\mathbf{\Pi}}_m^\intercal + + where + + .. math:: + \bar{\mathbf{B}}_m = \underset{\mathbf{Z} \in \mathbb{C}^{N \times L_m}}{\text{argmin}} \left\lVert \mathbf{Z} \left( \tilde{\mathbf{\Pi}}_m^\intercal \mathbf{R^{(t)}}\tilde{\mathbf{\Pi}}_m + \tilde{\mathbf{\Sigma}}^{(2)}_m \right) - \mathbf{R^{(t)}}\tilde{\mathbf{\Pi}}_m \right\rVert_{\text{F}}^2 + + where :math:`\mathbf{R^{(t)}}` is the :math:`N \times N` channel time covariance matrix, + :math:`\tilde{\mathbf{\Pi}}_m` the :math:`N \times L_m` matrix that spreads :math:`L_m` + values to a vector of size :math:`N` according to the ``pilot_pattern`` for the :math:`m^{\text{th}}` sub-carrier, + and :math:`\tilde{\mathbf{\Sigma}}^{(2)}_m \in \mathbb{R}^{L_m \times L_m}` is the diagonal matrix of channel estimation error variances + built by gathering the error variances from (:math:`\mathbf{\Sigma}^{(2)}_1,\dots,\mathbf{\Sigma}^{(2)}_N`) corresponding + to resource elements carried by the :math:`m^{\text{th}}` sub-carrier. + Computation of :math:`\bar{\mathbf{B}}_m` is done using an algorithm based on complete orthogonal decomposition. + This is done to avoid matrix inversion for badly conditioned covariance matrices. + + The resulting channel estimate for the resource grid is + + .. math:: + \hat{\mathbf{H}}^{(3)} = \left[ \hat{\mathbf{h}}_1^{(3)} \dots \hat{\mathbf{h}}_M^{(3)} \right] + + The resulting channel estimation error variances are the diagonal coefficients of the matrices + + .. math:: + \mathbf{\Sigma}^{(3)}_m = \mathbf{R^{(t)}} - \mathbf{B}_m \tilde{\mathbf{\Xi}}_m \mathbf{R^{(t)}}, 1 \leq m \leq M + + where :math:`\tilde{\mathbf{\Xi}}_m` is the diagonal matrix of size :math:`N \times N` that zeros the + columns corresponding to OFDM symbols not carrying any pilots. + + **Remark**: The interpolation matrix differs across sub-carriers as different + sub-carriers may have different estimation error variances computed by the first + pass. + However, all sub-carriers carry at least one channel estimate as a result of + the first pass, ensuring that a channel estimate is computed for all the resource + elements after the second pass. + + **Remark:** LMMSE interpolation requires knowledge of the time and frequency + covariance matrices of the channel. The notebook `OFDM MIMO Channel Estimation and Detection <../examples/OFDM_MIMO_Detection.ipynb>`_ shows how to estimate + such matrices for arbitrary channel models. + Moreover, the functions :func:`~sionna.ofdm.tdl_time_cov_mat` + and :func:`~sionna.ofdm.tdl_freq_cov_mat` compute the expected time and frequency + covariance matrices, respectively, for the :class:`~sionna.channel.tr38901.TDL` channel models. + + Scaling of the estimates is then performed to ensure that their + variances match the ones expected by the next smoothing step, and the + error variances are updated accordingly: + + .. math:: + \begin{align} + \left[\hat{\mathbf{h}}_m^{(4)}\right]_n &= \gamma_{m,n} \left[\hat{\mathbf{h}}_m^{(3)}\right]_n\\ + \left[\mathbf{\Sigma}^{(4)}_m\right]_{n,n} &= \gamma_{m,n}\left( \gamma_{m,n}-1 \right) \left[\hat{\mathbf{\Sigma}}^{(3)}_m\right]_{n,n} + \left( 1 - \gamma_{m,n} \right) \left[\mathbf{R^{(t)}}\right]_{n,n} + \gamma_{m,n} \left[\mathbf{\Sigma}^{(3)}_n\right]_{m,m} + \end{align} + + where: + + .. math:: + \begin{align} + \gamma_{m,n} &= \frac{2 \left[\mathbf{R^{(t)}}\right]_{n,n}}{\left[\mathbf{R^{(t)}}\right]_{n,n} - \left[\mathbf{\Sigma}^{(3)}_m\right]_{n,n} + \left[\hat{\mathbf{\Sigma}}^{(3)}_n\right]_{m,m}}\\ + \hat{\mathbf{\Sigma}}^{(3)}_m &= \mathbf{B}_m \mathbf{R^{(t)}} \mathbf{B}_m^{\mathrm{H}} + \end{align} + + Finally, a spatial smoothing step is applied to every resource element carrying + a channel estimate. + For clarity, we drop the resource element indexing :math:`(n,m)`. + We denote by :math:`L` the number of receive antennas, and by + :math:`\mathbf{R^{(s)}}\in\mathbb{C}^{L \times L}` the spatial covariance matrix. + + LMMSE spatial smoothing consists in the following computations: + + .. math:: + \hat{\mathbf{h}}^{(5)} = \mathbf{C} \hat{\mathbf{h}}^{(4)} + + where + + .. math:: + \mathbf{C} = \mathbf{R^{(s)}} \left( \mathbf{R^{(s)}} + \mathbf{\Sigma}^{(4)} \right)^{-1}. + + The estimation error variances are the digonal coefficients of + + .. math:: + \mathbf{\Sigma}^{(5)} = \mathbf{R^{(s)}} - \mathbf{C}\mathbf{R^{(s)}} + + The smoothed channel estimate :math:`\hat{\mathbf{h}}^{(5)}` and corresponding + error variances :math:`\text{diag}\left( \mathbf{\Sigma}^{(5)} \right)` are + returned for every resource element :math:`(m,n)`. + + **Remark:** No scaling is performed after the last interpolation or smoothing + step. + + **Remark:** All passes assume that the estimation error covariance matrix + (:math:`\mathbf{\Sigma}`, :math:`\tilde{\mathbf{\Sigma}}^{(2)}`, or :math:`\tilde{\mathbf{\Sigma}}^{(4)}`) is diagonal, which + may not be accurate. When this assumption does not hold, this interpolator is only + an approximation of LMMSE interpolation. + + **Remark:** The order in which frequency interpolation, temporal + interpolation, and, optionally, spatial smoothing are applied, is controlled using the + ``order`` parameter. + + Note + ---- + This layer does not support graph mode with XLA. + + Parameters + ---------- + pilot_pattern : PilotPattern + An instance of :class:`~sionna.ofdm.PilotPattern` + + cov_mat_time : [num_ofdm_symbols, num_ofdm_symbols], tf.complex + Time covariance matrix of the channel + + cov_mat_freq : [fft_size, fft_size], tf.complex + Frequency covariance matrix of the channel + + cov_time_space : [num_rx_ant, num_rx_ant], tf.complex + Spatial covariance matrix of the channel. + Defaults to `None`. + Only required if spatial smoothing is requested (see ``order``). + + order : str + Order in which to perform interpolation and optional smoothing. + For example, ``"t-f-s"`` means that interpolation across the OFDM symbols + is performed first (``"t"``: time), followed by interpolation across the + sub-carriers (``"f"``: frequency), and finally smoothing across the + receive antennas (``"s"``: space). + Similarly, ``"f-t"`` means interpolation across the sub-carriers followed + by interpolation across the OFDM symbols and no spatial smoothing. + The spatial covariance matrix (``cov_time_space``) is only required when + spatial smoothing is requested. + Time and frequency interpolation are not optional to ensure that a channel + estimate is computed for all resource elements. + + Input + ----- + h_hat : [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, num_pilot_symbols], tf.complex + Channel estimates for the pilot-carrying resource elements + + err_var : [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, num_pilot_symbols], tf.complex + Channel estimation error variances for the pilot-carrying resource elements + + Output + ------ + h_hat : [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, num_ofdm_symbols, fft_size], tf.complex + Channel estimates accross the entire resource grid for all + transmitters and streams + + err_var : Same shape as ``h_hat``, tf.float + Channel estimation error variances accross the entire resource grid + for all transmitters and streams + """ + + def __init__(self, pilot_pattern, cov_mat_time, cov_mat_freq, + cov_mat_space=None, order='t-f'): + + # Check the specified order + order = order.split('-') + assert 2 <= len(order) <= 3, "Invalid order for interpolation." + spatial_smoothing = False + freq_smoothing = False + time_smoothing = False + for o in order: + assert o in ('s', 'f', 't'), f"Uknown dimension {o}" + if o == 's': + assert not spatial_smoothing,\ + "Spatial smoothing can be specified at most once" + spatial_smoothing = True + elif o == 't': + assert not time_smoothing,\ + "Temporal interpolation can be specified once only" + time_smoothing = True + elif o == 'f': + assert not freq_smoothing,\ + "Frequency interpolation can be specified once only" + freq_smoothing = True + if spatial_smoothing: + assert cov_mat_space is not None,\ + "A spatial covariance matrix is required for spatial smoothing" + assert freq_smoothing, "Frequency interpolation is required" + assert time_smoothing, "Time interpolation is required" + + self._order = order + self._num_ofdm_symbols = pilot_pattern.num_ofdm_symbols + self._num_effective_subcarriers =pilot_pattern.num_effective_subcarriers + + # Build pilot masks for every stream + pilot_mask = self._build_pilot_mask(pilot_pattern) + + # Build indices for mapping channel estimates and + # error variances that are given as input to a + # resource grid + num_pilots = pilot_pattern.pilots.shape[2] + inputs_to_rg_indices = self._build_inputs2rg_indices(pilot_mask, + num_pilots) + self._inputs_to_rg_indices = tf.cast(inputs_to_rg_indices, tf.int32) + + # 1D interpolator according to requested order + # Interpolation is always performed along the inner dimension. + interpolators = [] + # Masks for masking error variances that were not updated + err_var_masks = [] + for i, o in enumerate(order): + # Is it the last one? + last_step = (i == len(order)-1) + # Frequency + if o == "f": + interpolator = LMMSEInterpolator1D(pilot_mask, cov_mat_freq, + last_step=last_step) + pilot_mask = self._update_pilot_mask_interp(pilot_mask) + err_var_mask = tf.cast(pilot_mask == 1, + cov_mat_freq.dtype.real_dtype) + # Time + elif o == 't': + pilot_mask = tf.transpose(pilot_mask, [0, 1, 3, 2]) + interpolator = LMMSEInterpolator1D(pilot_mask, cov_mat_time, + last_step=last_step) + pilot_mask = self._update_pilot_mask_interp(pilot_mask) + pilot_mask = tf.transpose(pilot_mask, [0, 1, 3, 2]) + err_var_mask = tf.cast(pilot_mask == 1, + cov_mat_freq.dtype.real_dtype) + # Space + elif o == 's': + interpolator = SpatialChannelFilter(cov_mat_space, + last_step=last_step) + err_var_mask = tf.cast(pilot_mask == 1, + cov_mat_freq.dtype.real_dtype) + interpolators.append(interpolator) + err_var_masks.append(err_var_mask) + self._interpolators = interpolators + self._err_var_masks = err_var_masks + + def _build_pilot_mask(self, pilot_pattern): + """ + Build for every transmitter and stream a pilot mask indicating + which REs are allocated to pilots, data, or not used. + # 0 -> Data + # 1 -> Pilot + # 2 -> Not used + """ + + mask = pilot_pattern.mask + pilots = pilot_pattern.pilots + num_tx = mask.shape[0] + num_streams_per_tx = mask.shape[1] + num_ofdm_symbols = mask.shape[2] + num_effective_subcarriers = mask.shape[3] + + pilot_mask = np.zeros([num_tx, num_streams_per_tx, num_ofdm_symbols, + num_effective_subcarriers], int) + for tx,st in itertools.product( range(num_tx), + range(num_streams_per_tx)): + pil_index = 0 + for sb,sc in itertools.product( range(num_ofdm_symbols), + range(num_effective_subcarriers)): + if mask[tx,st,sb,sc] == 1: + if np.abs(pilots[tx,st,pil_index]) > 0.0: + pilot_mask[tx,st,sb,sc] = 1 + else: + pilot_mask[tx,st,sb,sc] = 2 + pil_index += 1 + + return pilot_mask + + def _build_inputs2rg_indices(self, pilot_mask, num_pilots): + """ + Builds indices for mapping channel estimates and + error variances that are given as input to a + resource grid + """ + + num_tx = pilot_mask.shape[0] + num_streams_per_tx = pilot_mask.shape[1] + num_ofdm_symbols = pilot_mask.shape[2] + num_effective_subcarriers = pilot_mask.shape[3] + + inputs_to_rg_indices = np.zeros([num_tx, num_streams_per_tx, + num_pilots, 4], int) + for tx,st in itertools.product( range(num_tx), + range(num_streams_per_tx)): + pil_index = 0 # Pilot index for this stream + for sb,sc in itertools.product( range(num_ofdm_symbols), + range(num_effective_subcarriers)): + if pilot_mask[tx,st,sb,sc] == 0: + continue + if pilot_mask[tx,st,sb,sc] == 1: + inputs_to_rg_indices[tx, st, pil_index] = [tx, st, sb, sc] + pil_index += 1 + + return inputs_to_rg_indices + + def _update_pilot_mask_interp(self, pilot_mask): + """ + Update the pilot mask to label the resource elements for which the + channel was interpolated. + """ + + interpolated = np.any(pilot_mask == 1, axis=-1, keepdims=True) + pilot_mask = np.where(interpolated, 1, pilot_mask) + + return pilot_mask + + def __call__(self, h_hat, err_var): + + # h_hat : [batch_size, num_rx, num_rx_ant, num_tx, + # num_streams_per_tx, num_pilots] + # err_var : [batch_size, num_rx, num_rx_ant, num_tx, + # num_streams_per_tx, num_pilots] + + batch_size = tf.shape(h_hat)[0] + num_rx = tf.shape(h_hat)[1] + num_rx_ant = tf.shape(h_hat)[2] + num_tx = tf.shape(h_hat)[3] + num_tx_stream = tf.shape(h_hat)[4] + num_ofdm_symbols = self._num_ofdm_symbols + num_effective_subcarriers = self._num_effective_subcarriers + + # For some estimator, err_var might not have the same shape + # as h_hat + err_var = tf.broadcast_to(err_var, tf.shape(h_hat)) + + # Mapping the channel estimates and error variances to a resource grid + # all : [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, + # num_ofdm_symbols, num_effective_subcarriers] + h_hat = tf.transpose(h_hat, [3, 4, 5, 0, 1, 2]) + err_var = tf.transpose(err_var, [3, 4, 5, 0, 1, 2]) + h_hat = tf.scatter_nd(self._inputs_to_rg_indices, h_hat, + [num_tx, num_tx_stream, + num_ofdm_symbols, + num_effective_subcarriers, + batch_size, num_rx, num_rx_ant]) + err_var = tf.scatter_nd(self._inputs_to_rg_indices, err_var, + [num_tx, num_tx_stream, + num_ofdm_symbols, + num_effective_subcarriers, + batch_size, num_rx, num_rx_ant]) + h_hat = tf.transpose(h_hat, [4, 5, 6, 0, 1, 2, 3]) + err_var = tf.transpose(err_var, [4, 5, 6, 0, 1, 2, 3]) + + # Interpolation + # Performed according to the requested order. Transpose are used as + # 1D interpolation is performed along the inner axis. + items = zip(self._order, self._interpolators, self._err_var_masks) + for o,interp,err_var_mask in items: + # Frequency + if o == 'f': + # [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, + # num_ofdm_symbols, num_effective_subcarriers] + h_hat, err_var = interp(h_hat, err_var) + err_var_mask = expand_to_rank(err_var_mask, tf.rank(err_var), 0) + err_var = err_var*err_var_mask + # Time + elif o == 't': + # [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, + # num_effective_subcarriers, num_ofdm_symbols] + h_hat = tf.transpose(h_hat, [0, 1, 2, 3, 4, 6, 5]) + err_var = tf.transpose(err_var, [0, 1, 2, 3, 4, 6, 5]) + h_hat, err_var = interp(h_hat, err_var) + # [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, + # num_ofdm_symbols, num_effective_subcarriers] + h_hat = tf.transpose(h_hat, [0, 1, 2, 3, 4, 6, 5]) + err_var = tf.transpose(err_var, [0, 1, 2, 3, 4, 6, 5]) + err_var_mask = expand_to_rank(err_var_mask, tf.rank(err_var), 0) + err_var = err_var*err_var_mask + # Space + elif o == 's': + # [batch_size, num_rx, num_tx, num_streams_per_tx, + # num_ofdm_symbols, num_effective_subcarriers, num_rx_ant] + h_hat = tf.transpose(h_hat, [0, 1, 3, 4, 5, 6, 2]) + err_var = tf.transpose(err_var, [0, 1, 3, 4, 5, 6, 2]) + h_hat, err_var = interp(h_hat, err_var) + # [batch_size, num_rx, num_tx, num_streams_per_tx, + # num_ofdm_symbols, num_effective_subcarriers, num_rx_ant] + h_hat = tf.transpose(h_hat, [0, 1, 6, 2, 3, 4, 5]) + err_var = tf.transpose(err_var, [0, 1, 6, 2, 3, 4, 5]) + err_var_mask = expand_to_rank(err_var_mask, tf.rank(err_var), 0) + err_var = err_var*err_var_mask + + return h_hat, err_var + +####################################################### +# Utilities +####################################################### + +def tdl_freq_cov_mat(model, subcarrier_spacing, fft_size, delay_spread, + dtype=tf.complex64): + # pylint: disable=line-too-long + r""" + Computes the frequency covariance matrix of a + :class:`~sionna.channel.tr38901.TDL` channel model. + + The channel frequency covariance matrix :math:`\mathbf{R}^{(f)}` of a TDL channel model is + + .. math:: + \mathbf{R}^{(f)}_{u,v} = \sum_{\ell=1}^L P_\ell e^{-j 2 \pi \tau_\ell \Delta_f (u-v)}, 1 \leq u,v \leq M + + where :math:`M` is the FFT size, :math:`L` is the number of paths for the selected TDL model, + :math:`P_\ell` and :math:`\tau_\ell` are the average power and delay for the + :math:`\ell^{\text{th}}` path, respectively, and :math:`\Delta_f` is the sub-carrier spacing. + + Input + ------ + model : str + TDL model for which to return the covariance matrix. + Should be one of "A", "B", "C", "D", or "E". + + subcarrier_spacing : float + Sub-carrier spacing [Hz] + + fft_size : float + FFT size + + delay_spread : float + Delay spread [s] + + dtype : tf.DType + Datatype to use for the output. + Should be one of `tf.complex64` or `tf.complex128`. + Defaults to `tf.complex64`. + + Output + ------ + cov_mat : [fft_size, fft_size], tf.complex + Channel frequency covariance matrix + """ + + assert dtype in (tf.complex64, tf.complex128),\ + "The `dtype` should be a complex datatype" + + # + # Load the power delay profile + # + + # Set the file from which to load the model + assert model in ('A', 'B', 'C', 'D', 'E'), "Invalid TDL model" + if model == 'A': + parameters_fname = "TDL-A.json" + elif model == 'B': + parameters_fname = "TDL-B.json" + elif model == 'C': + parameters_fname = "TDL-C.json" + elif model == 'D': + parameters_fname = "TDL-D.json" + elif model == 'E': + parameters_fname = "TDL-E.json" + source = files(models).joinpath(parameters_fname) + # pylint: disable=unspecified-encoding + with open(source) as parameter_file: + params = json.load(parameter_file) + # LoS scenario ? + los = bool(params['los']) + # Retrieve power and delays + delays = np.array(params['delays'])*delay_spread + mean_powers = np.power(10.0, np.array(params['powers'])/10.0) + + if los: + # Add the power of the specular and non-specular component of + # the first path + mean_powers[0] = mean_powers[0] + mean_powers[1] + mean_powers = np.concatenate([mean_powers[:1], mean_powers[2:]], axis=0) + # The first two paths have 0 delays as they correspond to the + # specular and reflected components of the first path. + delays = delays[1:] + + # Normalize the PDP + norm_factor = np.sum(mean_powers) + mean_powers = mean_powers / norm_factor + + # + # Build frequency covariance matrix + # + + n = np.arange(fft_size) + p = -2.*np.pi*subcarrier_spacing*n + p = np.expand_dims(p, axis=0) + delays = np.expand_dims(delays, axis=1) + p = p*delays + p = np.exp(1j*p) + p = np.expand_dims(p, axis=-1) + cov_mat = np.matmul(p, np.transpose(np.conj(p), [0, 2, 1])) + mean_powers = np.expand_dims(mean_powers, axis=(1,2)) + cov_mat = np.sum(mean_powers*cov_mat, axis=0) + + return tf.cast(cov_mat, dtype) + +def tdl_time_cov_mat(model, speed, carrier_frequency, ofdm_symbol_duration, + num_ofdm_symbols, los_angle_of_arrival=PI/4., dtype=tf.complex64): + # pylint: disable=line-too-long + r""" + Computes the time covariance matrix of a + :class:`~sionna.channel.tr38901.TDL` channel model. + + For non-line-of-sight (NLoS) model, the channel time covariance matrix + :math:`\mathbf{R^{(t)}}` of a TDL channel model is + + .. math:: + \mathbf{R^{(t)}}_{u,v} = J_0 \left( \nu \Delta_t \left( u-v \right) \right) + + where :math:`J_0` is the zero-order Bessel function of the first kind, + :math:`\Delta_t` the duration of an OFDM symbol, and :math:`\nu` the Doppler + spread defined by + + .. math:: + \nu = 2 \pi \frac{v}{c} f_c + + where :math:`v` is the movement speed, :math:`c` the speed of light, and + :math:`f_c` the carrier frequency. + + For line-of-sight (LoS) channel models, the channel time covariance matrix + is + + .. math:: + \mathbf{R^{(t)}}_{u,v} = P_{\text{NLoS}} J_0 \left( \nu \Delta_t \left( u-v \right) \right) + P_{\text{LoS}}e^{j \nu \Delta_t \left( u-v \right) \cos{\alpha_{\text{LoS}}}} + + where :math:`\alpha_{\text{LoS}}` is the angle-of-arrival for the LoS path, + :math:`P_{\text{NLoS}}` the total power of NLoS paths, and + :math:`P_{\text{LoS}}` the power of the LoS path. The power delay profile + is assumed to have unit power, i.e., :math:`P_{\text{NLoS}} + P_{\text{LoS}} = 1`. + + Input + ------ + model : str + TDL model for which to return the covariance matrix. + Should be one of "A", "B", "C", "D", or "E". + + speed : float + Speed [m/s] + + carrier_frequency : float + Carrier frequency [Hz] + + ofdm_symbol_duration : float + Duration of an OFDM symbol [s] + + num_ofdm_symbols : int + Number of OFDM symbols + + los_angle_of_arrival : float + Angle-of-arrival for LoS path [radian]. Only used with LoS models. + Defaults to :math:`\pi/4`. + + dtype : tf.DType + Datatype to use for the output. + Should be one of `tf.complex64` or `tf.complex128`. + Defaults to `tf.complex64`. + + Output + ------ + cov_mat : [num_ofdm_symbols, num_ofdm_symbols], tf.complex + Channel time covariance matrix + """ + + # Doppler spread + doppler_spread = 2.*PI*speed/SPEED_OF_LIGHT*carrier_frequency + + # + # Load the power delay profile + # + + # Set the file from which to load the model + assert model in ('A', 'B', 'C', 'D', 'E'), "Invalid TDL model" + if model == 'A': + parameters_fname = "TDL-A.json" + elif model == 'B': + parameters_fname = "TDL-B.json" + elif model == 'C': + parameters_fname = "TDL-C.json" + elif model == 'D': + parameters_fname = "TDL-D.json" + elif model == 'E': + parameters_fname = "TDL-E.json" + source = files(models).joinpath(parameters_fname) + # pylint: disable=unspecified-encoding + with open(source) as parameter_file: + params = json.load(parameter_file) + # LoS scenario ? + los = bool(params['los']) + # Retrieve power and delays + mean_powers = np.power(10.0, np.array(params['powers'])/10.0) + + # Normalize the PDP + norm_factor = np.sum(mean_powers) + mean_powers = mean_powers / norm_factor + + if los: + los_power = mean_powers[0] + nlos_power = np.sum(mean_powers[1:]) + else: + nlos_power = np.sum(mean_powers) + + # + # Build time covariance matrix + # + + indices = np.arange(num_ofdm_symbols) + s1 = np.expand_dims(indices, axis=1) + s2 = np.expand_dims(indices, axis=0) + exp = doppler_spread*ofdm_symbol_duration*(s1-s2) + cov_mat_nlos = jv(0.0, exp)*nlos_power + if los: + cov_mat_los = np.exp(1j*exp*np.cos(los_angle_of_arrival))*los_power + cov_mat = cov_mat_nlos+cov_mat_los + else: + cov_mat = cov_mat_nlos + + return tf.cast(cov_mat, dtype) diff --git a/sionna/ofdm/detection.py b/sionna/ofdm/detection.py index 7fc8dcd2..6f4eebc3 100644 --- a/sionna/ofdm/detection.py +++ b/sionna/ofdm/detection.py @@ -5,57 +5,66 @@ """Class definition and functions related to OFDM channel equalization""" import tensorflow as tf -import sionna as sn +from tensorflow.keras.layers import Layer from sionna.utils import flatten_dims, split_dim, flatten_last_dims, expand_to_rank from sionna.ofdm import RemoveNulledSubcarriers +from sionna.mimo import MaximumLikelihoodDetectorWithPrior as MaximumLikelihoodDetectorWithPrior_ +from sionna.mimo import MaximumLikelihoodDetector as MaximumLikelihoodDetector_ +from sionna.mimo import LinearDetector as LinearDetector_ +from sionna.mimo import KBestDetector as KBestDetector_ +from sionna.mimo import EPDetector as EPDetector_ +from sionna.mimo import MMSEPICDetector as MMSEPICDetector_ +from sionna.mapping import Constellation -# pylint: disable=line-too-long -class MaximumLikelihoodDetectorWithPrior(sn.mimo.MaximumLikelihoodDetectorWithPrior): + +class OFDMDetector(Layer): # pylint: disable=line-too-long - r"""MaximumLikelihoodDetectorWithPrior(output, demapping_method, resource_grid, stream_management, constellation_type=None, num_bits_per_symbol=None, constellation=None, hard_out=False, dtype=tf.complex64, **kwargs) + r"""OFDMDetector(detector, output, resource_grid, stream_management, dtype=tf.complex64, **kwargs) - Maximum-likelihood (ML) detection for OFDM MIMO transmissions, assuming prior - knowledge of the bits or constellation points is available. + Layer that wraps a MIMO detector for use with the OFDM waveform. - This layer implements maximum-likelihood (ML) detection - for OFDM MIMO transmissions assuming prior knowledge on the transmitted data is available. - Both ML detection of symbols or bits with either - soft- or hard-decisions are supported. The OFDM and stream configuration are provided - by a :class:`~sionna.ofdm.ResourceGrid` and - :class:`~sionna.mimo.StreamManagement` instance, respectively. The - actual detector is an instance of :class:`~sionna.mimo.MaximumLikelihoodDetectorWithPrior`. + The parameter ``detector`` is a callable (e.g., a function) that + implements a MIMO detection algorithm for arbitrary batch dimensions. - Parameters - ---------- - output : One of ["bit", "symbol"], str - The type of output, either LLRs on bits or logits on constellation symbols. + This class pre-processes the received resource grid ``y`` and channel + estimate ``h_hat``, and computes for each receiver the + noise-plus-interference covariance matrix according to the OFDM and stream + configuration provided by the ``resource_grid`` and + ``stream_management``, which also accounts for the channel + estimation error variance ``err_var``. These quantities serve as input to the detection + algorithm that is implemented by ``detector``. + Both detection of symbols or bits with either soft- or hard-decisions are supported. - demapping_method : One of ["app", "maxlog"], str - The demapping method used. + Note + ----- + The callable ``detector`` must take as input a tuple :math:`(\mathbf{y}, \mathbf{h}, \mathbf{s})` such that: - resource_grid : ResourceGrid - An instance of :class:`~sionna.ofdm.ResourceGrid`. + * **y** ([...,num_rx_ant], tf.complex) -- 1+D tensor containing the received signals. + * **h** ([...,num_rx_ant,num_streams_per_rx], tf.complex) -- 2+D tensor containing the channel matrices. + * **s** ([...,num_rx_ant,num_rx_ant], tf.complex) -- 2+D tensor containing the noise-plus-interference covariance matrices. - stream_management : StreamManagement - An instance of :class:`~sionna.mimo.StreamManagement`. + It must generate one of following outputs depending on the value of ``output``: - constellation_type : One of ["qam", "pam", "custom"], str - For "custom", an instance of :class:`~sionna.mapping.Constellation` - must be provided. + * **b_hat** ([..., num_streams_per_rx, num_bits_per_symbol], tf.float) -- LLRs or hard-decisions for every bit of every stream, if ``output`` equals `"bit"`. + * **x_hat** ([..., num_streams_per_rx, num_points], tf.float) or ([..., num_streams_per_rx], tf.int) -- Logits or hard-decisions for constellation symbols for every stream, if ``output`` equals `"symbol"`. Hard-decisions correspond to the symbol indices. - num_bits_per_symbol : int - The number of bits per constellation symbol, e.g., 4 for QAM16. - Only required for ``constellation_type`` in ["qam", "pam"]. + Parameters + ---------- + detector : Callable + Callable object (e.g., a function) that implements a MIMO detection + algorithm for arbitrary batch dimensions. Either one of the existing detectors, e.g., + :class:`~sionna.mimo.LinearDetector`, :class:`~sionna.mimo.MaximumLikelihoodDetector`, or + :class:`~sionna.mimo.KBestDetector` can be used, or a custom detector + callable provided that has the same input/output specification. - constellation : Constellation - An instance of :class:`~sionna.mapping.Constellation` or `None`. - In the latter case, ``constellation_type`` - and ``num_bits_per_symbol`` must be provided. + output : One of ["bit", "symbol"], str + Type of output, either bits or symbols - hard_out : bool - If `True`, the detector computes hard-decided bit values or - constellation point indices instead of soft-values. - Defaults to `False`. + resource_grid : ResourceGrid + Instance of :class:`~sionna.ofdm.ResourceGrid` + + stream_management : StreamManagement + Instance of :class:`~sionna.mimo.StreamManagement` dtype : One of [tf.complex64, tf.complex128] tf.DType (dtype) The dtype of `y`. Defaults to tf.complex64. @@ -63,57 +72,41 @@ class MaximumLikelihoodDetectorWithPrior(sn.mimo.MaximumLikelihoodDetectorWithPr Input ------ - (y, h_hat, prior, err_var, no) : + (y, h_hat, err_var, no) : Tuple: y : [batch_size, num_rx, num_rx_ant, num_ofdm_symbols, fft_size], tf.complex - The received OFDM resource grid after cyclic prefix removal and FFT. + Received OFDM resource grid after cyclic prefix removal and FFT h_hat : [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, num_ofdm_symbols, num_effective_subcarriers], tf.complex - The channel estimates for all streams from all transmitters. - - prior : [batch_size, num_tx, num_streams, num_data_symbols x num_bits_per_symbol] or [batch_size, num_tx, num_streams, num_data_symbols, num_points], tf.float - Prior of the transmitted signals. - If ``output`` equals "bit", LLRs of the transmitted bits are expected. - If ``output`` equals "symbol", logits of the transmitted constellation points are expected. + Channel estimates for all streams from all transmitters err_var : [Broadcastable to shape of ``h_hat``], tf.float - The variance of the channel estimation error. + Variance of the channel estimation error no : [batch_size, num_rx, num_rx_ant] (or only the first n dims), tf.float - The variance of the AWGN noise. + Variance of the AWGN Output ------ One of: : [batch_size, num_tx, num_streams, num_data_symbols*num_bits_per_symbol], tf.float - LLRs or hard-decisions for every bit of every stream, if ``output`` equals `"bit"`. + LLRs or hard-decisions for every bit of every stream, if ``output`` equals `"bit"` : [batch_size, num_tx, num_streams, num_data_symbols, num_points], tf.float or [batch_size, num_tx, num_streams, num_data_symbols], tf.int Logits or hard-decisions for constellation symbols for every stream, if ``output`` equals `"symbol"`. Hard-decisions correspond to the symbol indices. - - Note - ---- - If you want to use this layer in Graph mode with XLA, i.e., within - a function that is decorated with ``@tf.function(jit_compile=True)``, - you must set ``sionna.Config.xla_compat=true``. - See :py:attr:`~sionna.Config.xla_compat`. """ - def __init__(self, + detector, output, - demapping_method, resource_grid, stream_management, - constellation_type=None, - num_bits_per_symbol=None, - constellation=None, - hard_out=False, dtype=tf.complex64, **kwargs): - + super().__init__(dtype=dtype, **kwargs) + self._detector = detector self._resource_grid = resource_grid self._stream_management = stream_management self._removed_nulled_scs = RemoveNulledSubcarriers(self._resource_grid) @@ -125,41 +118,9 @@ def __init__(self, data_ind = tf.argsort(flatten_last_dims(mask), direction="ASCENDING") self._data_ind = data_ind[...,:num_data_symbols] - # Precompute indices to map priors to a resource grid - rg_type = resource_grid.build_type_grid() - self._data_ind_scatter = tf.where(rg_type==0) - - # Initializing maximum-likelihood baseclass - super().__init__(output=output, - demapping_method=demapping_method, - k = stream_management.num_streams_per_rx, - constellation_type=constellation_type, - num_bits_per_symbol=num_bits_per_symbol, - constellation=constellation, - hard_out=hard_out, - dtype=dtype, - **kwargs) - - def call(self, inputs): - y, h_hat, prior, err_var, no = inputs - # y has shape: - # [batch_size, num_rx, num_rx_ant, num_ofdm_symbols, fft_size] - - # h_hat has shape: - # [batch_size, num_rx, num_rx_ant, num_tx, num_streams,... - # ..., num_ofdm_symbols, num_effective_subcarriers] - - # prior has shape - # [batch_size, num_tx, num_streams,... - # ... num_data_symbols x num_bits_per_symbol] - # if ``output`` equals "bit" - # [batch_size, num_tx, num_streams, num_data_symbols, num_points] - # if ``output`` equals "symbol" - - # err_var has a shape that is broadcastable to h_hat - - # no has shape [batch_size, num_rx, num_rx_ant] - # or just the first n dimensions of this + def _preprocess_inputs(self, y, h_hat, err_var, no): + """Pro-process the received signal and compute the + noise-plus-interference covariance matrix""" # Remove nulled subcarriers from y (guards, dc). New shape: # [batch_size, num_rx, num_rx_ant, ... @@ -175,6 +136,12 @@ def call(self, inputs): y_dt = tf.transpose(y_eff, [0, 1, 3, 4, 2]) y_dt = tf.cast(y_dt, self._dtype) + # Transpose y_eff to put num_rx_ant last. New shape: + # [batch_size, num_rx, num_ofdm_symbols,... + # ..., num_effective_subcarriers, num_rx_ant] + y_dt = tf.transpose(y_eff, [0, 1, 3, 4, 2]) + y_dt = tf.cast(y_dt, self._dtype) + ############################################## ### Prepare the err_var for MIMO detection ### ############################################## @@ -211,9 +178,11 @@ def call(self, inputs): # [num_rx, num_streams_per_rx, batch_size, num_rx_ant, ... # ..., num_ofdm_symbols, num_effective_subcarriers] h_dt_desired = split_dim(h_dt_desired, - [self._stream_management.num_rx, -1], 0) + [self._stream_management.num_rx, + self._stream_management.num_streams_per_rx], + 0) h_dt_undesired = split_dim(h_dt_undesired, - [self._stream_management.num_rx, -1], 0) + [self._stream_management.num_rx, -1], 0) # Permutate dims to # [batch_size, num_rx, num_ofdm_symbols, num_effective_subcarriers,.. @@ -256,54 +225,17 @@ def call(self, inputs): s = s_inf + s_no + s_csi s = tf.cast(s, self._dtype) - ######################### - ### Prepare the prior ### - ######################### - # [batch_size, num_tx, num_streams_per_tx, num_data_symbols, - # ... num_bits_per_symbol/num_points] - if self._output == 'bit': - prior = split_dim( prior, - [ self._resource_grid.num_data_symbols, - self._constellation.num_bits_per_symbol], - 3) - # Create a zero template for the prior - # [num_tx, num_streams_per_tx, num_ofdm_symbols,... - # ... num_effective_subcarriers, num_bits_per_symbol/num_points, - # ... batch_size] - template = tf.zeros([ self._resource_grid.num_tx, - self._resource_grid.num_streams_per_tx, - self._resource_grid.num_ofdm_symbols, - self._resource_grid.num_effective_subcarriers, - tf.shape(prior)[-1], - tf.shape(prior)[0]], - tf.as_dtype(self._dtype).real_dtype) - # [num_tx, num_streams_per_tx, num_data_symbols, - # ... num_bits_per_symbol/num_points, batch_size] - prior = tf.transpose(prior, [1, 2, 3, 4, 0]) - # [num_tx, num_streams_per_tx, num_ofdm_symbols,... - # ... num_effective_subcarriers, num_bits_per_symbol/num_points,... - # ... batch_size] - prior = flatten_dims(prior, 3, 0) - prior = tf.tensor_scatter_nd_update(template, self._data_ind_scatter, - prior) - # [batch_size, num_ofdm_symbols, num_effective_subcarriers,... - # num_tx*num_streams_per_tx, num_bits_per_symbol/num_points] - prior = tf.transpose(prior, [5, 2, 3, 0, 1, 4]) - prior = flatten_dims(prior, 2, 3) - # Add the receive antenna dimension for broadcasting - # [batch_size, num_rx, num_ofdm_symbols, num_effective_subcarriers,... - # num_tx*num_streams_per_tx, num_bits_per_symbol/num_points] - prior = tf.tile(tf.expand_dims(prior, axis=1), - [1, tf.shape(y_dt)[1], 1, 1, 1, 1]) + return y_dt, h_dt_desired, s - ################################# - ### Maximum-likelihood detection - ################################# - z = super().call([y_dt,h_dt_desired,prior,s]) + def _extract_datasymbols(self, z): + """Extract data symbols for all detected TX""" + + # If output is symbols with hard decision, the rank is 5 and not 6 as + # for other cases. The tensor rank is therefore expanded with one extra + # dimension, which is removed later. + rank_extanded = len(z.shape) < 6 + z = expand_to_rank(z, 6, -1) - ############################################## - ### Extract data symbols for all detected TX - ############################################## # Transpose tensor to shape # [num_rx, num_streams_per_rx, num_ofdm_symbols, # num_effective_subcarriers, num_bits_per_symbol or num_points, @@ -349,74 +281,133 @@ def call(self, inputs): # if output is LLRs on bits if self._output == 'bit': z = flatten_dims(z, 2, 3) + # Remove dummy dimension if output is symbols with hard decision + if rank_extanded: + z = tf.squeeze(z, axis=-1) + + return z + + def call(self, inputs): + y, h_hat, err_var, no = inputs + # y has shape: + # [batch_size, num_rx, num_rx_ant, num_ofdm_symbols, fft_size] + + # h_hat has shape: + # [batch_size, num_rx, num_rx_ant, num_tx, num_streams,... + # ..., num_ofdm_symbols, num_effective_subcarriers] + + # err_var has a shape that is broadcastable to h_hat + + # no has shape [batch_size, num_rx, num_rx_ant] + # or just the first n dimensions of this + + ################################ + ### Pre-process the inputs + ################################ + y_dt, h_dt_desired, s = self._preprocess_inputs(y, h_hat, err_var, no) + + ################################# + ### Detection + ################################# + z = self._detector([y_dt, h_dt_desired, s]) + + ############################################## + ### Extract data symbols for all detected TX + ############################################## + z = self._extract_datasymbols(z) return z -class MaximumLikelihoodDetector(MaximumLikelihoodDetectorWithPrior): + +class OFDMDetectorWithPrior(OFDMDetector): # pylint: disable=line-too-long - r"""MaximumLikelihoodDetector(output, demapping_method, resource_grid, stream_management, constellation_type=None, num_bits_per_symbol=None, constellation=None, hard_out=False, dtype=tf.complex64, **kwargs) + r"""OFDMDetectorWithPrior(detector, output, resource_grid, stream_management, constellation_type, num_bits_per_symbol, constellation, dtype=tf.complex64, **kwargs) - Maximum-likelihood (ML) detection for OFDM MIMO transmissions. + Layer that wraps a MIMO detector that assumes prior knowledge of the bits or + constellation points is available, for use with the OFDM waveform. - This layer implements maximum-likelihood (ML) detection - for OFDM MIMO transmissions. Both ML detection of symbols or bits with either - soft- or hard-decisions are supported. The OFDM and stream configuration are provided - by a :class:`~sionna.ofdm.ResourceGrid` and - :class:`~sionna.mimo.StreamManagement` instance, respectively. The - actual detector is an instance of :class:`~sionna.mimo.MaximumLikelihoodDetector`. + The parameter ``detector`` is a callable (e.g., a function) that + implements a MIMO detection algorithm with prior for arbitrary batch + dimensions. + + This class pre-processes the received resource grid ``y``, channel + estimate ``h_hat``, and the prior information ``prior``, and computes for each receiver the + noise-plus-interference covariance matrix according to the OFDM and stream + configuration provided by the ``resource_grid`` and + ``stream_management``, which also accounts for the channel + estimation error variance ``err_var``. These quantities serve as input to the detection + algorithm that is implemented by ``detector``. + Both detection of symbols or bits with either soft- or hard-decisions are supported. + + Note + ----- + The callable ``detector`` must take as input a tuple :math:`(\mathbf{y}, \mathbf{h}, \mathbf{prior}, \mathbf{s})` such that: + + * **y** ([...,num_rx_ant], tf.complex) -- 1+D tensor containing the received signals. + * **h** ([...,num_rx_ant,num_streams_per_rx], tf.complex) -- 2+D tensor containing the channel matrices. + * **prior** ([...,num_streams_per_rx,num_bits_per_symbol] or [...,num_streams_per_rx,num_points], tf.float) -- Prior for the transmitted signals. If ``output`` equals "bit", then LLRs for the transmitted bits are expected. If ``output`` equals "symbol", then logits for the transmitted constellation points are expected. + * **s** ([...,num_rx_ant,num_rx_ant], tf.complex) -- 2+D tensor containing the noise-plus-interference covariance matrices. + + It must generate one of the following outputs depending on the value of ``output``: + + * **b_hat** ([..., num_streams_per_rx, num_bits_per_symbol], tf.float) -- LLRs or hard-decisions for every bit of every stream, if ``output`` equals `"bit"`. + * **x_hat** ([..., num_streams_per_rx, num_points], tf.float) or ([..., num_streams_per_rx], tf.int) -- Logits or hard-decisions for constellation symbols for every stream, if ``output`` equals `"symbol"`. Hard-decisions correspond to the symbol indices. Parameters ---------- - output : One of ["bit", "symbol"], str - The type of output, either LLRs on bits or logits on constellation symbols. + detector : Callable + Callable object (e.g., a function) that implements a MIMO detection + algorithm with prior for arbitrary batch dimensions. Either the existing detector + :class:`~sionna.mimo.MaximumLikelihoodDetectorWithPrior` can be used, or a custom detector + callable provided that has the same input/output specification. - demapping_method : One of ["app", "maxlog"], str - The demapping method used. + output : One of ["bit", "symbol"], str + Type of output, either bits or symbols resource_grid : ResourceGrid - An instance of :class:`~sionna.ofdm.ResourceGrid`. + Instance of :class:`~sionna.ofdm.ResourceGrid` stream_management : StreamManagement - An instance of :class:`~sionna.mimo.StreamManagement`. + Instance of :class:`~sionna.mimo.StreamManagement` constellation_type : One of ["qam", "pam", "custom"], str For "custom", an instance of :class:`~sionna.mapping.Constellation` must be provided. num_bits_per_symbol : int - The number of bits per constellation symbol, e.g., 4 for QAM16. + Number of bits per constellation symbol, e.g., 4 for QAM16. Only required for ``constellation_type`` in ["qam", "pam"]. constellation : Constellation - An instance of :class:`~sionna.mapping.Constellation` or `None`. + Instance of :class:`~sionna.mapping.Constellation` or `None`. In the latter case, ``constellation_type`` and ``num_bits_per_symbol`` must be provided. - hard_out : bool - If `True`, the detector computes hard-decided bit values or - constellation point indices instead of soft-values. - Defaults to `False`. - dtype : One of [tf.complex64, tf.complex128] tf.DType (dtype) The dtype of `y`. Defaults to tf.complex64. The output dtype is the corresponding real dtype (tf.float32 or tf.float64). Input ------ - (y, h_hat, err_var, no) : + (y, h_hat, prior, err_var, no) : Tuple: y : [batch_size, num_rx, num_rx_ant, num_ofdm_symbols, fft_size], tf.complex - The received OFDM resource grid after cyclic prefix removal and FFT. + Received OFDM resource grid after cyclic prefix removal and FFT h_hat : [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, num_ofdm_symbols, num_effective_subcarriers], tf.complex - The channel estimates for all streams from all transmitters. + Channel estimates for all streams from all transmitters + + prior : [batch_size, num_tx, num_streams, num_data_symbols x num_bits_per_symbol] or [batch_size, num_tx, num_streams, num_data_symbols, num_points], tf.float + Prior of the transmitted signals. + If ``output`` equals "bit", LLRs of the transmitted bits are expected. + If ``output`` equals "symbol", logits of the transmitted constellation points are expected. err_var : [Broadcastable to shape of ``h_hat``], tf.float - The variance of the channel estimation error. + Variance of the channel estimation error no : [batch_size, num_rx, num_rx_ant] (or only the first n dims), tf.float - The variance of the AWGN noise. + Variance of the AWGN Output ------ @@ -428,59 +419,859 @@ class MaximumLikelihoodDetector(MaximumLikelihoodDetectorWithPrior): : [batch_size, num_tx, num_streams, num_data_symbols, num_points], tf.float or [batch_size, num_tx, num_streams, num_data_symbols], tf.int Logits or hard-decisions for constellation symbols for every stream, if ``output`` equals `"symbol"`. Hard-decisions correspond to the symbol indices. - - Note - ---- - If you want to use this layer in Graph mode with XLA, i.e., within - a function that is decorated with ``@tf.function(jit_compile=True)``, - you must set ``sionna.Config.xla_compat=true``. - See :py:attr:`~sionna.Config.xla_compat`. """ - def __init__(self, + detector, output, - demapping_method, resource_grid, stream_management, constellation_type=None, num_bits_per_symbol=None, constellation=None, - hard_out=False, dtype=tf.complex64, **kwargs): - - super().__init__(output=output, - demapping_method=demapping_method, + super().__init__(detector=detector, + output=output, resource_grid=resource_grid, stream_management=stream_management, - constellation_type=constellation_type, - num_bits_per_symbol=num_bits_per_symbol, - constellation=constellation, - hard_out=hard_out, dtype=dtype, **kwargs) + # Constellation object + self._constellation = Constellation.create_or_check_constellation( + constellation_type, + num_bits_per_symbol, + constellation, + dtype=dtype) + + # Precompute indices to map priors to a resource grid + rg_type = resource_grid.build_type_grid() + self._data_ind_scatter = tf.where(rg_type==0) + + # Overwrite the call() method of baseclass `BaseDetector` def call(self, inputs): - y, h_hat, err_var, no = inputs + y, h_hat, prior, err_var, no = inputs + # y has shape: + # [batch_size, num_rx, num_rx_ant, num_ofdm_symbols, fft_size] + + # h_hat has shape: + # [batch_size, num_rx, num_rx_ant, num_tx, num_streams,... + # ..., num_ofdm_symbols, num_effective_subcarriers] + + # prior has shape + # [batch_size, num_tx, num_streams,... + # ... num_data_symbols x num_bits_per_symbol] + # or [batch_size, num_tx, num_streams, num_data_symbols, num_points] + + # err_var has a shape that is broadcastable to h_hat + + # no has shape [batch_size, num_rx, num_rx_ant] + # or just the first n dimensions of this - batch_size = tf.shape(y)[0] - num_data_symbols = self._resource_grid.num_data_symbols - num_bits_per_symbol = self._constellation.num_bits_per_symbol - num_points = 2**num_bits_per_symbol - real_dtype = tf.as_dtype(self._dtype).real_dtype - - # Prior shape - if self._output == "bit": - dim_data = [num_data_symbols*num_bits_per_symbol] - else: - dim_data = [num_data_symbols, num_points] - prior_shape = tf.concat([[ batch_size, - self._resource_grid.num_tx, - self._resource_grid.num_streams_per_tx], - dim_data], axis=0) - - # Build null-prior - prior = tf.zeros(prior_shape, real_dtype) - - # Call - return super().call([y, h_hat, prior, err_var, no]) + ################################ + ### Pre-process the inputs + ################################ + y_dt, h_dt_desired, s = self._preprocess_inputs(y, h_hat, err_var, no) + + ######################### + ### Prepare the prior ### + ######################### + # [batch_size, num_tx, num_streams_per_tx, num_data_symbols, + # ... num_bits_per_symbol/num_points] + if self._output == 'bit': + prior = split_dim( prior, + [ self._resource_grid.num_data_symbols, + self._constellation.num_bits_per_symbol], + 3) + # Create a zero template for the prior + # [num_tx, num_streams_per_tx, num_ofdm_symbols,... + # ... num_effective_subcarriers, num_bits_per_symbol/num_points, + # ... batch_size] + template = tf.zeros([ self._resource_grid.num_tx, + self._resource_grid.num_streams_per_tx, + self._resource_grid.num_ofdm_symbols, + self._resource_grid.num_effective_subcarriers, + tf.shape(prior)[-1], + tf.shape(prior)[0]], + tf.as_dtype(self._dtype).real_dtype) + # [num_tx, num_streams_per_tx, num_data_symbols, + # ... num_bits_per_symbol/num_points, batch_size] + prior = tf.transpose(prior, [1, 2, 3, 4, 0]) + # [num_tx, num_streams_per_tx, num_ofdm_symbols,... + # ... num_effective_subcarriers, num_bits_per_symbol/num_points,... + # ... batch_size] + prior = flatten_dims(prior, 3, 0) + prior = tf.tensor_scatter_nd_update(template, self._data_ind_scatter, + prior) + # [batch_size, num_ofdm_symbols, num_effective_subcarriers,... + # num_tx*num_streams_per_tx, num_bits_per_symbol/num_points] + prior = tf.transpose(prior, [5, 2, 3, 0, 1, 4]) + prior = flatten_dims(prior, 2, 3) + # Add the receive antenna dimension for broadcasting + # [batch_size, num_rx, num_ofdm_symbols, num_effective_subcarriers,... + # num_tx*num_streams_per_tx, num_bits_per_symbol/num_points] + prior = tf.tile(tf.expand_dims(prior, axis=1), + [1, tf.shape(y)[1], 1, 1, 1, 1]) + + ################################# + ### Maximum-likelihood detection + ################################# + z = self._detector([y_dt, h_dt_desired, prior, s]) + + ############################################## + ### Extract data symbols for all detected TX + ############################################## + z = self._extract_datasymbols(z) + + return z + + +class MaximumLikelihoodDetector(OFDMDetector): + # pylint: disable=line-too-long + r"""MaximumLikelihoodDetector(output, demapping_method, resource_grid, stream_management, constellation_type=None, num_bits_per_symbol=None, constellation=None, hard_out=False, dtype=tf.complex64, **kwargs) + + Maximum-likelihood (ML) detection for OFDM MIMO transmissions. + + This layer implements maximum-likelihood (ML) detection + for OFDM MIMO transmissions. Both ML detection of symbols or bits with either + soft- or hard-decisions are supported. The OFDM and stream configuration are provided + by a :class:`~sionna.ofdm.ResourceGrid` and + :class:`~sionna.mimo.StreamManagement` instance, respectively. The + actual detector is an instance of :class:`~sionna.mimo.MaximumLikelihoodDetector`. + + Parameters + ---------- + output : One of ["bit", "symbol"], str + Type of output, either bits or symbols. Whether soft- or + hard-decisions are returned can be configured with the + ``hard_out`` flag. + + demapping_method : One of ["app", "maxlog"], str + Demapping method used + + resource_grid : ResourceGrid + Instance of :class:`~sionna.ofdm.ResourceGrid` + + stream_management : StreamManagement + Instance of :class:`~sionna.mimo.StreamManagement` + + constellation_type : One of ["qam", "pam", "custom"], str + For "custom", an instance of :class:`~sionna.mapping.Constellation` + must be provided. + + num_bits_per_symbol : int + Number of bits per constellation symbol, e.g., 4 for QAM16. + Only required for ``constellation_type`` in ["qam", "pam"]. + + constellation : Constellation + Instance of :class:`~sionna.mapping.Constellation` or `None`. + In the latter case, ``constellation_type`` + and ``num_bits_per_symbol`` must be provided. + + hard_out : bool + If `True`, the detector computes hard-decided bit values or + constellation point indices instead of soft-values. + Defaults to `False`. + + dtype : One of [tf.complex64, tf.complex128] tf.DType (dtype) + The dtype of `y`. Defaults to tf.complex64. + The output dtype is the corresponding real dtype (tf.float32 or tf.float64). + + Input + ------ + (y, h_hat, err_var, no) : + Tuple: + + y : [batch_size, num_rx, num_rx_ant, num_ofdm_symbols, fft_size], tf.complex + Received OFDM resource grid after cyclic prefix removal and FFT + + h_hat : [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, num_ofdm_symbols, num_effective_subcarriers], tf.complex + Channel estimates for all streams from all transmitters + + err_var : [Broadcastable to shape of ``h_hat``], tf.float + Variance of the channel estimation error + + no : [batch_size, num_rx, num_rx_ant] (or only the first n dims), tf.float + Variance of the AWGN noise + + Output + ------ + One of: + + : [batch_size, num_tx, num_streams, num_data_symbols*num_bits_per_symbol], tf.float + LLRs or hard-decisions for every bit of every stream, if ``output`` equals `"bit"`. + + : [batch_size, num_tx, num_streams, num_data_symbols, num_points], tf.float or [batch_size, num_tx, num_streams, num_data_symbols], tf.int + Logits or hard-decisions for constellation symbols for every stream, if ``output`` equals `"symbol"`. + Hard-decisions correspond to the symbol indices. + + Note + ---- + If you want to use this layer in Graph mode with XLA, i.e., within + a function that is decorated with ``@tf.function(jit_compile=True)``, + you must set ``sionna.Config.xla_compat=true``. + See :py:attr:`~sionna.Config.xla_compat`. + """ + + def __init__(self, + output, + demapping_method, + resource_grid, + stream_management, + constellation_type=None, + num_bits_per_symbol=None, + constellation=None, + hard_out=False, + dtype=tf.complex64, + **kwargs): + + # Instantiate the maximum-likelihood detector + detector = MaximumLikelihoodDetector_(output=output, + demapping_method=demapping_method, + num_streams = stream_management.num_streams_per_rx, + constellation_type=constellation_type, + num_bits_per_symbol=num_bits_per_symbol, + constellation=constellation, + hard_out=hard_out, + dtype=dtype, + **kwargs) + + super().__init__(detector=detector, + output=output, + resource_grid=resource_grid, + stream_management=stream_management, + dtype=dtype, + **kwargs) + + +class MaximumLikelihoodDetectorWithPrior(OFDMDetectorWithPrior): + # pylint: disable=line-too-long + r"""MaximumLikelihoodDetectorWithPrior(output, demapping_method, resource_grid, stream_management, constellation_type=None, num_bits_per_symbol=None, constellation=None, hard_out=False, dtype=tf.complex64, **kwargs) + + Maximum-likelihood (ML) detection for OFDM MIMO transmissions, assuming prior + knowledge of the bits or constellation points is available. + + This layer implements maximum-likelihood (ML) detection + for OFDM MIMO transmissions assuming prior knowledge on the transmitted data is available. + Both ML detection of symbols or bits with either + soft- or hard-decisions are supported. The OFDM and stream configuration are provided + by a :class:`~sionna.ofdm.ResourceGrid` and + :class:`~sionna.mimo.StreamManagement` instance, respectively. The + actual detector is an instance of :class:`~sionna.mimo.MaximumLikelihoodDetectorWithPrior`. + + Parameters + ---------- + output : One of ["bit", "symbol"], str + Type of output, either bits or symbols. Whether soft- or + hard-decisions are returned can be configured with the + ``hard_out`` flag. + + demapping_method : One of ["app", "maxlog"], str + Demapping method used + + resource_grid : ResourceGrid + Instance of :class:`~sionna.ofdm.ResourceGrid` + + stream_management : StreamManagement + Instance of :class:`~sionna.mimo.StreamManagement` + + constellation_type : One of ["qam", "pam", "custom"], str + For "custom", an instance of :class:`~sionna.mapping.Constellation` + must be provided. + + num_bits_per_symbol : int + Number of bits per constellation symbol, e.g., 4 for QAM16. + Only required for ``constellation_type`` in ["qam", "pam"]. + + constellation : Constellation + Instance of :class:`~sionna.mapping.Constellation` or `None`. + In the latter case, ``constellation_type`` + and ``num_bits_per_symbol`` must be provided. + + hard_out : bool + If `True`, the detector computes hard-decided bit values or + constellation point indices instead of soft-values. + Defaults to `False`. + + dtype : One of [tf.complex64, tf.complex128] tf.DType (dtype) + The dtype of `y`. Defaults to tf.complex64. + The output dtype is the corresponding real dtype (tf.float32 or tf.float64). + + Input + ------ + (y, h_hat, prior, err_var, no) : + Tuple: + + y : [batch_size, num_rx, num_rx_ant, num_ofdm_symbols, fft_size], tf.complex + Received OFDM resource grid after cyclic prefix removal and FFT + + h_hat : [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, num_ofdm_symbols, num_effective_subcarriers], tf.complex + Channel estimates for all streams from all transmitters + + prior : [batch_size, num_tx, num_streams, num_data_symbols x num_bits_per_symbol] or [batch_size, num_tx, num_streams, num_data_symbols, num_points], tf.float + Prior of the transmitted signals. + If ``output`` equals "bit", LLRs of the transmitted bits are expected. + If ``output`` equals "symbol", logits of the transmitted constellation points are expected. + + err_var : [Broadcastable to shape of ``h_hat``], tf.float + Variance of the channel estimation error + + no : [batch_size, num_rx, num_rx_ant] (or only the first n dims), tf.float + Variance of the AWGN noise + + Output + ------ + One of: + + : [batch_size, num_tx, num_streams, num_data_symbols*num_bits_per_symbol], tf.float + LLRs or hard-decisions for every bit of every stream, if ``output`` equals `"bit"`. + + : [batch_size, num_tx, num_streams, num_data_symbols, num_points], tf.float or [batch_size, num_tx, num_streams, num_data_symbols], tf.int + Logits or hard-decisions for constellation symbols for every stream, if ``output`` equals `"symbol"`. + Hard-decisions correspond to the symbol indices. + + Note + ---- + If you want to use this layer in Graph mode with XLA, i.e., within + a function that is decorated with ``@tf.function(jit_compile=True)``, + you must set ``sionna.Config.xla_compat=true``. + See :py:attr:`~sionna.Config.xla_compat`. + """ + + def __init__(self, + output, + demapping_method, + resource_grid, + stream_management, + constellation_type=None, + num_bits_per_symbol=None, + constellation=None, + hard_out=False, + dtype=tf.complex64, + **kwargs): + + # Instantiate the maximum-likelihood detector + detector = MaximumLikelihoodDetectorWithPrior_(output=output, + demapping_method=demapping_method, + num_streams = stream_management.num_streams_per_rx, + constellation_type=constellation_type, + num_bits_per_symbol=num_bits_per_symbol, + constellation=constellation, + hard_out=hard_out, + dtype=dtype, + **kwargs) + + super().__init__(detector=detector, + output=output, + resource_grid=resource_grid, + stream_management=stream_management, + constellation_type=constellation_type, + num_bits_per_symbol=num_bits_per_symbol, + constellation=constellation, + dtype=dtype, + **kwargs) + + +class LinearDetector(OFDMDetector): + # pylint: disable=line-too-long + r"""LinearDetector(equalizer, output, demapping_method, resource_grid, stream_management, constellation_type=None, num_bits_per_symbol=None, constellation=None, hard_out=False, dtype=tf.complex64, **kwargs) + + This layer wraps a MIMO linear equalizer and a :class:`~sionna.mapping.Demapper` + for use with the OFDM waveform. + + Both detection of symbols or bits with either + soft- or hard-decisions are supported. The OFDM and stream configuration are provided + by a :class:`~sionna.ofdm.ResourceGrid` and + :class:`~sionna.mimo.StreamManagement` instance, respectively. The + actual detector is an instance of :class:`~sionna.mimo.LinearDetector`. + + Parameters + ---------- + equalizer : str, one of ["lmmse", "zf", "mf"], or an equalizer function + Equalizer to be used. Either one of the existing equalizers, e.g., + :func:`~sionna.mimo.lmmse_equalizer`, :func:`~sionna.mimo.zf_equalizer`, or + :func:`~sionna.mimo.mf_equalizer` can be used, or a custom equalizer + function provided that has the same input/output specification. + + output : One of ["bit", "symbol"], str + Type of output, either bits or symbols. Whether soft- or + hard-decisions are returned can be configured with the + ``hard_out`` flag. + + demapping_method : One of ["app", "maxlog"], str + Demapping method used + + resource_grid : ResourceGrid + Instance of :class:`~sionna.ofdm.ResourceGrid` + + stream_management : StreamManagement + Instance of :class:`~sionna.mimo.StreamManagement` + + constellation_type : One of ["qam", "pam", "custom"], str + For "custom", an instance of :class:`~sionna.mapping.Constellation` + must be provided. + + num_bits_per_symbol : int + Number of bits per constellation symbol, e.g., 4 for QAM16. + Only required for ``constellation_type`` in ["qam", "pam"]. + + constellation : Constellation + Instance of :class:`~sionna.mapping.Constellation` or `None`. + In the latter case, ``constellation_type`` + and ``num_bits_per_symbol`` must be provided. + + hard_out : bool + If `True`, the detector computes hard-decided bit values or + constellation point indices instead of soft-values. + Defaults to `False`. + + dtype : One of [tf.complex64, tf.complex128] tf.DType (dtype) + The dtype of `y`. Defaults to tf.complex64. + The output dtype is the corresponding real dtype (tf.float32 or tf.float64). + + Input + ------ + (y, h_hat, err_var, no) : + Tuple: + + y : [batch_size, num_rx, num_rx_ant, num_ofdm_symbols, fft_size], tf.complex + Received OFDM resource grid after cyclic prefix removal and FFT + + h_hat : [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, num_ofdm_symbols, num_effective_subcarriers], tf.complex + Channel estimates for all streams from all transmitters + + err_var : [Broadcastable to shape of ``h_hat``], tf.float + Variance of the channel estimation error + + no : [batch_size, num_rx, num_rx_ant] (or only the first n dims), tf.float + Variance of the AWGN + + Output + ------ + One of: + + : [batch_size, num_tx, num_streams, num_data_symbols*num_bits_per_symbol], tf.float + LLRs or hard-decisions for every bit of every stream, if ``output`` equals `"bit"`. + + : [batch_size, num_tx, num_streams, num_data_symbols, num_points], tf.float or [batch_size, num_tx, num_streams, num_data_symbols], tf.int + Logits or hard-decisions for constellation symbols for every stream, if ``output`` equals `"symbol"`. + Hard-decisions correspond to the symbol indices. + + Note + ---- + If you want to use this layer in Graph mode with XLA, i.e., within + a function that is decorated with ``@tf.function(jit_compile=True)``, + you must set ``sionna.Config.xla_compat=true``. + See :py:attr:`~sionna.Config.xla_compat`. + """ + + def __init__(self, + equalizer, + output, + demapping_method, + resource_grid, + stream_management, + constellation_type=None, + num_bits_per_symbol=None, + constellation=None, + hard_out=False, + dtype=tf.complex64, + **kwargs): + + # Instantiate the maximum-likelihood detector + detector = LinearDetector_(equalizer=equalizer, + output=output, + demapping_method=demapping_method, + constellation_type=constellation_type, + num_bits_per_symbol=num_bits_per_symbol, + constellation=constellation, + hard_out=hard_out, + dtype=dtype, + **kwargs) + + super().__init__(detector=detector, + output=output, + resource_grid=resource_grid, + stream_management=stream_management, + dtype=dtype, + **kwargs) + + +class KBestDetector(OFDMDetector): + # pylint: disable=line-too-long + r"""KBestDetector(output, num_streams, k, resource_grid, stream_management, constellation_type=None, num_bits_per_symbol=None, constellation=None, hard_out=False, use_real_rep=False, list2llr=None, dtype=tf.complex64, **kwargs) + + This layer wraps the MIMO K-Best detector for use with the OFDM waveform. + + Both detection of symbols or bits with either + soft- or hard-decisions are supported. The OFDM and stream configuration are provided + by a :class:`~sionna.ofdm.ResourceGrid` and + :class:`~sionna.mimo.StreamManagement` instance, respectively. The + actual detector is an instance of :class:`~sionna.mimo.KBestDetector`. + + Parameters + ---------- + output : One of ["bit", "symbol"], str + Type of output, either bits or symbols. Whether soft- or + hard-decisions are returned can be configured with the + ``hard_out`` flag. + + num_streams : tf.int + Number of transmitted streams + + k : tf.int + Number of paths to keep. Cannot be larger than the + number of constellation points to the power of the number of + streams. + + resource_grid : ResourceGrid + Instance of :class:`~sionna.ofdm.ResourceGrid` + + stream_management : StreamManagement + Instance of :class:`~sionna.mimo.StreamManagement` + + constellation_type : One of ["qam", "pam", "custom"], str + For "custom", an instance of :class:`~sionna.mapping.Constellation` + must be provided. + + num_bits_per_symbol : int + Number of bits per constellation symbol, e.g., 4 for QAM16. + Only required for ``constellation_type`` in ["qam", "pam"]. + + constellation : Constellation + Instance of :class:`~sionna.mapping.Constellation` or `None`. + In the latter case, ``constellation_type`` + and ``num_bits_per_symbol`` must be provided. + + hard_out : bool + If `True`, the detector computes hard-decided bit values or + constellation point indices instead of soft-values. + Defaults to `False`. + + use_real_rep : bool + If `True`, the detector use the real-valued equivalent representation + of the channel. Note that this only works with a QAM constellation. + Defaults to `False`. + + list2llr: `None` or instance of :class:`~sionna.mimo.List2LLR` + The function to be used to compute LLRs from a list of candidate solutions. + If `None`, the default solution :class:`~sionna.mimo.List2LLRSimple` + is used. + + dtype : One of [tf.complex64, tf.complex128] tf.DType (dtype) + The dtype of `y`. Defaults to tf.complex64. + The output dtype is the corresponding real dtype (tf.float32 or tf.float64). + + Input + ------ + (y, h_hat, err_var, no) : + Tuple: + + y : [batch_size, num_rx, num_rx_ant, num_ofdm_symbols, fft_size], tf.complex + Received OFDM resource grid after cyclic prefix removal and FFT + + h_hat : [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, num_ofdm_symbols, num_effective_subcarriers], tf.complex + Channel estimates for all streams from all transmitters + + err_var : [Broadcastable to shape of ``h_hat``], tf.float + Variance of the channel estimation error + + no : [batch_size, num_rx, num_rx_ant] (or only the first n dims), tf.float + Variance of the AWGN + + Output + ------ + One of: + + : [batch_size, num_tx, num_streams, num_data_symbols*num_bits_per_symbol], tf.float + LLRs or hard-decisions for every bit of every stream, if ``output`` equals `"bit"`. + + : [batch_size, num_tx, num_streams, num_data_symbols, num_points], tf.float or [batch_size, num_tx, num_streams, num_data_symbols], tf.int + Logits or hard-decisions for constellation symbols for every stream, if ``output`` equals `"symbol"`. + Hard-decisions correspond to the symbol indices. + + Note + ---- + If you want to use this layer in Graph mode with XLA, i.e., within + a function that is decorated with ``@tf.function(jit_compile=True)``, + you must set ``sionna.Config.xla_compat=true``. + See :py:attr:`~sionna.Config.xla_compat`. + """ + + def __init__(self, + output, + num_streams, + k, + resource_grid, + stream_management, + constellation_type=None, + num_bits_per_symbol=None, + constellation=None, + hard_out=False, + use_real_rep=False, + list2llr="default", + dtype=tf.complex64, + **kwargs): + + # Instantiate the K-Best detector + detector = KBestDetector_(output=output, + num_streams=num_streams, + k=k, + constellation_type=constellation_type, + num_bits_per_symbol=num_bits_per_symbol, + constellation=constellation, + hard_out=hard_out, + use_real_rep=use_real_rep, + list2llr=list2llr, + dtype=dtype, + **kwargs) + + super().__init__(detector=detector, + output=output, + resource_grid=resource_grid, + stream_management=stream_management, + dtype=dtype, + **kwargs) + + +class EPDetector(OFDMDetector): + # pylint: disable=line-too-long + r"""EPDetector(output, resource_grid, stream_management, num_bits_per_symbol, hard_out=False, l=10, beta=0.9, dtype=tf.complex64, **kwargs) + + This layer wraps the MIMO EP detector for use with the OFDM waveform. + + Both detection of symbols or bits with either + soft- or hard-decisions are supported. The OFDM and stream configuration are provided + by a :class:`~sionna.ofdm.ResourceGrid` and + :class:`~sionna.mimo.StreamManagement` instance, respectively. The + actual detector is an instance of :class:`~sionna.mimo.EPDetector`. + + Parameters + ---------- + output : One of ["bit", "symbol"], str + Type of output, either bits or symbols. Whether soft- or + hard-decisions are returned can be configured with the + ``hard_out`` flag. + + resource_grid : ResourceGrid + Instance of :class:`~sionna.ofdm.ResourceGrid` + + stream_management : StreamManagement + Instance of :class:`~sionna.mimo.StreamManagement` + + num_bits_per_symbol : int + Number of bits per constellation symbol, e.g., 4 for QAM16. + Only required for ``constellation_type`` in ["qam", "pam"]. + + hard_out : bool + If `True`, the detector computes hard-decided bit values or + constellation point indices instead of soft-values. + Defaults to `False`. + + l : int + Number of iterations. Defaults to 10. + + beta : float + Parameter :math:`\beta\in[0,1]` for update smoothing. + Defaults to 0.9. + + dtype : One of [tf.complex64, tf.complex128] tf.DType (dtype) + Precision used for internal computations. Defaults to ``tf.complex64``. + Especially for large MIMO setups, the precision can make a significant + performance difference. + + Input + ------ + (y, h_hat, err_var, no) : + Tuple: + + y : [batch_size, num_rx, num_rx_ant, num_ofdm_symbols, fft_size], tf.complex + Received OFDM resource grid after cyclic prefix removal and FFT + + h_hat : [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, num_ofdm_symbols, num_effective_subcarriers], tf.complex + Channel estimates for all streams from all transmitters + + err_var : [Broadcastable to shape of ``h_hat``], tf.float + Variance of the channel estimation error + + no : [batch_size, num_rx, num_rx_ant] (or only the first n dims), tf.float + Variance of the AWGN + + Output + ------ + One of: + + : [batch_size, num_tx, num_streams, num_data_symbols*num_bits_per_symbol], tf.float + LLRs or hard-decisions for every bit of every stream, if ``output`` equals `"bit"`. + + : [batch_size, num_tx, num_streams, num_data_symbols, num_points], tf.float or [batch_size, num_tx, num_streams, num_data_symbols], tf.int + Logits or hard-decisions for constellation symbols for every stream, if ``output`` equals `"symbol"`. + Hard-decisions correspond to the symbol indices. + + Note + ---- + For numerical stability, we do not recommend to use this function in Graph + mode with XLA, i.e., within a function that is decorated with + ``@tf.function(jit_compile=True)``. + However, it is possible to do so by setting + ``sionna.Config.xla_compat=true``. + See :py:attr:`~sionna.Config.xla_compat`. + """ + def __init__(self, + output, + resource_grid, + stream_management, + num_bits_per_symbol=None, + hard_out=False, + l=10, + beta=0.9, + dtype=tf.complex64, + **kwargs): + + # Instantiate the EP detector + detector = EPDetector_(output=output, + num_bits_per_symbol=num_bits_per_symbol, + hard_out=hard_out, + l=l, + beta=beta, + dtype=dtype, + **kwargs) + + super().__init__(detector=detector, + output=output, + resource_grid=resource_grid, + stream_management=stream_management, + dtype=dtype, + **kwargs) + +class MMSEPICDetector(OFDMDetectorWithPrior): + # pylint: disable=line-too-long + r"""MMSEPICDetector(output, resource_grid, stream_management, demapping_method="maxlog", num_iter=1, constellation_type=None, num_bits_per_symbol=None, constellation=None, hard_out=False, dtype=tf.complex64, **kwargs) + + This layer wraps the MIMO MMSE PIC detector for use with the OFDM waveform. + + Both detection of symbols or bits with either + soft- or hard-decisions are supported. The OFDM and stream configuration are provided + by a :class:`~sionna.ofdm.ResourceGrid` and + :class:`~sionna.mimo.StreamManagement` instance, respectively. The + actual detector is an instance of :class:`~sionna.mimo.MMSEPICDetector`. + + Parameters + ---------- + output : One of ["bit", "symbol"], str + Type of output, either bits or symbols. Whether soft- or + hard-decisions are returned can be configured with the + ``hard_out`` flag. + + resource_grid : ResourceGrid + Instance of :class:`~sionna.ofdm.ResourceGrid` + + stream_management : StreamManagement + Instance of :class:`~sionna.mimo.StreamManagement` + + demapping_method : One of ["app", "maxlog"], str + The demapping method used. + Defaults to "maxlog". + + num_iter : int + Number of MMSE PIC iterations. + Defaults to 1. + + constellation_type : One of ["qam", "pam", "custom"], str + For "custom", an instance of :class:`~sionna.mapping.Constellation` + must be provided. + + num_bits_per_symbol : int + The number of bits per constellation symbol, e.g., 4 for QAM16. + Only required for ``constellation_type`` in ["qam", "pam"]. + + constellation : Constellation + An instance of :class:`~sionna.mapping.Constellation` or `None`. + In the latter case, ``constellation_type`` + and ``num_bits_per_symbol`` must be provided. + + hard_out : bool + If `True`, the detector computes hard-decided bit values or + constellation point indices instead of soft-values. + Defaults to `False`. + + dtype : One of [tf.complex64, tf.complex128] tf.DType (dtype) + Precision used for internal computations. Defaults to ``tf.complex64``. + Especially for large MIMO setups, the precision can make a significant + performance difference. + + Input + ------ + (y, h_hat, prior, err_var, no) : + Tuple: + + y : [batch_size, num_rx, num_rx_ant, num_ofdm_symbols, fft_size], tf.complex + Received OFDM resource grid after cyclic prefix removal and FFT + + h_hat : [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, num_ofdm_symbols, num_effective_subcarriers], tf.complex + Channel estimates for all streams from all transmitters + + prior : [batch_size, num_tx, num_streams, num_data_symbols x num_bits_per_symbol] or [batch_size, num_tx, num_streams, num_data_symbols, num_points], tf.float + Prior of the transmitted signals. + If ``output`` equals "bit", LLRs of the transmitted bits are expected. + If ``output`` equals "symbol", logits of the transmitted constellation points are expected. + + err_var : [Broadcastable to shape of ``h_hat``], tf.float + Variance of the channel estimation error + + no : [batch_size, num_rx, num_rx_ant] (or only the first n dims), tf.float + Variance of the AWGN + + Output + ------ + One of: + + : [batch_size, num_tx, num_streams, num_data_symbols*num_bits_per_symbol], tf.float + LLRs or hard-decisions for every bit of every stream, if ``output`` equals `"bit"`. + + : [batch_size, num_tx, num_streams, num_data_symbols, num_points], tf.float or [batch_size, num_tx, num_streams, num_data_symbols], tf.int + Logits or hard-decisions for constellation symbols for every stream, if ``output`` equals `"symbol"`. + Hard-decisions correspond to the symbol indices. + + Note + ---- + For numerical stability, we do not recommend to use this function in Graph + mode with XLA, i.e., within a function that is decorated with + ``@tf.function(jit_compile=True)``. + However, it is possible to do so by setting + ``sionna.Config.xla_compat=true``. + See :py:attr:`~sionna.Config.xla_compat`. + """ + def __init__(self, + output, + resource_grid, + stream_management, + demapping_method="maxlog", + num_iter=1, + constellation_type=None, + num_bits_per_symbol=None, + constellation=None, + hard_out=False, + dtype=tf.complex64, + **kwargs): + + # Instantiate the EP detector + detector = MMSEPICDetector_(output=output, + demapping_method=demapping_method, + num_iter=num_iter, + constellation_type=constellation_type, + num_bits_per_symbol=num_bits_per_symbol, + constellation=constellation, + hard_out=hard_out, + dtype=dtype, + **kwargs) + + super().__init__(detector=detector, + output=output, + resource_grid=resource_grid, + stream_management=stream_management, + constellation_type=constellation_type, + num_bits_per_symbol=num_bits_per_symbol, + constellation=constellation, + dtype=dtype, + **kwargs) diff --git a/sionna/ofdm/equalization.py b/sionna/ofdm/equalization.py index cb4bf723..3d517bf5 100644 --- a/sionna/ofdm/equalization.py +++ b/sionna/ofdm/equalization.py @@ -8,37 +8,54 @@ from tensorflow.keras.layers import Layer import sionna from sionna.utils import flatten_dims, split_dim, flatten_last_dims, expand_to_rank -from sionna.mimo import lmmse_equalizer +from sionna.mimo import lmmse_equalizer, zf_equalizer, mf_equalizer from sionna.ofdm import RemoveNulledSubcarriers -class LMMSEEqualizer(Layer): +class OFDMEqualizer(Layer): # pylint: disable=line-too-long - """LMMSEEqualizer(resource_grid, stream_management, whiten_interference=True, dtype=tf.complex64, **kwargs) + r"""OFDMEqualizer(equalizer, resource_grid, stream_management, dtype=tf.complex64, **kwargs) + + Layer that wraps a MIMO equalizer for use with the OFDM waveform. + + The parameter ``equalizer`` is a callable (e.g., a function) that + implements a MIMO equalization algorithm for arbitrary batch dimensions. + + This class pre-processes the received resource grid ``y`` and channel + estimate ``h_hat``, and computes for each receiver the + noise-plus-interference covariance matrix according to the OFDM and stream + configuration provided by the ``resource_grid`` and + ``stream_management``, which also accounts for the channel + estimation error variance ``err_var``. These quantities serve as input + to the equalization algorithm that is implemented by the callable ``equalizer``. + This layer computes soft-symbol estimates together with effective noise + variances for all streams which can, e.g., be used by a + :class:`~sionna.mapping.Demapper` to obtain LLRs. - LMMSE equalization for OFDM MIMO transmissions. + Note + ----- + The callable ``equalizer`` must take three inputs: - This layer computes linear minimum mean squared error (LMMSE) estimation - for OFDM MIMO transmissions. The OFDM and stream configuration are provided - by a :class:`~sionna.ofdm.ResourceGrid` and - :class:`~sionna.mimo.StreamManagement` instance, respectively. The - detection algorithm is the :meth:`~sionna.mimo.lmmse_equalizer`. The layer - computes soft-symbol estimates together with effective noise variances - for all streams which can, e.g., be used by a - :class:`~sionna.mapping.Demapper` to obtain LLRs. + * **y** ([...,num_rx_ant], tf.complex) -- 1+D tensor containing the received signals. + * **h** ([...,num_rx_ant,num_streams_per_rx], tf.complex) -- 2+D tensor containing the channel matrices. + * **s** ([...,num_rx_ant,num_rx_ant], tf.complex) -- 2+D tensor containing the noise-plus-interference covariance matrices. + + It must generate two outputs: + + * **x_hat** ([...,num_streams_per_rx], tf.complex) -- 1+D tensor representing the estimated symbol vectors. + * **no_eff** (tf.float) -- Tensor of the same shape as ``x_hat`` containing the effective noise variance estimates. Parameters ---------- + equalizer : Callable + Callable object (e.g., a function) that implements a MIMO equalization + algorithm for arbitrary batch dimensions + resource_grid : ResourceGrid - An instance of :class:`~sionna.ofdm.ResourceGrid`. + Instance of :class:`~sionna.ofdm.ResourceGrid` stream_management : StreamManagement - An instance of :class:`~sionna.mimo.StreamManagement`. - - whiten_interference : bool - If `True` (default), the interference is first whitened before equalization. - In this case, an alternative expression for the receive filter is used which - can be numerically more stable. + Instance of :class:`~sionna.mimo.StreamManagement` dtype : tf.Dtype Datatype for internal calculations and the output dtype. @@ -50,44 +67,38 @@ class LMMSEEqualizer(Layer): Tuple: y : [batch_size, num_rx, num_rx_ant, num_ofdm_symbols, fft_size], tf.complex - The received OFDM resource grid after cyclic prefix removal and FFT. + Received OFDM resource grid after cyclic prefix removal and FFT h_hat : [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, num_ofdm_symbols, num_effective_subcarriers], tf.complex - The channel estimates for all streams from all transmitters. + Channel estimates for all streams from all transmitters err_var : [Broadcastable to shape of ``h_hat``], tf.float - The variance of the channel estimation error. + Variance of the channel estimation error no : [batch_size, num_rx, num_rx_ant] (or only the first n dims), tf.float - The variance of the AWGN noise. + Variance of the AWGN Output ------ x_hat : [batch_size, num_tx, num_streams, num_data_symbols], tf.complex - The estimated symbols. + Estimated symbols no_eff : [batch_size, num_tx, num_streams, num_data_symbols], tf.float - The effective noise variance for each estimated symbol. - - Note - ---- - If you want to use this layer in Graph mode with XLA, i.e., within - a function that is decorated with ``@tf.function(jit_compile=True)``, - you must set ``sionna.Config.xla_compat=true``. - See :py:attr:`~sionna.Config.xla_compat`. + Effective noise variance for each estimated symbol """ def __init__(self, + equalizer, resource_grid, stream_management, - whiten_interference=True, dtype=tf.complex64, **kwargs): super().__init__(dtype=dtype, **kwargs) + assert callable(equalizer) assert isinstance(resource_grid, sionna.ofdm.ResourceGrid) assert isinstance(stream_management, sionna.mimo.StreamManagement) + self._equalizer = equalizer self._resource_grid = resource_grid self._stream_management = stream_management - self._whiten_interference = whiten_interference self._removed_nulled_scs = RemoveNulledSubcarriers(self._resource_grid) # Precompute indices to extract data symbols @@ -160,8 +171,12 @@ def call(self, inputs): # Split first dimension to separate RX and TX: # [num_rx, num_streams_per_rx, batch_size, num_rx_ant, ... # ..., num_ofdm_symbols, num_effective_subcarriers] - h_dt_desired = split_dim(h_dt_desired, [self._stream_management.num_rx, -1], 0) - h_dt_undesired = split_dim(h_dt_undesired, [self._stream_management.num_rx, -1], 0) + h_dt_desired = split_dim(h_dt_desired, + [self._stream_management.num_rx, + self._stream_management.num_streams_per_rx], + 0) + h_dt_undesired = split_dim(h_dt_undesired, + [self._stream_management.num_rx, -1], 0) # Permutate dims to # [batch_size, num_rx, num_ofdm_symbols, num_effective_subcarriers,.. @@ -205,12 +220,11 @@ def call(self, inputs): s = tf.cast(s, self._dtype) ############################################################ - #### Compute LMMSE estimate and effective noise variance ### + ### Compute symbol estimate and effective noise variance ### ############################################################ # [batch_size, num_rx, num_ofdm_symbols, num_effective_subcarriers,... # ..., num_stream_per_rx] - x_hat, no_eff = lmmse_equalizer(y_dt, h_dt_desired, - s, self._whiten_interference) + x_hat, no_eff = self._equalizer(y_dt, h_dt_desired, s) ################################################ ### Extract data symbols for all detected TX ### @@ -261,3 +275,223 @@ def call(self, inputs): no_eff = tf.transpose(no_eff, [3, 0, 1, 2]) return (x_hat, no_eff) + + +class LMMSEEqualizer(OFDMEqualizer): + # pylint: disable=line-too-long + """LMMSEEqualizer(resource_grid, stream_management, whiten_interference=True, dtype=tf.complex64, **kwargs) + + LMMSE equalization for OFDM MIMO transmissions. + + This layer computes linear minimum mean squared error (LMMSE) equalization + for OFDM MIMO transmissions. The OFDM and stream configuration are provided + by a :class:`~sionna.ofdm.ResourceGrid` and + :class:`~sionna.mimo.StreamManagement` instance, respectively. The + detection algorithm is the :meth:`~sionna.mimo.lmmse_equalizer`. The layer + computes soft-symbol estimates together with effective noise variances + for all streams which can, e.g., be used by a + :class:`~sionna.mapping.Demapper` to obtain LLRs. + + Parameters + ---------- + resource_grid : ResourceGrid + Instance of :class:`~sionna.ofdm.ResourceGrid` + + stream_management : StreamManagement + Instance of :class:`~sionna.mimo.StreamManagement` + + whiten_interference : bool + If `True` (default), the interference is first whitened before equalization. + In this case, an alternative expression for the receive filter is used which + can be numerically more stable. + + dtype : tf.Dtype + Datatype for internal calculations and the output dtype. + Defaults to `tf.complex64`. + + Input + ----- + (y, h_hat, err_var, no) : + Tuple: + + y : [batch_size, num_rx, num_rx_ant, num_ofdm_symbols, fft_size], tf.complex + Received OFDM resource grid after cyclic prefix removal and FFT + + h_hat : [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, num_ofdm_symbols, num_effective_subcarriers], tf.complex + Channel estimates for all streams from all transmitters + + err_var : [Broadcastable to shape of ``h_hat``], tf.float + Variance of the channel estimation error + + no : [batch_size, num_rx, num_rx_ant] (or only the first n dims), tf.float + Variance of the AWGN + + Output + ------ + x_hat : [batch_size, num_tx, num_streams, num_data_symbols], tf.complex + Estimated symbols + + no_eff : [batch_size, num_tx, num_streams, num_data_symbols], tf.float + Effective noise variance for each estimated symbol + + Note + ---- + If you want to use this layer in Graph mode with XLA, i.e., within + a function that is decorated with ``@tf.function(jit_compile=True)``, + you must set ``sionna.Config.xla_compat=true``. + See :py:attr:`~sionna.Config.xla_compat`. + """ + def __init__(self, + resource_grid, + stream_management, + whiten_interference=True, + dtype=tf.complex64, + **kwargs): + + def equalizer(y, h, s): + return lmmse_equalizer(y, h, s, whiten_interference) + + super().__init__(equalizer=equalizer, + resource_grid=resource_grid, + stream_management=stream_management, + dtype=dtype, **kwargs) + + +class ZFEqualizer(OFDMEqualizer): + # pylint: disable=line-too-long + """ZFEqualizer(resource_grid, stream_management, dtype=tf.complex64, **kwargs) + + ZF equalization for OFDM MIMO transmissions. + + This layer computes zero-forcing (ZF) equalization + for OFDM MIMO transmissions. The OFDM and stream configuration are provided + by a :class:`~sionna.ofdm.ResourceGrid` and + :class:`~sionna.mimo.StreamManagement` instance, respectively. The + detection algorithm is the :meth:`~sionna.mimo.zf_equalizer`. The layer + computes soft-symbol estimates together with effective noise variances + for all streams which can, e.g., be used by a + :class:`~sionna.mapping.Demapper` to obtain LLRs. + + Parameters + ---------- + resource_grid : ResourceGrid + An instance of :class:`~sionna.ofdm.ResourceGrid`. + + stream_management : StreamManagement + An instance of :class:`~sionna.mimo.StreamManagement`. + + dtype : tf.Dtype + Datatype for internal calculations and the output dtype. + Defaults to `tf.complex64`. + + Input + ----- + (y, h_hat, err_var, no) : + Tuple: + + y : [batch_size, num_rx, num_rx_ant, num_ofdm_symbols, fft_size], tf.complex + Received OFDM resource grid after cyclic prefix removal and FFT + + h_hat : [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, num_ofdm_symbols, num_effective_subcarriers], tf.complex + Channel estimates for all streams from all transmitters + + err_var : [Broadcastable to shape of ``h_hat``], tf.float + Variance of the channel estimation error + + no : [batch_size, num_rx, num_rx_ant] (or only the first n dims), tf.float + Variance of the AWGN + + Output + ------ + x_hat : [batch_size, num_tx, num_streams, num_data_symbols], tf.complex + Estimated symbols + + no_eff : [batch_size, num_tx, num_streams, num_data_symbols], tf.float + Effective noise variance for each estimated symbol + + Note + ---- + If you want to use this layer in Graph mode with XLA, i.e., within + a function that is decorated with ``@tf.function(jit_compile=True)``, + you must set ``sionna.Config.xla_compat=true``. + See :py:attr:`~sionna.Config.xla_compat`. + """ + def __init__(self, + resource_grid, + stream_management, + dtype=tf.complex64, + **kwargs): + super().__init__(equalizer=zf_equalizer, + resource_grid=resource_grid, + stream_management=stream_management, + dtype=dtype, **kwargs) + + +class MFEqualizer(OFDMEqualizer): + # pylint: disable=line-too-long + """MFEqualizer(resource_grid, stream_management, dtype=tf.complex64, **kwargs) + + MF equalization for OFDM MIMO transmissions. + + This layer computes matched filter (MF) equalization + for OFDM MIMO transmissions. The OFDM and stream configuration are provided + by a :class:`~sionna.ofdm.ResourceGrid` and + :class:`~sionna.mimo.StreamManagement` instance, respectively. The + detection algorithm is the :meth:`~sionna.mimo.mf_equalizer`. The layer + computes soft-symbol estimates together with effective noise variances + for all streams which can, e.g., be used by a + :class:`~sionna.mapping.Demapper` to obtain LLRs. + + Parameters + ---------- + resource_grid : ResourceGrid + An instance of :class:`~sionna.ofdm.ResourceGrid`. + + stream_management : StreamManagement + An instance of :class:`~sionna.mimo.StreamManagement`. + + dtype : tf.Dtype + Datatype for internal calculations and the output dtype. + Defaults to `tf.complex64`. + + Input + ----- + (y, h_hat, err_var, no) : + Tuple: + + y : [batch_size, num_rx, num_rx_ant, num_ofdm_symbols, fft_size], tf.complex + Received OFDM resource grid after cyclic prefix removal and FFT + + h_hat : [batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, num_ofdm_symbols, num_effective_subcarriers], tf.complex + Channel estimates for all streams from all transmitters + + err_var : [Broadcastable to shape of ``h_hat``], tf.float + Variance of the channel estimation error + + no : [batch_size, num_rx, num_rx_ant] (or only the first n dims), tf.float + Variance of the AWGN + + Output + ------ + x_hat : [batch_size, num_tx, num_streams, num_data_symbols], tf.complex + Estimated symbols + + no_eff : [batch_size, num_tx, num_streams, num_data_symbols], tf.float + Effective noise variance for each estimated symbol + + Note + ---- + If you want to use this layer in Graph mode with XLA, i.e., within + a function that is decorated with ``@tf.function(jit_compile=True)``, + you must set ``sionna.Config.xla_compat=true``. + See :py:attr:`~sionna.Config.xla_compat`. + """ + def __init__(self, + resource_grid, + stream_management, + dtype=tf.complex64, + **kwargs): + super().__init__(equalizer=mf_equalizer, + resource_grid=resource_grid, + stream_management=stream_management, + dtype=dtype, **kwargs) diff --git a/sionna/ofdm/pilot_pattern.py b/sionna/ofdm/pilot_pattern.py index 0f29a81e..360b344d 100644 --- a/sionna/ofdm/pilot_pattern.py +++ b/sionna/ofdm/pilot_pattern.py @@ -51,38 +51,40 @@ def __init__(self, mask, pilots, trainable=False, normalize=False, @property def num_tx(self): - """The number of transmitters.""" + """Number of transmitters""" return self._mask.shape[0] @property def num_streams_per_tx(self): - """The number of streams per transmitter.""" + """Number of streams per transmitter""" return self._mask.shape[1] @ property def num_ofdm_symbols(self): - """The number of OFDM symbols.""" + """Number of OFDM symbols""" return self._mask.shape[2] @ property def num_effective_subcarriers(self): - """The number of effectvie subcarriers.""" + """Number of effectvie subcarriers""" return self._mask.shape[3] @property def num_pilot_symbols(self): - """Number of pilot symbols per transmit antenna.""" + """Number of pilot symbols per transmit antenna""" return tf.shape(self._pilots)[-1] @property def num_data_symbols(self): - """ Number of data symbols per transmit antenna.""" + """ Number of data symbols per transmit antenna""" return tf.shape(self._mask)[-1]*tf.shape(self._mask)[-2] - \ self.num_pilot_symbols @property def normalize(self): - """Indicates if the pilots are normalized or not.""" + """Returns or sets the flag indicating if the pilots + are normalized or not + """ return self._normalize @normalize.setter @@ -91,12 +93,16 @@ def normalize(self, value): @property def mask(self): - """The mask of the pilot pattern.""" + """Mask of the pilot pattern""" return self._mask @property def pilots(self): - """Returns the possibly normalized tensor of pilot symbols.""" + """Returns or sets the possibly normalized tensor of pilot symbols. + If pilots are normalized, the normalization will be applied + after new values for pilots have been set. If this is + not the desired behavior, turn normalization off. + """ def norm_pilots(): scale = tf.abs(self._pilots)**2 scale = 1/tf.sqrt(tf.reduce_mean(scale, axis=-1, keepdims=True)) @@ -105,6 +111,10 @@ def norm_pilots(): return tf.cond(self.normalize, norm_pilots, lambda: self._pilots) + @pilots.setter + def pilots(self, value): + self._pilots.assign(value) + def _check_settings(self): """Validate that all properties define a valid pilot pattern.""" @@ -125,6 +135,12 @@ def _check_settings(self): return True + @property + def trainable(self): + """Returns if pilots are trainable or not""" + return self._pilots.trainable + + def show(self, tx_ind=None, stream_ind=None, show_pilot_ind=False): """Visualizes the non-zero pilots for some transmitters and streams. diff --git a/sionna/ofdm/resource_grid.py b/sionna/ofdm/resource_grid.py index dcbd3945..38cf9a49 100644 --- a/sionna/ofdm/resource_grid.py +++ b/sionna/ofdm/resource_grid.py @@ -355,18 +355,22 @@ def build(self, input_shape): # pylint: disable=unused-argument which is prefilled with pilots and stores indices to scatter data symbols. """ - rg_type = self._resource_grid.build_type_grid() - pilot_ind = tf.where(rg_type==1) - pilots = flatten_last_dims(self._resource_grid.pilot_pattern.pilots, 3) - self._template = tf.scatter_nd(pilot_ind, pilots, rg_type.shape) - self._template = tf.expand_dims(self._template, -1) - self._data_ind = tf.where(rg_type==0) + self._rg_type = self._resource_grid.build_type_grid() + self._pilot_ind = tf.where(self._rg_type==1) + self._data_ind = tf.where(self._rg_type==0) def call(self, inputs): + # Map pilots on empty resource grid + pilots = flatten_last_dims(self._resource_grid.pilot_pattern.pilots, 3) + template = tf.scatter_nd(self._pilot_ind, + pilots, + self._rg_type.shape) + template = tf.expand_dims(template, -1) + # Broadcast the resource grid template to batch_size batch_size = tf.shape(inputs)[0] - new_shape = tf.concat([tf.shape(self._template)[:-1], [batch_size]], 0) - template = tf.broadcast_to(self._template, new_shape) + new_shape = tf.concat([tf.shape(template)[:-1], [batch_size]], 0) + template = tf.broadcast_to(template, new_shape) # Flatten the inputs and put batch_dim last for scatter update inputs = tf.transpose(flatten_last_dims(inputs, 3)) diff --git a/sionna/utils/misc.py b/sionna/utils/misc.py index f5fe2613..71361fce 100644 --- a/sionna/utils/misc.py +++ b/sionna/utils/misc.py @@ -407,6 +407,7 @@ def sim_ber(mc_fun, num_target_bit_errors=None, num_target_block_errors=None, early_stop=True, + graph_mode=None, verbose=True, forward_keyboard_interrupt=True, dtype=tf.complex64): @@ -420,7 +421,7 @@ def sim_ber(mc_fun, Input ----- mc_fun: - Function that yields the transmitted bits `b` and the + Callable that yields the transmitted bits `b` and the receiver's estimate `b_hat` for a given ``batch_size`` and ``ebno_db``. If ``soft_estimates`` is True, b_hat is interpreted as logit. @@ -452,6 +453,10 @@ def sim_ber(mc_fun, first error-free SNR point (i.e., no error occurred after ``max_mc_iter`` Monte-Carlo iterations). + graph_mode: One of ["graph", "xla"], str + A string describing the execution mode of ``mc_fun``. + Defaults to `None`. In this case, ``mc_fun`` is executed as is. + verbose: bool A boolean defaults to True. If True, the current progress will be printed. @@ -570,6 +575,28 @@ def _print_progress(is_final, rt, idx_snr, idx_it, header_text=None): assert dtype.is_complex, "dtype must be a complex type." assert isinstance(verbose, bool), "verbose must be bool." + if graph_mode is None: + graph_mode="default" # applies default graph mode + assert isinstance(graph_mode, str), "graph_mode must be str." + + if graph_mode=="default": + pass # nothing to do + elif graph_mode=="graph": + # avoid retracing -> check if mc_fun is already a function + if not isinstance(mc_fun, tf.types.experimental.GenericFunction): + mc_fun = tf.function(mc_fun, + jit_compile=False, + experimental_follow_type_hints=True) + elif graph_mode=="xla": + # avoid retracing -> check if mc_fun is already a function + if not isinstance(mc_fun, tf.types.experimental.GenericFunction) or \ + not mc_fun.function_spec.jit_compile: + mc_fun = tf.function(mc_fun, + jit_compile=True, + experimental_follow_type_hints=True) + else: + raise TypeError("Unknown graph_mode selected.") + ebno_dbs = tf.cast(ebno_dbs, dtype.real_dtype) batch_size = tf.cast(batch_size, tf.int32) num_points = tf.shape(ebno_dbs)[0] diff --git a/sionna/utils/plotting.py b/sionna/utils/plotting.py index 3d94c9ca..846faa3a 100644 --- a/sionna/utils/plotting.py +++ b/sionna/utils/plotting.py @@ -245,17 +245,18 @@ def __call__(self, is_bler = self._is_bler + is_bler # deactivate BER/BLER - if show_ber is False: - snrs = list(compress(snrs, is_bler)) - bers = list(compress(bers, is_bler)) - legends = list(compress(legends, is_bler)) - is_bler = list(compress(is_bler, is_bler)) - - if show_bler is False: - snrs = list(compress(snrs, np.invert(is_bler))) - bers = list(compress(bers, np.invert(is_bler))) - legends = list(compress(legends, np.invert(is_bler))) - is_bler = list(compress(is_bler, np.invert(is_bler))) + if len(is_bler)>0: # ignore if object is empty + if show_ber is False: + snrs = list(compress(snrs, is_bler)) + bers = list(compress(bers, is_bler)) + legends = list(compress(legends, is_bler)) + is_bler = list(compress(is_bler, is_bler)) + + if show_bler is False: + snrs = list(compress(snrs, np.invert(is_bler))) + bers = list(compress(bers, np.invert(is_bler))) + legends = list(compress(legends, np.invert(is_bler))) + is_bler = list(compress(is_bler, np.invert(is_bler))) # set ylabel ylabel = "BER / BLER" @@ -320,6 +321,7 @@ def simulate(self, num_target_bit_errors=None, num_target_block_errors=None, early_stop=True, + graph_mode=None, add_results=True, forward_keyboard_interrupt=True, show_fig=True, @@ -331,7 +333,7 @@ def simulate(self, Input ----- mc_fun: - Function that yields the transmitted bits `b` and the + Callable that yields the transmitted bits `b` and the receiver's estimate `b_hat` for a given ``batch_size`` and ``ebno_db``. If ``soft_estimates`` is True, b_hat interpreted as logit. @@ -373,6 +375,10 @@ def simulate(self, first error-free SNR point (i.e., no error occurred after ``max_mc_iter`` Monte-Carlo iterations). + graph_mode: One of ["graph", "xla"], str + A string describing the execution mode of ``mc_fun``. + Defaults to `None`. In this case, ``mc_fun`` is executed as is. + add_results: bool Defaults to True. If True, the simulation results will be appended to the internal list of results. @@ -410,6 +416,7 @@ def simulate(self, num_target_bit_errors=num_target_bit_errors, num_target_block_errors=num_target_block_errors, early_stop=early_stop, + graph_mode=graph_mode, verbose=verbose, forward_keyboard_interrupt=forward_keyboard_interrupt) diff --git a/test/codes/turbo/ref_k112_u.npy b/test/codes/turbo/ref_k112_u.npy new file mode 100644 index 0000000000000000000000000000000000000000..de2d99ff609242967815d9069aa25f220e86e96e GIT binary patch literal 9088 zcmcJ}v1*)G6h&dF%~MP_4ydqF1(P~mjGGj$f{0{nFj0_<8{?O@O}!oKO_{%h12l(t8v6-KT!?_$B|b{pxOauP3AbwaP2ceYtzb z!__->e%XD>`d#)r&8v6nPkHrD_vxM8-h1`i%UFLm_x{xEZsPQR_}$yf$Zyt{^_uxp z-@Tr^%jrG$cGQ=4?A||T$0)R*>&e7jtBZ|1N1Y482y=~wh#*}G|P-@N+{&%IpQakcZx z-pM=}+c)!Y+Na(f){}SXou|_Gyd&do&nu^Qc&F~Y^B&rkdp zZ_2xUztcNAc9$piZr5{%?&OKPp8Lt4`rhq&?(ng9^=0&Kx!UVb**m!F{olUtdKdNB zUdCN-<$Gsh@1M)_9rPV}BCq7@r>sXl>L>DW@*dmozE`h#`YsQ7Q`RFN^|KienXjK%Z7LhJwxfO` 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HcmV?d00001 diff --git a/test/integration/test_ofdm_mimo_detectors.py b/test/integration/test_ofdm_mimo_detectors.py new file mode 100644 index 00000000..5eda5b90 --- /dev/null +++ b/test/integration/test_ofdm_mimo_detectors.py @@ -0,0 +1,145 @@ +# +# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +"""Integration tests various OFDM MIMO Detectors""" + +import unittest +import numpy as np +import tensorflow as tf +gpus = tf.config.list_physical_devices('GPU') +print('Number of GPUs available :', len(gpus)) +if gpus: + gpu_num = 0 # Number of the GPU to be used + try: + tf.config.set_visible_devices(gpus[gpu_num], 'GPU') + print('Only GPU number', gpu_num, 'used.') + tf.config.experimental.set_memory_growth(gpus[gpu_num], True) + except RuntimeError as e: + print(e) + +try: + import sionna +except ImportError as e: + import sys + sys.path.append("../") + import sionna + +from sionna.mimo import StreamManagement +from sionna.ofdm import ResourceGrid, ResourceGridMapper, RemoveNulledSubcarriers, LinearDetector, EPDetector, KBestDetector, MaximumLikelihoodDetector +from sionna.channel.tr38901 import AntennaArray, CDL +from sionna.channel import OFDMChannel +from sionna.fec.ldpc.encoding import LDPC5GEncoder +from sionna.fec.ldpc.decoding import LDPC5GDecoder +from sionna.mapping import Mapper +from sionna.utils import BinarySource, ebnodb2no, compute_ber + +class OFDMModel(tf.keras.Model): + def __init__(self, + detector, + output): + super().__init__() + self.num_tx_ant = 4 + self.num_rx_ant = 8 + self.num_streams_per_tx = self.num_tx_ant + self.coderate = 0.5 + self.num_bits_per_symbol = 4 + self.carrier_frequency = 2.6e9 + self.sm = StreamManagement(np.array([[1]]), self.num_streams_per_tx) + self.rg = ResourceGrid(num_ofdm_symbols=14, + fft_size=12, + subcarrier_spacing=15e3, + num_tx=1, + num_streams_per_tx=self.num_tx_ant) + self.n = int(self.rg.num_data_symbols * self.num_bits_per_symbol) + self.k = int(self.n * self.coderate) + + self.ut_array = AntennaArray(num_rows=1, + num_cols=int(self.num_tx_ant/2), + polarization="dual", + polarization_type="cross", + antenna_pattern="38.901", + carrier_frequency=self.carrier_frequency) + + self.bs_array = AntennaArray(num_rows=1, + num_cols=int(self.num_rx_ant/2), + polarization="dual", + polarization_type="cross", + antenna_pattern="38.901", + carrier_frequency=self.carrier_frequency) + + self.cdl = CDL(model="A", + delay_spread=100e-9, + carrier_frequency=self.carrier_frequency, + ut_array=self.ut_array, + bs_array=self.bs_array, + direction="uplink", + min_speed=3.0) + + self.channel = OFDMChannel(self.cdl, self.rg, normalize_channel=True, add_awgn=False, return_channel=True) + + self.binary_source = BinarySource() + self.encoder = LDPC5GEncoder(self.k, self.n) + self.decoder = LDPC5GDecoder(self.encoder, hard_out=True) + self.mapper = Mapper("qam", self.num_bits_per_symbol, return_indices=True) + self.rg_mapper = ResourceGridMapper(self.rg) + self.remove_nulled_scs = RemoveNulledSubcarriers(self.rg) + + if output=="symbol": + hard_out = True + else: + hard_out = False + + self._output = output + + if detector in ["mf", "zf", "lmmse"]: + self.detector = LinearDetector(detector, output, "maxlog", self.rg, self.sm, "qam", self.num_bits_per_symbol, hard_out=hard_out) + elif detector=="ep": + self.detector = EPDetector(output, self.rg, self.sm, self.num_bits_per_symbol, hard_out=hard_out) + elif detector=="kbest": + self.detector = KBestDetector(output, self.num_tx_ant, 16, self.rg, self.sm, "qam", self.num_bits_per_symbol, hard_out=hard_out) + elif detector=="ml": + self.detector = MaximumLikelihoodDetector(output, "maxlog", self.rg, self.sm, "qam", self.num_bits_per_symbol, hard_out=hard_out) + + def call(self, batch_size): + no = 1e-4 + b = self.binary_source([batch_size, 1, self.num_streams_per_tx, self.k]) + c = self.encoder(b) + x, x_ind = self.mapper(c) + x_rg = self.rg_mapper(x) + y, h_hat = self.channel(x_rg) + err_var = 0.0 + llr = self.detector([y, h_hat, err_var, no]) + + if self._output=="symbol": + return x_ind, llr + + b_hat = self.decoder(llr) + return b, b_hat + +class TestOFDMMIMODetectors(unittest.TestCase): + + def test_all_detectors_in_all_modes(self): + """Test for all detectors in all execution modes + """ + + tf.random.set_seed(1) + + for detector in ["mf", "lmmse", "zf", "ep", "kbest", "ml"]: + for output in ["bit", "symbol"]: + for mode in ["eager", "graph", "xla"]: + model = OFDMModel(detector, output) + if mode=="eager": + ber = compute_ber(*model(4)) + elif mode=="graph": + ber = compute_ber(*tf.function(model)(4)) + elif mode=="xla": + sionna.config.xla_compat=True + ber = compute_ber(*tf.function(model, jit_compile=True)(4)) + sionna.config.xla_compat=False + if detector=="mf": + self.assertTrue(ber<1) + elif detector=="ep" and mode=="xla": + self.assertTrue(ber<1) + else: + self.assertTrue(ber==0) diff --git a/test/integration/test_ofdm_mimo_estimation_detection.py b/test/integration/test_ofdm_mimo_estimation_detection.py new file mode 100644 index 00000000..c7c3b34c --- /dev/null +++ b/test/integration/test_ofdm_mimo_estimation_detection.py @@ -0,0 +1,217 @@ +# +# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +"""Integration tests various OFDM MIMO Detectors""" + +import unittest +import numpy as np +import tensorflow as tf +gpus = tf.config.list_physical_devices('GPU') +print('Number of GPUs available :', len(gpus)) +if gpus: + gpu_num = 0 # Number of the GPU to be used + try: + tf.config.set_visible_devices(gpus[gpu_num], 'GPU') + print('Only GPU number', gpu_num, 'used.') + tf.config.experimental.set_memory_growth(gpus[gpu_num], True) + except RuntimeError as e: + print(e) + +try: + import sionna +except ImportError as e: + import sys + sys.path.append("../") + import sionna + +from sionna.mimo import StreamManagement +from sionna.ofdm import ResourceGrid, ResourceGridMapper, LinearDetector, EPDetector, KBestDetector, MMSEPICDetector, LMMSEInterpolator, LSChannelEstimator, tdl_freq_cov_mat, tdl_time_cov_mat +from sionna.channel.tr38901 import TDL +from sionna.channel import OFDMChannel, exp_corr_mat +from sionna.fec.ldpc.encoding import LDPC5GEncoder +from sionna.fec.ldpc.decoding import LDPC5GDecoder +from sionna.mapping import Mapper +from sionna.utils import BinarySource, ebnodb2no, compute_ber +from tensorflow.keras import Model + +class MIMOOFDMLink(Model): + + def __init__(self, output, det_method, perf_csi, num_tx, num_bits_per_symbol, det_param=None, coderate=0.5, **kwargs): + super().__init__(kwargs) + + assert det_method in ('lmmse', 'k-best', 'ep', 'mmse-pic'), "Unknown detection method" + + self._output = output + self.num_tx = num_tx + self.num_bits_per_symbol = num_bits_per_symbol + self.coderate = coderate + self.det_method = det_method + self.perf_csi = perf_csi + + self.num_ofdm_symbols = 14 + self.fft_size = 12*4 # 4 PRBs + self.subcarrier_spacing = 30e3 # Hz + self.carrier_frequency = 3.5e9 # Hz + self.speed = 3. # m/s + + # 3GPP UMi channel model is considered + num_rx_ant = 16 + delay_spread = 300e-9 + rx_corr_mat = exp_corr_mat(0.5, num_rx_ant).numpy() + tx_corr_mat = exp_corr_mat(0.0, self.num_tx).numpy() + space_cov_mat = np.kron(rx_corr_mat, tx_corr_mat) + space_cov_mat = tf.constant(space_cov_mat, tf.complex64) + rx_corr_mat = tf.constant(rx_corr_mat, tf.complex64) + self.channel_model = TDL('A', delay_spread=300e-9, carrier_frequency=self.carrier_frequency, + num_rx_ant=num_rx_ant, num_tx_ant=self.num_tx, + spatial_corr_mat=space_cov_mat) + + # Configure the resource grid + rg = ResourceGrid(num_ofdm_symbols=self.num_ofdm_symbols, + fft_size=self.fft_size, + subcarrier_spacing=self.subcarrier_spacing, + num_tx=1, + num_streams_per_tx=self.num_tx, + pilot_pattern="kronecker", + pilot_ofdm_symbol_indices=[2,11]) + self.rg = rg + + # Stream management + sm = StreamManagement([[1]], self.num_tx) + + # Codeword length and number of information bits per codeword + n = int(rg.num_data_symbols*num_bits_per_symbol) + k = int(coderate*n) + self.n = n + self.k = k + + # If output is symbol, then no FEC is used and hard decision are output + hard_out = (output == "symbol") + coded = (output == "bit") + self.hard_out = hard_out + self.coded = coded + + ################################## + # Transmitter + ################################## + + self.binary_source = BinarySource() + self.mapper = Mapper(constellation_type="qam", num_bits_per_symbol=num_bits_per_symbol, return_indices=True) + self.rg_mapper = ResourceGridMapper(rg) + if coded: + self.encoder = LDPC5GEncoder(k, n, num_bits_per_symbol=num_bits_per_symbol) + + ################################## + # Channel + ################################## + + self.channel = OFDMChannel(self.channel_model, rg, return_channel=True) + + ################################### + # Receiver + ################################### + + # Channel estimation + if not self.perf_csi: + freq_cov_mat = tdl_freq_cov_mat('A', self.subcarrier_spacing, self.fft_size, delay_spread) + time_cov_mat = tdl_time_cov_mat('A', self.speed, self.carrier_frequency, rg.ofdm_symbol_duration, self.num_ofdm_symbols) + lmmse_int_time_first = LMMSEInterpolator(rg.pilot_pattern, time_cov_mat, freq_cov_mat, rx_corr_mat, order='t-f-s') + self.channel_estimator = LSChannelEstimator(rg, interpolator=lmmse_int_time_first) + + # Detection + if det_method == "lmmse": + self.detector = LinearDetector("lmmse", output, "app", rg, sm, constellation_type="qam", num_bits_per_symbol=num_bits_per_symbol, hard_out=hard_out) + elif det_method == 'k-best': + if det_param is None: + k = 64 + else: + k = det_param + self.detector = KBestDetector(output, num_tx, k, rg, sm, constellation_type="qam", num_bits_per_symbol=num_bits_per_symbol, hard_out=hard_out) + elif det_method == "ep": + if det_param is None: + l = 10 + else: + l = det_param + self.detector = EPDetector(output, rg, sm, num_bits_per_symbol, l=l, hard_out=hard_out) + elif det_method == 'mmse-pic': + if det_param is None: + l = 4 + else: + l = det_param + self.detector = MMSEPICDetector(output, rg, sm, 'app', num_iter=l, constellation_type="qam", num_bits_per_symbol=num_bits_per_symbol, hard_out=hard_out) + + if coded: + self.decoder = LDPC5GDecoder(self.encoder, hard_out=True) + + def call(self, batch_size, ebno_db): + + + ################################## + # Transmitter + ################################## + + if self.coded: + b = self.binary_source([batch_size, 1, self.num_tx, self.k]) + c = self.encoder(b) + else: + c = self.binary_source([batch_size, 1, self.num_tx, self.n]) + bits_shape = tf.shape(c) + x,x_ind = self.mapper(c) + x_rg = self.rg_mapper(x) + + ################################## + # Channel + ################################## + + no = ebnodb2no(ebno_db, self.num_bits_per_symbol, self.coderate, resource_grid=self.rg) + y_rg, h_freq = self.channel((x_rg, no)) + + ################################### + # Receiver + ################################### + + # Channel estimation + if self.perf_csi: + h_hat = h_freq + err_var = 0.0 + else: + h_hat,err_var = self.channel_estimator((y_rg,no)) + + # Detection + if self.det_method == "mmse-pic": + if self._output == "bit": + prior_shape = bits_shape + elif self._output == "symbol": + prior_shape = tf.concat([tf.shape(x), [self.num_bits_per_symbol]], axis=0) + prior = tf.zeros(prior_shape) + det_out = self.detector((y_rg,h_hat,prior,err_var,no)) + else: + det_out = self.detector((y_rg,h_hat,err_var,no)) + + # (Decoding) and output + if self._output == "bit": + llr = tf.reshape(det_out, bits_shape) + b_hat = self.decoder(llr) + return b, b_hat + elif self._output == "symbol": + x_hat = tf.reshape(det_out, tf.shape(x_ind)) + return x_ind, x_hat + +class TestOFDMMIMODetectors(unittest.TestCase): + + def test_all_detectors_in_all_modes(self): + """Test for all detectors in all execution modes + """ + + tf.random.set_seed(42) + + for detector in ["lmmse", "ep", "k-best", "mmse-pic"]: + for output in ["bit", "symbol"]: + model = MIMOOFDMLink(output, detector, False, 4, 2) + # Eager + er_eager = compute_ber(*model(1, 40.0)) + self.assertTrue(er_eager == 0.0) + # Graph + er_graph = compute_ber(*tf.function(model)(1, 40.0)) + self.assertTrue(er_graph == 0.0) diff --git a/test/unit/channel/channel_test_utils.py b/test/unit/channel/channel_test_utils.py index 0993212c..7ed478ab 100644 --- a/test/unit/channel/channel_test_utils.py +++ b/test/unit/channel/channel_test_utils.py @@ -1642,7 +1642,43 @@ def xpr(model, submodel, batch_size, num_clusters): -25.6, -20.2, -29.8, - -29.2]) + -29.2]), + 'A30' : np.array([-15.5, + 0.0, + -5.1, + -5.1, + -9.6, + -8.2, + -13.1, + -11.5, + -11.0, + -16.2, + -16.6, + -26.2]), + 'B100' : np.array([0.0, + -2.2, + -0.6, + -0.6, + -0.3, + -1.2, + -5.9, + -2.2, + -0.8, + -6.3, + -7.5, + -7.1]), + 'C300' : np.array([-6.9, + 0.0, + -7.7, + -2.5, + -2.4, + -9.9, + -8.0, + -6.6, + -7.1, + -13.0, + -14.2, + -16.0]) } TDL_DELAYS = { @@ -1744,6 +1780,42 @@ def xpr(model, submodel, batch_size, num_clusters): 5.4524, 12.0034, 20.6519]), + 'A30' : np.array([0.0, + 10.0, + 15.0, + 20.0, + 25.0, + 50.0, + 65.0, + 75.0, + 105.0, + 135.0, + 150.0, + 190.0]), + 'B100' : np.array([0.0, + 10.0, + 20.0, + 30.0, + 35.0, + 45.0, + 55.0, + 120.0, + 170.0, + 245.0, + 330.0, + 480.0]), + 'C300' : np.array([0.0, + 65.0, + 70.0, + 190.0, + 195.0, + 200.0, + 240.0, + 325.0, + 520.0, + 1045.0, + 1510.0, + 2595.0]) } TDL_RICIAN_K = {'A' : None, diff --git a/test/unit/channel/test_3gpp_channel_tdl.py b/test/unit/channel/test_3gpp_channel_tdl.py index a7fe36f4..05e0a919 100644 --- a/test/unit/channel/test_3gpp_channel_tdl.py +++ b/test/unit/channel/test_3gpp_channel_tdl.py @@ -23,6 +23,7 @@ import unittest import numpy as np from sionna.channel.tr38901 import TDL +from sionna.channel import exp_corr_mat from channel_test_utils import * from scipy.stats import kstest, rayleigh, rice from scipy.special import jv @@ -132,8 +133,47 @@ def setUpClass(): TestTDL.channel_coeff['E'] = h.numpy()[:,0,0,0,0,:,:] TestTDL.delays['E'] = tau.numpy()[:,0,0,:] + ########## TDL-A30 + tdl = TDL( "A30", + delay_spread=30e-9, + carrier_frequency=TestTDL.CARRIER_FREQUENCY, + num_sinusoids=TestTDL.NUM_SINUSOIDS, + los_angle_of_arrival=TestTDL.LoS_AoA, + min_speed=TestTDL.SPEED) + h,tau = tdl(batch_size=TestTDL.BATCH_SIZE, + num_time_steps=TestTDL.NUM_TIME_STEPS, + sampling_frequency=TestTDL.SAMPLING_FREQUENCY) + TestTDL.channel_coeff['A30'] = h.numpy()[:,0,0,0,0,:,:] + TestTDL.delays['A30'] = tau.numpy()[:,0,0,:] - @channel_test_on_models(('A', 'B', 'C', 'D', 'E'), ('foo',)) + ########## TDL-B100 + tdl = TDL( "B100", + delay_spread=100e-9, + carrier_frequency=TestTDL.CARRIER_FREQUENCY, + num_sinusoids=TestTDL.NUM_SINUSOIDS, + los_angle_of_arrival=TestTDL.LoS_AoA, + min_speed=TestTDL.SPEED) + h,tau = tdl(batch_size=TestTDL.BATCH_SIZE, + num_time_steps=TestTDL.NUM_TIME_STEPS, + sampling_frequency=TestTDL.SAMPLING_FREQUENCY) + TestTDL.channel_coeff['B100'] = h.numpy()[:,0,0,0,0,:,:] + TestTDL.delays['B100'] = tau.numpy()[:,0,0,:] + + ########## TDL-C300 + tdl = TDL( "C300", + delay_spread=300e-9, + carrier_frequency=TestTDL.CARRIER_FREQUENCY, + num_sinusoids=TestTDL.NUM_SINUSOIDS, + los_angle_of_arrival=TestTDL.LoS_AoA, + min_speed=TestTDL.SPEED) + h,tau = tdl(batch_size=TestTDL.BATCH_SIZE, + num_time_steps=TestTDL.NUM_TIME_STEPS, + sampling_frequency=TestTDL.SAMPLING_FREQUENCY) + TestTDL.channel_coeff['C300'] = h.numpy()[:,0,0,0,0,:,:] + TestTDL.delays['C300'] = tau.numpy()[:,0,0,:] + + + @channel_test_on_models(('A', 'B', 'C', 'D', 'E', 'A30', 'B100', 'C300'), ('foo',)) def test_pdp(self, model, submodel): # Submodel does not apply to TDL """Test power delay profiles""" # Checking powers @@ -144,13 +184,17 @@ def test_pdp(self, model, submodel): # Submodel does not apply to TDL max_err = np.max(np.abs(ref_p - p)) self.assertLessEqual(max_err, TestTDL.MAX_ERR, f'{model}') # Checking delays - tau = TestTDL.delays[model]/TestTDL.DELAY_SPREAD - ref_tau = np.expand_dims(TDL_DELAYS[model], axis=0) + if model in ('A30', 'B100', 'C300'): + tau = TestTDL.delays[model] + ref_tau = np.expand_dims(TDL_DELAYS[model], axis=0)*1e-9 # ns to s + else: + tau = TestTDL.delays[model]/TestTDL.DELAY_SPREAD + ref_tau = np.expand_dims(TDL_DELAYS[model], axis=0) max_err = np.max(np.abs(ref_tau - tau)) self.assertLessEqual(max_err, TestTDL.MAX_ERR, f'{model}') # Submodel does not apply to TDL - @channel_test_on_models(('A', 'B', 'C', 'D', 'E'), ('foo',)) + @channel_test_on_models(('A', 'B', 'C', 'D', 'E', 'A30', 'B100', 'C300'), ('foo',)) def test_taps_powers_distributions(self, model, submodel): """Test the distribution of the taps powers""" ref_powers = np.power(10.0, TDL_POWERS[model]/10.0) @@ -195,7 +239,7 @@ def auto_complex_rice(self, max_doppler, K, theta_0, t): return (a + b+ c)/(1+K) # Submodel does not apply to TDL - @channel_test_on_models(('A', 'B', 'C', 'D', 'E'), ('foo',)) + @channel_test_on_models(('A', 'B', 'C', 'D', 'E', 'A30', 'B100', 'C300'), ('foo',)) def test_autocorrelation(self, model, submodel): """Test the autocorrelation""" max_lag = TestTDL.NUM_TIME_STEPS//2 @@ -237,3 +281,290 @@ def test_autocorrelation(self, model, submodel): # TestTDL.NUM_SINUSOIDS, time, p) # max_err = np.max(np.abs(r_abs2 - ref_r_abs2)) # self.assertLessEqual(max_err, TestTDL.MAX_ERR, f'{model}') + + # No need to test on evey channel model for spatial correlation + def test_spatial_correlation_separate_rx_tx(self): + """Test spatial Correlation with separate RX and TX correlation""" + # Forcing the seed to make the tests deterministic + tf.random.set_seed(42) + np.random.seed(42) + + # Instantiate the model + num_rx_ant = 16 + num_tx_ant = 16 + rx_corr_mat = exp_corr_mat(0.9, num_rx_ant) + tx_corr_mat = exp_corr_mat(0.5, num_tx_ant) + tdl = TDL(model = "A", + delay_spread = 100e-9, + carrier_frequency = 3.5e9, + min_speed = 0.0, max_speed = 0.0, + num_rx_ant=num_rx_ant,num_tx_ant=num_tx_ant, + rx_corr_mat=rx_corr_mat, tx_corr_mat=tx_corr_mat) + + # Empirical estimation of the correlation matrices + est_rx_cov = np.zeros([num_rx_ant,num_rx_ant], complex) + est_tx_cov = np.zeros([num_tx_ant,num_tx_ant], complex) + num_it = 1000 + batch_size = 1000 + for _ in range(num_it): + h, _ = tdl(batch_size, 1, 1) + + h = np.transpose(h, [0,1,3,5,6,2,4]) # [..., rx ant, tx ant] + h = h[:,0,0,0,0,:,:]/np.sqrt(tdl.mean_powers[0].numpy()) # [batch size, rx ant, tx ant] + + # RX correlation + h_ = np.expand_dims(h[:,:,0], axis=-1) # [batch size, rx ant, 1] + est_rx_cov_ = np.matmul(h_, np.conj(np.transpose(h_, [0,2,1]))) + est_rx_cov_ = np.mean(est_rx_cov_, axis=0) # [rx ant, rx ant] + est_rx_cov += est_rx_cov_ + + # TX correlation + h_ = np.expand_dims(h[:,0,:], axis=-1) # [batch size, rx ant, 1] + est_tx_cov_ = np.matmul(h_, np.conj(np.transpose(h_, [0,2,1]))) + est_tx_cov_ = np.mean(est_tx_cov_, axis=0) # [rx ant, rx ant] + est_tx_cov += est_tx_cov_ + est_rx_cov /= num_it + est_tx_cov /= num_it + + # Test + max_err = np.max(np.abs(est_rx_cov - rx_corr_mat)) + self.assertLessEqual(max_err, TestTDL.MAX_ERR, f'Receiver correlation') + max_err = np.max(np.abs(est_tx_cov - tx_corr_mat)) + self.assertLessEqual(max_err, TestTDL.MAX_ERR, f'Transmitter correlation') + + # No need to test on evey channel model for spatial correlation + def test_spatial_correlation_joint_rx_tx(self): + """Test spatial Correlation with joint filtering""" + # Forcing the seed to make the tests deterministic + tf.random.set_seed(42) + np.random.seed(42) + + # Instantiate the model + num_rx_ant = 16 + num_tx_ant = 16 + rx_corr_mat = exp_corr_mat(0.9, num_rx_ant//2).numpy() + pol_corr_mat = np.array([[1.0, 0.8, 0.0, 0.0], + [0.8, 1.0, 0.0, 0.0], + [0.0, 0.0, 1.0, 0.8], + [0.0, 0.0, 0.8, 1.0]]) + tx_corr_mat = exp_corr_mat(0.5, num_tx_ant//2).numpy() + spatial_corr_mat = np.kron(pol_corr_mat, tx_corr_mat) + spatial_corr_mat = np.kron(rx_corr_mat, spatial_corr_mat) + tdl = TDL(model = "A", + delay_spread = 100e-9, + carrier_frequency = 3.5e9, + min_speed = 0.0, max_speed = 0.0, + num_rx_ant=num_rx_ant,num_tx_ant=num_tx_ant, + spatial_corr_mat=spatial_corr_mat) + + # Empirical estimation of the correlation matrices + est_spatial_cov = np.zeros([num_tx_ant*num_rx_ant, + num_tx_ant*num_rx_ant], complex) + num_it = 1000 + batch_size = 1000 + for _ in range(num_it): + h, _ = tdl(batch_size, 1, 1) + + h = np.transpose(h, [0,1,3,5,6,2,4]) # [..., rx ant, tx ant] + h = h[:,0,0,0,0,:,:]/np.sqrt(tdl.mean_powers[0].numpy()) # [batch size, rx ant, tx ant] + h = np.reshape(h, [batch_size, -1]) # [batch size, rx ant*tx ant] + + # Spatial correlation + h_ = np.expand_dims(h, axis=-1) # [batch size, rx ant*tx ant, 1] + est_spatial_cov_ = np.matmul(h_, np.conj(np.transpose(h_, [0,2,1]))) + est_spatial_cov_ = np.mean(est_spatial_cov_, axis=0) # [rx ant, rx ant] + est_spatial_cov += est_spatial_cov_ + est_spatial_cov /= num_it + + # Test + max_err = np.max(np.abs(est_spatial_cov - spatial_corr_mat)) + self.assertLessEqual(max_err, TestTDL.MAX_ERR) + + # No need to test on evey channel model for spatial correlation + def test_no_spatial_correlation(self): + """No spatial correlation specified leads to no spatial correlation observed""" + # Forcing the seed to make the tests deterministic + tf.random.set_seed(42) + np.random.seed(42) + + # Instantiate the model + num_rx_ant = 16 + num_tx_ant = 16 + tdl = TDL(model = "A", + delay_spread = 100e-9, + carrier_frequency = 3.5e9, + min_speed = 0.0, max_speed = 0.0, + num_rx_ant=num_rx_ant,num_tx_ant=num_tx_ant) + + # Empirical estimation of the correlation matrices + est_spatial_cov = np.zeros([num_tx_ant*num_rx_ant, + num_tx_ant*num_rx_ant], complex) + num_it = 1000 + batch_size = 1000 + for _ in range(num_it): + h, _ = tdl(batch_size, 1, 1) + + h = np.transpose(h, [0,1,3,5,6,2,4]) # [..., rx ant, tx ant] + h = h[:,0,0,0,0,:,:]/np.sqrt(tdl.mean_powers[0].numpy()) # [batch size, rx ant, tx ant] + h = np.reshape(h, [batch_size, -1]) # [batch size, rx ant*tx ant] + + # Spatial correlation + h_ = np.expand_dims(h, axis=-1) # [batch size, rx ant*tx ant, 1] + est_spatial_cov_ = np.matmul(h_, np.conj(np.transpose(h_, [0,2,1]))) + est_spatial_cov_ = np.mean(est_spatial_cov_, axis=0) # [rx ant, rx ant] + est_spatial_cov += est_spatial_cov_ + est_spatial_cov /= num_it + + # Test + spatial_corr_mat = np.eye(num_rx_ant*num_rx_ant) + max_err = np.max(np.abs(est_spatial_cov - spatial_corr_mat)) + self.assertLessEqual(max_err, TestTDL.MAX_ERR) + + # No need to test on evey channel model for spatial correlation + def test_rx_corr_only(self): + """Test with RX spatial correlation only""" + # Forcing the seed to make the tests deterministic + tf.random.set_seed(42) + np.random.seed(42) + + # Instantiate the model + num_rx_ant = 16 + num_tx_ant = 16 + rx_corr_mat = exp_corr_mat(0.9, num_rx_ant) + tx_corr_mat = np.eye(num_tx_ant) + tdl = TDL(model = "A", + delay_spread = 100e-9, + carrier_frequency = 3.5e9, + min_speed = 0.0, max_speed = 0.0, + num_rx_ant=num_rx_ant,num_tx_ant=num_tx_ant, + rx_corr_mat=rx_corr_mat) + + # Empirical estimation of the correlation matrices + est_rx_cov = np.zeros([num_rx_ant,num_rx_ant], complex) + est_tx_cov = np.zeros([num_tx_ant,num_tx_ant], complex) + num_it = 1000 + batch_size = 1000 + for _ in range(num_it): + h, _ = tdl(batch_size, 1, 1) + + h = np.transpose(h, [0,1,3,5,6,2,4]) # [..., rx ant, tx ant] + h = h[:,0,0,0,0,:,:]/np.sqrt(tdl.mean_powers[0].numpy()) # [batch size, rx ant, tx ant] + + # RX correlation + h_ = np.expand_dims(h[:,:,0], axis=-1) # [batch size, rx ant, 1] + est_rx_cov_ = np.matmul(h_, np.conj(np.transpose(h_, [0,2,1]))) + est_rx_cov_ = np.mean(est_rx_cov_, axis=0) # [rx ant, rx ant] + est_rx_cov += est_rx_cov_ + + # TX correlation + h_ = np.expand_dims(h[:,0,:], axis=-1) # [batch size, rx ant, 1] + est_tx_cov_ = np.matmul(h_, np.conj(np.transpose(h_, [0,2,1]))) + est_tx_cov_ = np.mean(est_tx_cov_, axis=0) # [rx ant, rx ant] + est_tx_cov += est_tx_cov_ + est_rx_cov /= num_it + est_tx_cov /= num_it + + # Test + max_err = np.max(np.abs(est_rx_cov - rx_corr_mat)) + self.assertLessEqual(max_err, TestTDL.MAX_ERR, f'Receiver correlation') + max_err = np.max(np.abs(est_tx_cov - tx_corr_mat)) + self.assertLessEqual(max_err, TestTDL.MAX_ERR, f'Transmitter correlation') + + # No need to test on evey channel model for spatial correlation + def test_tx_corr_only(self): + """Test with TX spatial Correlation only""" + # Forcing the seed to make the tests deterministic + tf.random.set_seed(42) + np.random.seed(42) + + # Instantiate the model + num_rx_ant = 16 + num_tx_ant = 16 + rx_corr_mat = np.eye(num_tx_ant) + tx_corr_mat = exp_corr_mat(0.9, num_rx_ant) + tdl = TDL(model = "A", + delay_spread = 100e-9, + carrier_frequency = 3.5e9, + min_speed = 0.0, max_speed = 0.0, + num_rx_ant=num_rx_ant,num_tx_ant=num_tx_ant, + tx_corr_mat=tx_corr_mat) + + # Empirical estimation of the correlation matrices + est_rx_cov = np.zeros([num_rx_ant,num_rx_ant], complex) + est_tx_cov = np.zeros([num_tx_ant,num_tx_ant], complex) + num_it = 1000 + batch_size = 1000 + for _ in range(num_it): + h, _ = tdl(batch_size, 1, 1) + + h = np.transpose(h, [0,1,3,5,6,2,4]) # [..., rx ant, tx ant] + h = h[:,0,0,0,0,:,:]/np.sqrt(tdl.mean_powers[0].numpy()) # [batch size, rx ant, tx ant] + + # RX correlation + h_ = np.expand_dims(h[:,:,0], axis=-1) # [batch size, rx ant, 1] + est_rx_cov_ = np.matmul(h_, np.conj(np.transpose(h_, [0,2,1]))) + est_rx_cov_ = np.mean(est_rx_cov_, axis=0) # [rx ant, rx ant] + est_rx_cov += est_rx_cov_ + + # TX correlation + h_ = np.expand_dims(h[:,0,:], axis=-1) # [batch size, rx ant, 1] + est_tx_cov_ = np.matmul(h_, np.conj(np.transpose(h_, [0,2,1]))) + est_tx_cov_ = np.mean(est_tx_cov_, axis=0) # [rx ant, rx ant] + est_tx_cov += est_tx_cov_ + est_rx_cov /= num_it + est_tx_cov /= num_it + + # Test + max_err = np.max(np.abs(est_rx_cov - rx_corr_mat)) + self.assertLessEqual(max_err, TestTDL.MAX_ERR, f'Receiver correlation') + max_err = np.max(np.abs(est_tx_cov - tx_corr_mat)) + self.assertLessEqual(max_err, TestTDL.MAX_ERR, f'Transmitter correlation') + + # No need to test on evey channel model for spatial correlation + def test_spatial_correlation_all_three_inputs(self): + """Test spatial correlation with all three inputs""" + # Forcing the seed to make the tests deterministic + tf.random.set_seed(42) + np.random.seed(42) + + # Instantiate the model + num_rx_ant = 16 + num_tx_ant = 16 + rx_corr_mat = exp_corr_mat(0.9, num_rx_ant//2).numpy() + pol_corr_mat = np.array([[1.0, 0.8, 0.0, 0.0], + [0.8, 1.0, 0.0, 0.0], + [0.0, 0.0, 1.0, 0.8], + [0.0, 0.0, 0.8, 1.0]]) + tx_corr_mat = exp_corr_mat(0.5, num_tx_ant//2).numpy() + spatial_corr_mat = np.kron(pol_corr_mat, tx_corr_mat) + spatial_corr_mat = np.kron(rx_corr_mat, spatial_corr_mat) + tdl = TDL(model = "A", + delay_spread = 100e-9, + carrier_frequency = 3.5e9, + min_speed = 0.0, max_speed = 0.0, + num_rx_ant=num_rx_ant,num_tx_ant=num_tx_ant, + spatial_corr_mat=spatial_corr_mat, + rx_corr_mat=np.eye(num_rx_ant), tx_corr_mat=np.eye(num_tx_ant)) + + # Empirical estimation of the correlation matrices + est_spatial_cov = np.zeros([num_tx_ant*num_rx_ant, + num_tx_ant*num_rx_ant], complex) + num_it = 1000 + batch_size = 1000 + for _ in range(num_it): + h, _ = tdl(batch_size, 1, 1) + + h = np.transpose(h, [0,1,3,5,6,2,4]) # [..., rx ant, tx ant] + h = h[:,0,0,0,0,:,:]/np.sqrt(tdl.mean_powers[0].numpy()) # [batch size, rx ant, tx ant] + h = np.reshape(h, [batch_size, -1]) # [batch size, rx ant*tx ant] + + # Spatial correlation + h_ = np.expand_dims(h, axis=-1) # [batch size, rx ant*tx ant, 1] + est_spatial_cov_ = np.matmul(h_, np.conj(np.transpose(h_, [0,2,1]))) + est_spatial_cov_ = np.mean(est_spatial_cov_, axis=0) # [rx ant, rx ant] + est_spatial_cov += est_spatial_cov_ + est_spatial_cov /= num_it + + # Test + max_err = np.max(np.abs(est_spatial_cov - spatial_corr_mat)) + self.assertLessEqual(max_err, TestTDL.MAX_ERR) diff --git a/test/unit/fec/Validate_OSD.ipynb b/test/unit/fec/Validate_OSD.ipynb new file mode 100755 index 00000000..8a2ac25b --- /dev/null +++ b/test/unit/fec/Validate_OSD.ipynb @@ -0,0 +1,808 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Verify performance of OSD and compare against other ML solutions\n", + "\n", + "This notebook includes benchmarks of OSD against other known ML decoders." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tensorflow version: 2.10.0\n", + "Only GPU number 0 used\n" + ] + } + ], + "source": [ + "num_GPU = 0\n", + "import tensorflow as tf\n", + "print('Tensorflow version: ', tf.__version__)\n", + "\n", + "gpus = tf.config.experimental.list_physical_devices(\"GPU\")\n", + "tf.config.experimental.set_visible_devices(gpus[num_GPU], 'GPU')\n", + "tf.config.experimental.set_memory_growth(gpus[num_GPU], True)\n", + "print('Only GPU number', num_GPU, 'used')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import scipy as sp\n", + "import sys\n", + "sys.path.append('../../..')\n", + "import sionna\n", + "%reload_ext autoreload\n", + "%autoreload 2\n", + "import importlib\n", + "importlib.reload(sionna)\n", + "\n", + "# Load Sionna components\n", + "from sionna.mapping import Mapper, Demapper, Constellation\n", + "from sionna.utils import BinarySource, ebnodb2no, hard_decisions, PlotBER\n", + "from sionna.channel import AWGN\n", + "from sionna.fec.utils import load_parity_check_examples\n", + "\n", + "from sionna.fec.linear import LinearEncoder, OSDecoder\n", + "from sionna.fec.polar import PolarEncoder, PolarSCLDecoder, generate_5g_ranking\n", + "from sionna.fec.conv import ConvEncoder, ViterbiDecoder\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define System Model" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "class System_Model(tf.keras.Model):\n", + " \"\"\"System model for channel coding BER simulations.\n", + " \n", + " This model allows to simulate BERs over an AWGN channel with\n", + " QAM modulation. Arbitrary FEC encoder/decoder layers can be used to \n", + " initialize the model.\n", + " \n", + " Parameters\n", + " ---------- \n", + " encoder: Keras layer\n", + " A Keras layer that encodes information bit tensors.\n", + " \n", + " decoder: Keras layer\n", + " A Keras layer that decodes llr tensors.\n", + "\n", + " cw_estimate: bool\n", + " Defaults to True. If True the decoder outputs codeword estimates instead of information estimates.\n", + " \n", + " Input\n", + " -----\n", + " batch_size: int or tf.int\n", + " The batch_size used for the simulation.\n", + " \n", + " ebno_db: float or tf.float\n", + " A float defining the simulation SNR.\n", + " \n", + " Output\n", + " ------\n", + " (u, u_hat):\n", + " Tuple:\n", + " \n", + " u: tf.float32\n", + " A tensor of shape `[batch_size, k] of 0s and 1s containing the transmitted information bits. \n", + "\n", + " u_hat: tf.float32\n", + " A tensor of shape `[batch_size, k] of 0s and 1s containing the estimated information bits. \n", + " \"\"\"\n", + " def __init__(self, \n", + " encoder,\n", + " decoder,\n", + " cw_estimate=True):\n", + "\n", + " super().__init__()\n", + " \n", + " # store values internally\n", + " self.k = encoder.k\n", + " self.n = encoder.n\n", + "\n", + " self._cw_estimate = cw_estimate\n", + " \n", + " # number of bit per QAM symbol\n", + " # use pam as no additional filler bits are required for odd length\n", + " self.num_bits_per_symbol = 1 \n", + "\n", + " # initialize mapper and demapper \n", + " self.mapper = Mapper(\"pam\", 1)\n", + " self.demapper = Demapper(\"app\", \"pam\", 1)\n", + " \n", + " # init components\n", + " self.source = BinarySource()\n", + "\n", + " # the channel can be replaced by more sophisticated models\n", + " self.channel = AWGN()\n", + "\n", + " # FEC encoder / decoder\n", + " self.encoder = encoder\n", + " self.decoder = decoder\n", + "\n", + " @tf.function(jit_compile=True) # enable graph mode for increased throughputs\n", + " def call(self, batch_size, ebno_db):\n", + "\n", + " no = ebnodb2no(ebno_db,\n", + " num_bits_per_symbol=self.num_bits_per_symbol,\n", + " coderate=self.k/self.n) \n", + "\n", + " u = self.source([batch_size, self.k]) # generate random data\n", + " #u = tf.zeros_like(u)\n", + " c = self.encoder(u) # explicitly encode\n", + "\n", + " x = self.mapper(c) # map c to symbols x\n", + " y = self.channel([x, no]) # transmit over AWGN channel\n", + " llr_ch = self.demapper([y, no]) # demap y to LLRs\n", + "\n", + " # and run the decoder\n", + " c_hat = self.decoder(llr_ch)\n", + " \n", + " #c_hat = hard_decisions(llr_ch)\n", + "\n", + " if self._cw_estimate:\n", + " return c, c_hat\n", + " else:\n", + " return u, c_hat" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Evaluate (7,4) Hamming" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", + "---------------------------------------------------------------------------------------------------------------------------------------\n", + " 0.0 | 8.2167e-02 | 1.7858e-01 | 6902 | 84000 | 2143 | 12000 | 2.2 |reached target block errors\n", + " 1.0 | 5.1968e-02 | 1.1333e-01 | 6548 | 126000 | 2040 | 18000 | 0.1 |reached target block errors\n", + " 2.0 | 2.8839e-02 | 6.3531e-02 | 6460 | 224000 | 2033 | 32000 | 0.2 |reached target block errors\n", + " 3.0 | 1.3571e-02 | 3.0224e-02 | 6365 | 469000 | 2025 | 67000 | 0.4 |reached target block errors\n", + " 4.0 | 5.0514e-03 | 1.1340e-02 | 3536 | 700000 | 1134 | 100000 | 0.6 |reached max iter \n", + " 5.0 | 1.4714e-03 | 3.3300e-03 | 1030 | 700000 | 333 | 100000 | 0.6 |reached max iter \n", + " 6.0 | 4.1571e-04 | 9.5000e-04 | 291 | 700000 | 95 | 100000 | 0.6 |reached max iter \n", + " 7.0 | 5.5714e-05 | 1.3000e-04 | 39 | 700000 | 13 | 100000 | 0.6 |reached max iter \n", + " 8.0 | 4.2857e-06 | 1.0000e-05 | 3 | 700000 | 1 | 100000 | 0.6 |reached max iter \n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ebno_db = np.linspace(0, 8, 9) # sim SNR range \n", + "id = 0 # 7,4 Hamming\n", + "pcm, k, n, coderate = load_parity_check_examples(id, verbose=False)\n", + "\n", + "# init components\n", + "enc = LinearEncoder(pcm, is_pcm=True)\n", + "#dec = OSDecoder(pcm, is_pcm=True, t=2)\n", + "dec = OSDecoder(encoder=enc, t=2)\n", + "model = System_Model(enc, dec, cw_estimate=True)\n", + "\n", + "# and run simulation\n", + "ber_plot = PlotBER(f\"7,4 Hamming\")\n", + "\n", + "# add TU KL reference curves\n", + "# https://www.uni-kl.de/fileadmin/chaco/public/results_bch/BCH_N7_K4_ML.txt\n", + "snrs_ref = np.linspace(0, 8, 9)\n", + "blers_ref = np.array([1.832e-01, 1.253e-01, 7.047e-02, 2.899e-02, 1.252e-02, 4.371e-03, 7.962e-04, 1.205e-04, 1.211e-05])\n", + "ber_plot.add(snrs_ref, blers_ref, is_bler=True, legend=\"ML (Kaiserslautern)\")\n", + "\n", + "ber_plot.simulate(model, \n", + " ebno_dbs=ebno_db, \n", + " legend=\"OSD\",\n", + " max_mc_iter=100, \n", + " num_target_block_errors=2000, \n", + " batch_size=1000, \n", + " soft_estimates=False, \n", + " early_stop=True,\n", + " show_fig=False, \n", + " add_bler=True,\n", + " forward_keyboard_interrupt=True); \n", + "\n", + "ber_plot(show_ber=False) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Evaluate (63,45) BCH" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "n: 63, k: 45, coderate: 0.714\n", + "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", + "---------------------------------------------------------------------------------------------------------------------------------------\n", + " 0.0 | 1.1772e-01 | 7.2100e-01 | 22250 | 189000 | 2163 | 3000 | 1.4 |reached target block errors\n", + " 0.5 | 9.2948e-02 | 5.8825e-01 | 23423 | 252000 | 2353 | 4000 | 0.0 |reached target block errors\n", + " 1.0 | 6.3152e-02 | 4.1600e-01 | 19893 | 315000 | 2080 | 5000 | 0.1 |reached target block errors\n", + " 1.5 | 3.9486e-02 | 2.6625e-01 | 19901 | 504000 | 2130 | 8000 | 0.1 |reached target block errors\n", + " 2.0 | 2.1156e-02 | 1.4643e-01 | 18660 | 882000 | 2050 | 14000 | 0.2 |reached target block errors\n", + " 2.5 | 9.4127e-03 | 6.7033e-02 | 17790 | 1890000 | 2011 | 30000 | 0.3 |reached target block errors\n", + " 3.0 | 3.5576e-03 | 2.6091e-02 | 17258 | 4851000 | 2009 | 77000 | 0.8 |reached target block errors\n", + " 3.5 | 1.1316e-03 | 8.5400e-03 | 7129 | 6300000 | 854 | 100000 | 1.0 |reached max iter \n", + " 4.0 | 2.5571e-04 | 1.9700e-03 | 1611 | 6300000 | 197 | 100000 | 1.0 |reached max iter \n", + " 4.5 | 5.0476e-05 | 4.0000e-04 | 318 | 6300000 | 40 | 100000 | 1.0 |reached max iter \n", + " 5.0 | 6.1905e-06 | 5.0000e-05 | 39 | 6300000 | 5 | 100000 | 1.0 |reached max iter \n", + " 5.5 | 0.0000e+00 | 0.0000e+00 | 0 | 6300000 | 0 | 100000 | 1.0 |reached max iter \n", + "\n", + "Simulation stopped as no error occurred @ EbNo = 5.5 dB.\n", + "\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ebno_db = np.linspace(0, 5.5, 12) # sim SNR range \n", + "id = 1 # 63,45 BCH\n", + "pcm, k, n, coderate = load_parity_check_examples(id, verbose=True)\n", + "\n", + "# init components\n", + "enc = LinearEncoder(pcm, is_pcm=True)\n", + "dec = OSDecoder(encoder=enc, t=2, dtype=tf.float32)\n", + "model = System_Model(enc, dec, cw_estimate=True)\n", + "\n", + "# and run simulation\n", + "ber_plot = PlotBER(f\"(63,45) BCH\")\n", + "\n", + "# add TU KL reference curves\n", + "# https://www.uni-kl.de/fileadmin/chaco/public/results_bch/BCH_N63_K45_ML.txt\n", + "snrs_ref = np.linspace(0,5.5,12)\n", + "blers_ref = np.array([6.329e-01,4.975e-01,3.704e-01, 2.445e-01, 1.447e-01, 7.353e-02,2.595e-02, 7.918e-03, 2.134e-03,4.751e-04,5.337e-05,6.300e-06])\n", + "ber_plot.add(snrs_ref, blers_ref, is_bler=True, legend=\"ML (Kaiserslautern)\")\n", + "\n", + "ber_plot.simulate(model, \n", + " ebno_dbs=ebno_db, \n", + " legend=\"OSD\",\n", + " max_mc_iter=100, \n", + " num_target_block_errors=2000, \n", + " batch_size=1000, \n", + " soft_estimates=False, \n", + " early_stop=True,\n", + " show_fig=False, \n", + " add_bler=True,\n", + " forward_keyboard_interrupt=True); \n", + " \n", + "ber_plot(show_ber=False) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Evaluate Polar & SCL" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: Required memory complexity is large for the given code parameters and t=4. Please consider small batch-sizes to keep the inference complexity small and activate XLA mode if possible.\n", + "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", + "---------------------------------------------------------------------------------------------------------------------------------------\n", + " 0.0 | 2.8106e-01 | 7.7500e-01 | 67454 | 240000 | 2325 | 3000 | 50.6 |reached target block errors\n", + " 1.0 | 1.2294e-01 | 4.0480e-01 | 49174 | 400000 | 2024 | 5000 | 1.1 |reached target block errors\n", + " 2.0 | 2.5726e-02 | 1.0984e-01 | 39104 | 1520000 | 2087 | 19000 | 4.1 |reached target block errors\n", + " 3.0 | 2.8274e-03 | 1.4890e-02 | 22619 | 8000000 | 1489 | 100000 | 21.6 |reached max iter \n", + " 4.0 | 1.8000e-04 | 1.1300e-03 | 1440 | 8000000 | 113 | 100000 | 21.8 |reached max iter \n", + " 5.0 | 4.0000e-06 | 3.0000e-05 | 32 | 8000000 | 3 | 100000 | 21.8 |reached max iter \n", + " 6.0 | 0.0000e+00 | 0.0000e+00 | 0 | 8000000 | 0 | 100000 | 21.9 |reached max iter \n", + "\n", + "Simulation stopped as no error occurred @ EbNo = 6.0 dB.\n", + "\n", + "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", + "---------------------------------------------------------------------------------------------------------------------------------------\n", + " 0.0 | 1.8821e-01 | 9.5800e-01 | 72272 | 384000 | 2874 | 3000 | 2.1 |reached target block errors\n", + " 1.0 | 1.4698e-01 | 8.2533e-01 | 56440 | 384000 | 2476 | 3000 | 0.1 |reached target block errors\n", + " 2.0 | 9.3434e-02 | 5.7450e-01 | 47838 | 512000 | 2298 | 4000 | 0.1 |reached target block errors\n", + " 3.0 | 3.9170e-02 | 2.6262e-01 | 40110 | 1024000 | 2101 | 8000 | 0.2 |reached target block errors\n", + " 4.0 | 1.2711e-02 | 8.8652e-02 | 37420 | 2944000 | 2039 | 23000 | 0.6 |reached target block errors\n", + " 5.0 | 2.7294e-03 | 1.9620e-02 | 34936 | 12800000 | 1962 | 100000 | 2.5 |reached max iter \n", + " 6.0 | 3.9547e-04 | 2.8200e-03 | 5062 | 12800000 | 282 | 100000 | 2.5 |reached max iter \n", + " 7.0 | 3.0156e-05 | 2.5000e-04 | 386 | 12800000 | 25 | 100000 | 2.6 |reached max iter \n", + "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", + "---------------------------------------------------------------------------------------------------------------------------------------\n", + " 0.0 | 1.4220e-01 | 8.6167e-01 | 54606 | 384000 | 2585 | 3000 | 2.2 |reached target block errors\n", + " 1.0 | 7.9277e-02 | 5.7875e-01 | 40590 | 512000 | 2315 | 4000 | 0.1 |reached target block errors\n", + " 2.0 | 2.7135e-02 | 2.3811e-01 | 31260 | 1152000 | 2143 | 9000 | 0.2 |reached target block errors\n", + " 3.0 | 5.0621e-03 | 5.2231e-02 | 25270 | 4992000 | 2037 | 39000 | 1.0 |reached target block errors\n", + " 4.0 | 4.5641e-04 | 5.1100e-03 | 5842 | 12800000 | 511 | 100000 | 2.7 |reached max iter \n", + " 5.0 | 2.1250e-05 | 2.6000e-04 | 272 | 12800000 | 26 | 100000 | 2.7 |reached max iter \n", + " 6.0 | 0.0000e+00 | 0.0000e+00 | 0 | 12800000 | 0 | 100000 | 2.6 |reached max iter \n", + "\n", + "Simulation stopped as no error occurred @ EbNo = 6.0 dB.\n", + "\n", + "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", + "---------------------------------------------------------------------------------------------------------------------------------------\n", + " 0.0 | 1.2481e-01 | 7.9400e-01 | 47928 | 384000 | 2382 | 3000 | 2.3 |reached target block errors\n", + " 1.0 | 5.5684e-02 | 4.4760e-01 | 35638 | 640000 | 2238 | 5000 | 0.2 |reached target block errors\n", + " 2.0 | 1.2599e-02 | 1.2987e-01 | 25802 | 2048000 | 2078 | 16000 | 0.5 |reached target block errors\n", + " 3.0 | 1.3877e-03 | 1.7890e-02 | 17762 | 12800000 | 1789 | 100000 | 2.8 |reached max iter \n", + " 4.0 | 8.3594e-05 | 1.2800e-03 | 1070 | 12800000 | 128 | 100000 | 2.8 |reached max iter \n", + " 5.0 | 3.7500e-06 | 6.0000e-05 | 48 | 12800000 | 6 | 100000 | 2.8 |reached max iter \n", + " 6.0 | 0.0000e+00 | 0.0000e+00 | 0 | 12800000 | 0 | 100000 | 2.6 |reached max iter \n", + "\n", + "Simulation stopped as no error occurred @ EbNo = 6.0 dB.\n", + "\n", + "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", + "---------------------------------------------------------------------------------------------------------------------------------------\n", + " 0.0 | 1.1879e-01 | 7.5967e-01 | 45616 | 384000 | 2279 | 3000 | 2.5 |reached target block errors\n", + " 1.0 | 4.9263e-02 | 4.0800e-01 | 31528 | 640000 | 2040 | 5000 | 0.4 |reached target block errors\n", + " 2.0 | 1.0201e-02 | 1.1122e-01 | 23504 | 2304000 | 2002 | 18000 | 1.5 |reached target block errors\n", + " 3.0 | 1.0783e-03 | 1.5020e-02 | 13802 | 12800000 | 1502 | 100000 | 8.5 |reached max iter \n", + " 4.0 | 7.6875e-05 | 1.1900e-03 | 984 | 12800000 | 119 | 100000 | 8.5 |reached max iter \n", + " 5.0 | 0.0000e+00 | 0.0000e+00 | 0 | 12800000 | 0 | 100000 | 8.5 |reached max iter \n", + "\n", + "Simulation stopped as no error occurred @ EbNo = 5.0 dB.\n", + "\n", + "Note: Required memory complexity is large for the given code parameters and t=4. Please consider small batch-sizes to keep the inference complexity small and activate XLA mode if possible.\n", + "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", + "---------------------------------------------------------------------------------------------------------------------------------------\n", + " 0.0 | 1.1633e-01 | 7.5600e-01 | 44672 | 384000 | 2268 | 3000 | 6.5 |reached target block errors\n", + " 1.0 | 5.0106e-02 | 4.1300e-01 | 32068 | 640000 | 2065 | 5000 | 6.4 |reached target block errors\n", + " 2.0 | 1.0090e-02 | 1.0989e-01 | 24540 | 2432000 | 2088 | 19000 | 24.4 |reached target block errors\n", + " 3.0 | 1.0605e-03 | 1.4590e-02 | 13574 | 12800000 | 1459 | 100000 | 128.3 |reached max iter \n", + " 4.0 | 7.7187e-05 | 1.1900e-03 | 988 | 12800000 | 119 | 100000 | 127.2 |reached max iter \n", + " 5.0 | 2.5000e-06 | 4.0000e-05 | 32 | 12800000 | 4 | 100000 | 128.2 |reached max iter \n", + " 6.0 | 0.0000e+00 | 0.0000e+00 | 0 | 12800000 | 0 | 100000 | 127.9 |reached max iter \n", + "\n", + "Simulation stopped as no error occurred @ EbNo = 6.0 dB.\n", + "\n" + ] + }, + { + "data": { + "image/png": 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v3r3l6G3xjEYjO3fu5O+//+bMmTOkpqaSnZ1tldogLi6uyOcluRkzFsuyiI+joyM+Pj4kJCQAWAW9LdWuXZu1a9dWZveqLYPBwLBhwzh58qR53xdffEGbNm3KVH/58uU8//zzXLlyBTAFNBs3boy/vz86nY6TJ0+SlpbGpk2b2LRpEz169GDBggXUqVOn0q8lPz+fJ554ggULFgCmW+N//vlnhg8fXuY2evfuTe/evSu9b6W5cOECo0ePZuPGjeZ9tWrVomHDhmi1Wi5cuMD58+e5evUqL7zwAh9//DFz5syhf//+RbZnucBVSSkRrmdZ1nJRPiGEEEJUHgmwCiGEEEJUkK+vL6+//jqvvfaa1e26ERERVuXKOsvRstz1bVTEvHnzeO+994iNjS1znYJV2UtT1gV2bkRZA3aWs4MLZmzerhRF4emnn2blypXmfS+99FKZZ3vOnDmTsWPHmrdHjx7NhAkTCAwMNO8zGo38/vvvjB8/nsuXL7N582Z69OjB33//bXU7emUYOnQo+/fvB0zB9CVLlljl3a2uoqOj6dKli/kLi6ZNm/Ldd9/Ro0cPq3KnT5/mtddeY/Xq1SQkJDB48GBWrlxZ5JcyluO8uFnFRbEsW9WzzoUQQojblQRYhRBCCCGK4OXlRceOHa32OTg44OHhQXBwMHfeeSc9e/bEwcGhUF3LWZROTk5lXvDJ19fX/DwtLQ1FUSqcX3L8+PF899135a6Xm5tbpnJubm7lbru87O3ty13Hcmbu7ejll1/m559/Nm8/9dRTJeb/tXTq1Cn+85//mLfffPNNPv/880Ll1Go1Dz74IOHh4bRv356kpCROnTrFO++8U6ExV5KC4CqYgq23QnAVYMSIEebgaqNGjdizZ0+Ri4A1adKEVatW8cgjj7B06VLy8/N58skniYyMLBQMdXV1NT/Pyckpc18sv3SwbEMIIYQQlUcWuRJCCCGEKEKrVq1Yu3at1WPFihXMmzePjz76iP79+xcZXAXrIGV5goSWZY1Go9UtweWxePFiq0BX8+bNmTRpEnv27OHy5ctkZ2djNBpRFAVFUawCcmVV1gV2xM3z7rvvMmXKFPP2Y489xsyZM8scpP/mm2/Mt5DXrl2bDz/8sMTywcHBvPPOO+bt2bNnk5mZWYGeF89yJudPP/1UZMC3utm9ezc7duwwb0+cOLHI4GoBlUrF1KlTzT//V69eZfHixYXKWX4Bk5mZWebXuiDVA4CPj0+Z6gghhBCifGQGqxBCCCFEJbMMppQn4GS5wreTk1OFZnACVkGo+++/nyVLlqDVast03prq6tWrjBw5stLb7d27N6+++mqlt1ten332GZ999pl5e9CgQfzyyy/lCoRb5grt06dPsV8gWBo8eLD5+nU6Hfv376d79+7l6HnJpk2bxscff2z+EuDtt99Go9Hw+uuvl7mNDRs28PXXX1danwq8+uqrReZ2tXwdtVot9957b6lt1alTh44dO7Jz504Atm/fzujRo63KXL+Y2IULF2jWrFmpbVumCGnSpEmp5YUQQghRfhJgFUIIIYSoZH5+fubnBoOBCxcuUK9evVLrRUZGFtlGecTHx3PkyBHz9jfffFNicBXKvrDVrSwnJ4d169ZVertVsbBTeU2ePJl3333XvN23b1+WLFlSroWQwHocBAUFlanO9eUsZ0tWBpVKxaxZszAYDMybNw+AN954A41GwyuvvFKmNuLi4qrkvX/ssceKPV8BPz8/HB0dy9Se5WtZ1OvYtGlTq+3Dhw+XGmDV6/UcP3682DaEEEIIUTnk3i4hhBBCiErWqlUrq+DWvn37ylRv79695uft2rWr0LktZ6v5+voSHBxcap09e/ZU6FzC9n788Udefvll83a3bt34/fffyzT79HqWdcqa4/P6RcXKmm+4PNRqNT///DPDhw8373v11Vet0iFUJxV5HcH6tSzqdWzQoIHVYmMFs11LcvDgQas+3H333WXujxBCCCHKTgKsQgghhBCVzMnJifDwcPP2okWLSq2TkpLC6tWrzdtdu3at0LnLm7c1MjLSKl9kTRUcHGzOOVuZjzlz5tjsmubPn8+zzz5r3u7cuTOrVq2qcJAzICDA/PzAgQNlqnPw4EGr7bp161bo3KVRq9XMmTOHoUOHmve99NJLZVpUa9SoUVXy3o8aNarI81m+jikpKZw/f75M12j5Whb3Og4aNMj8/LfffjPnzC3OggULzM+bN29OaGhomfoihBBCiPKRAKsQQgghRBV46qmnzM9///33Umex/ve//0Wn0wGmxa4sZ+uVh7+/v/l5YmIiZ8+eLbH8iy++iKIoFTqXsJ3ly5czatQojEYjAO3bt+evv/66oVXiLYP6O3fu5NChQ6XW+eabb8zP3d3dadu2bZHloqOjUalU5seECRPK3T+NRsO8efN49NFHzfvGjx/P9OnTy91WVbr+y5GyzLRdunQpFy9eNG/fc889RZazDOomJibyww8/FNvmxYsXmTt3bpF1hRBCCFG5JMAqhBBCCFEFhg8fTv369QFQFIUHH3yw2GDn9OnTrWbijR07llq1alXovPXr1zefF+CFF14ocpabXq/nueees5o1K24Na9eu5fHHH8dgMADQpk0b1q9fX+JK9WVhGYBTFIWHHnqIc+fOFVlWURTeeecdq/EzYsSIcud9LS+NRsP8+fN5+OGHzfv+85//lBhovNnuvPNOq8Wkpk6dysyZM4stv2fPHsaOHWverl27Nvfdd1+RZTt06GA1i/Wdd95h165dhcqlp6czdOhQ8wJ2/v7+PP/88+W+FiGEEEKUjSxyJYQQQghRBZydnfnpp5/o06cPBoOBixcv0qZNG8aMGUPPnj3x9PQkJiaG+fPns2HDBnO9hg0b8vnnn9/QuV988UXzAkDr1q0jPDycZ599lubNm5OXl8eRI0eYPXs2p0+fRqPRMGLECPMq7aJy/fLLLzz99NOF9lsGvX/55RcWL15cqMz69euLzJn5wAMPWNXXarU8/vjjZe7T3LlzqV27dqH9d911F8OHD2f+/PkAREVF0bp1a4YPH06vXr3w9/dHp9Nx7Ngx5s6dy9GjR811AwICeP/998vchxthZ2fHwoULMRgMLF++HEVRePbZZ9FoNIwZM+am9KEkGo2GKVOm0L9/fwwGA4qiMHbsWObPn89jjz1Go0aN0Gq1XLhwgdWrV/Pbb7+Zg+UAX331FS4uLsW2P2XKFPbs2UNCQgKZmZn07NmT0aNH06dPH1xdXTl69ChTp04lKioKMKVX+OGHH6okP64QQgghTCTAKoQQQghRRXr06MGvv/7K0KFDycvLIycnh6lTpzJ16tQiyzdp0oQNGzaUGFwpixdeeIF169aZV04/fvx4kbPX1Go133zzDW5ubhJgrSIGg4Hc3NwSyxiNxiLLFNz+f73rF07av39/ufpU0sJLs2bNIiMjgxUrVpjLzpw5s8QZmPXq1ePPP/8scdZ1fHy81XarVq3K1efr2dnZsXjxYh555BH++OMPcxBTo9Hw5JNP3lDblaFPnz7Mnj2bcePGmd/b7du3s3379mLr2NnZMXHixFLTgwQHB/PHH38wcOBAkpOTyc3NZfr06UWmSigI9g4cOPDGLkgIIYQQJZIUAUIIIYQQVejBBx/k8OHDDBgwAI1GU2QZDw8P3n33XQ4cOGC1SnhFaTQaVq5cySuvvFLsavItWrRg3bp1jB8//obPJ2oOBwcH/vjjD+bNm0ebNm1KLOvj48Obb77J0aNHadmyZYllLRdSa9q0KUOGDLnhvmq1WpYsWWK+ZV5RFMaMGWOVd9SWRo4cyaFDh3j00UfRarXFllOr1QwaNIjdu3ebZ56XpnPnzhw9epQHH3yw2LQMHTt2ZMeOHZIaQAghhLgJVIqsaiCEEEIIcVMkJSWxdetW4uLiyMrKwsfHh0aNGnHXXXeVGIC50XNu2bLFfLuwv78/rVq1uuEZhOL2cOHCBfbv309cXBwZGRk4Ojri4+NjHkNlzbk6ZMgQ/vjjDwDmzZvHE088UYW9rn4yMzPZv38/Z8+eJSUlBTB9sRIaGkrHjh3x9PSscNsJCQls376dixcvkpeXR0BAAB06dKBRo0aV1HshhBBClEYCrEIIIYQQQogq5efnR2JiIiEhIZw9e7bKF8MSQgghhLiZJEWAEEIIIYQQosqcOnWKxMREAN58800JrgohhBCixpEAqxBCCCGEEKLKFORfDQgIYNSoUbbtjBBCCCFEFZAUAUIIIYQQQgghhBBCCFFBMoNVCCGEEEIIIYQQQgghKkgSINVARqORS5cu4ebmhkqlsnV3hBBCCCGEEEIIIYS4pSiKQkZGBgEBAajVJc9RlQBrDXTp0iWCgoJs3Q0hhBBCCCGEEEIIIW5psbGxBAYGllhGAqw1kJubG2AaAO7u7jbuTeXT6/WsX7+ePn36oNVqbd0dcZuScShsTcagsDUZg8LWZAwKW5MxKGxNxqCwtZo+BtPT0wkKCjLH2UoiAdYaqCAtgLu7e40NsDo7O+Pu7l4jf4DFrUHGobA1GYPC1mQMCluTMShsTcagsDUZg8LWbpcxWJb0m7LIlRBCCCGEEEIIIYQQQlSQBFiFEEIIIYQQQgghhBCigiTAKoQQQgghhBBCCCGEEBUkAVYhhBBCCCGEEEIIIYSoIAmwCiGEEEIIIYQQQgghRAVJgFUIIYQQQgghhBBCCCEqSAKsQgghhBBCCCGEEEIIUUESYBVCCCGEEEIIIYQQQogKkgCrEEIIIYQQQgghhBBCVJAEWIUQQgghhBBCCCGEEKKC7GzdASGEEEIIIYQQQtQMiqKg1+sxGo227kqNp9frsbOzQ6fTYTAYbN0dcRuqzmNQo9FgZ2eHSqW6KeeTAKsQQgghhBBCCCFuSHZ2NmlpaWRkZFS7QEtNpSgKderUITY29qYFkYSwVN3HoIODA56ennh5eVV5/yTAKoQQQgghhBBCiArLyMjg4sWLaLVaPD09cXFxQa1WV8uAS01iNBrJzMzE1dUVtVoyQIqbr7qOQUVRyM/PJy0tjatXr5KXl0edOnWq9JwSYBVCCCGEEEIIIUSFZGdnc/HiRdzd3QkICJCg6k1kNBrJy8vD0dGxWgW3xO2juo9BNzc3UlJSuHLlCk5OTnh4eFTZuarf1QshhBBCCCGEEOKWkJaWhlarleCqEKJa8vLywtnZmfT09Co9jwRYhRBCCCGEEEIIUW6KopCRkYG7u7sEV4UQ1ZarqyvZ2dlVuvieBFiFEEIIIYQQQghRbnq9HoPBgIuLi627IoQQxXJ0dMRoNJKfn19l55AAazViMBiYOHEiYWFhODg4EBoayieffFKlA0AIIYQQQgghhKiIgtlg1TH3ohBCFCj4jKrKGayyyFU1Mn78eL7//ntGjhxJly5d+Pvvv3n//feJiopi9uzZtu6eEEIIIYQQQghRiKQHEEJUZzfjM0oCrNXEsWPHmDFjBs899xzTpk0DYMyYMXh4eDBp0iSeffZZ2rdvb+NeCiGEEEIIIYQQQgghLMk8/mpi8eLFKIrCSy+9ZLW/YHvx4sU3v1NCCCGEEEIIIYQQQogSSYC1mjhw4AA+Pj40bNjQan9QUBABAQEcOHDARj0TQgghhBBCCCGEEEIURwKsFrKzs1mzZg2ffPIJDzzwAPXr10elUqFSqZgwYUKZ2sjIyGDChAm0bNkSV1dXPDw86NChA19//TV5eXnF1rt06RJ169Yt8ljdunWJi4uryCUJIYQQQgghhBBCCCGqkORgtbBv3z769+9f4foxMTF069aN6OhoAJydncnNzeXAgQMcOHCABQsWsGnTJry8vArVzc7OxsfHp8h2HR0dycnJqXC/hBBCCCGEEEIIIYQQVUNmsF7Hy8uLnj178vrrr7No0SLq1KlTpnr5+fkMHDiQ6Oho/P392bBhA1lZWWRnZ7N48WLc3Nz4559/GD58eJH1C4KxRdHpdDg5OVX4moQQQgghhBBCCCHEzTdixAhUKhX/+c9/bN2VW8rFixdxcHDA3t6es2fP2ro7pZIAq4WuXbuSnJzMxo0b+eKLL3jsscdwcHAoU925c+dy7NgxAJYtW0avXr0AUKvVPProo/zwww8A/PXXX2zatKlQ/YCAgGLTAMTFxRWbPkAIIYQQQgghhBC3j6ysLGbMmMF9991Hs2bNcHZ2xsHBAT8/Pzp06MBTTz3FzJkziY2NLVN7Fy5c4IsvvqB3794EBwfj4uKCk5MTdevWpW/fvnzyySdERUUVWXfr1q3lTq1YXunp6SxevJhXX32Ve+65h7CwMDw8PLC3t6dWrVp069aNL774gqSkpBLb0ev1rF27lpdffpnOnTvj6+uLVqvF09OTdu3a8frrr3P+/PlK7fu+ffuYP38+9vb2vPXWW4WOW75+RT1cXV1p1KgRTzzxBJs3by7xXNHR0eZ6o0aNKndfR40aVWJfinocPny4UDvFlbW3t6d27drcc889fPrpp8THx5fYn8DAQJ588kn0ej2vvfZaua/nZpMAqwWNRlPhunPnzgWge/fudOrUqdDxxx57jJCQEADmzZtX6Hh4eDhJSUmcO3fOan9sbCyXLl0iPDy8wn0TQgghhBBCCCHErW/Pnj00a9aMZ599lrVr13L58mVyc3PJy8sjMTGRAwcO8PPPPzN27Fg6dOhQYls6nY6XX36ZRo0a8eabb7Jx40ZiYmLIzs5Gp9Nx6dIl1q9fz/vvv09oaCiPPvpomYO2lWnfvn08/vjjTJo0ie3btxMZGUl6ejp6vZ6EhAS2bdvGm2++SZMmTVi3bl2RbSQkJODv70+/fv2YPHkye/bsISkpifz8fNLS0vjnn3/46quvaNq0KVOmTKm0vr/33nsoisJTTz1FYGBguetnZWVx7tw55s+fT8+ePRk5ciQGg6HS+ncz6fV64uPj2b59O++99x5NmzZl/fr1JdZ5++230Wq1rFq1in379t2knlaM5GCtBNnZ2ezatQuAfv36FVlGpVJx77338v333xc5gB599FE+//xzJk+ezLRp08z7J0+eDJgCtEIIIYQQQgghhLg9nT17lr59+5KRkQHAwIED6d+/P61bt8bR0ZHExESOHDnChg0b2LJlS4ltJSYmMnDgQP7++28A3NzcGDp0KD169CAwMBCtVsuVK1fYtWsXy5cv59y5cyxZsoROnTrx0ksvVfWlFhIUFET37t0JDw8nKCgIf39/jEYjFy9eZOnSpSxfvpzExEQGDRrEvn37aN26tVX93Nxc8wzXNm3aMHjwYO644w5q165NWloaa9asYerUqeh0Ol566SWcnJwYO3bsDfV53759bNiwAYBXX3211PLPPvsszz33nHlbURSSk5PZs2cP33zzDfHx8cybN4+goCA++eSTG+pbadatW0dAQECp5Ro0aFDsgu7t27fn559/Nm9nZGQQERHB9OnT+fvvv0lOTuaBBx7g2LFj5gmJ16tfvz4PPvggixcv5pNPPmHlypUVu6CbQAKsleDUqVMYjUYAWrRoUWy5gmNXrlwhOTkZb29v87HWrVszduxYpk+fTlZWFl27dmXPnj3Mnj2bkSNHlvrNkxBCCCGEEEIIIWqud9991xxc/fnnnxkxYgTp6em4u7ujVptuUO7duzevvfYaCQkJLFmypMh2jEYjjzzyiDm4OmDAAGbPnk2tWrUKlR04cCCfffYZCxYssNlt2t27d+fChQvFHn/kkUf4448/GDJkCHl5eXz44YcsX77cqoxKpaJ379589NFH3HnnnUWe48EHH6R79+7k5OTwxhtv8Pjjj+Pm5lbhfhfMhL3jjjsICwsrtXytWrWKjCndc889DBo0iPDwcHQ6Hd9++y3//e9/sbe3r3DfStOoUSOCg4NLLWc0GosNsLq4uBS6nk6dOjF8+HAeeeQRli5dSlZWFl9//TXfffddsecYOnQoixcvZvXq1Zw/f54GDRqU61puFgmwVoJLly6Zn5eUK9Xy2KVLl6wCrADfffcd9evXZ9asWSxatIiAgAA+/PBD3n777RLPn5uba7VAVnp6OmCafq3X68t1LbeCgmuqidcmbh0yDoWtyRgUtiZjUNiajEFhazIGTdeuKApGo9E86UhUDYPBwOrVqwHTzMARI0agKAqA+T2w5OPjw7PPPlvk+zJ58mTzDNc+ffqwbNky7OzsSnwPhw0bxj333MPZs2etylk+L6oflUGlUpXa7qBBg2jcuDFnzpxhx44dhcr7+/uzdu3aQn221KFDB5599lkmTZpEWloa69at44EHHqhQn9PS0li2bBlgChAWd86yvn5NmjShf//+LF++nIyMDE6ePEmrVq0q1FZxCsZTQVtlqV/SGLy+T5Y+++wzli5dCsDGjRtLPFefPn3w8fEhKSmJn376iY8++qjUfhXVD0VR0Ov15UoPWp7PdwmwVoKCb5AAnJ2diy1necyyTgE7OzvefvvtUgOq1/vf//7Hhx9+WGj/+vXrS+zPra5gqr0QtiTjUNiajEFhazIGha3JGBS2djuPQTs7O+rUqUNmZmaxs9hE5bh69So5OTkA1KtXzzyxCoqOLxQnLy+Pr776CgBHR0cmT55MdnZ2meq6u7vTvn17q3Nb1s3NzbU6drMVxD90Ol2F+9GxY0fz85MnT5oXMC+vX3/91TwRrnfv3sX2pzyvn+Ut+8nJyYXKZmZmmp/r9fpyvwaWwcTMzMxy1S9qDObn5xfbhq+vLy4uLmRlZREbG1vqufr27cvChQtZtGhRhWZS5+XlkZOTw/bt28nPzy9zvbL+bIAEWGuEt99+m1deecW8nZ6eTlBQEH369MHd3d2GPasaer2e8/NfpnnaJnD0QHHwAEcPcHBDcSx47gGO7ige9VDCrvtANOhBo7VN50WNodfr2bBhA71790arlfEkbj4Zg8LWZAwKW5MxKGxNxqApkBUbG4urqyuOjo627k6NZhkUioyMxN3dHUVRyMjIwM3NDZVKVaZ2Vq1axeXLlwF46KGHaNy48Q31y3JSl4ODg81iEGfOnOHYsWOAabZnRfthObvRxcWlwu0UpF8ICgoq8TUuz+t35coV8/OmTZsWKuvq6mp+rtVqy913y88xV1fXMtUvaQza2dmV2IadnV2Z+9qlSxcWLlzI+fPnuXr1Kg0bNiy1b5Z0Oh1OTk7cfffd5fqsKk+QWQKslcAyJ0dJ0W3LYzeSx+N6Dg4OODg4FNqv1Wpr7C96h/wMVFkJkJVAib9G6nWCptctPDanL1w5ZgrElvQI6QaB4dfqGY2QFW86ZucIZfwFJmq2mvxzJm4NMgaFrckYFLYmY1DY2u08Bg0GAyqVCrVabc4BKqqGr68v9evXJyYmhiNHjvDll1+aF04qeA/KYseOHebnAwYMuOH3zbJ+efpRGbKzs4mLi2PVqlV88cUX5iD0Sy+9VOF+WL4+zZo1q3A7O3fuBExpB0pqo6yv3+nTp80pIu688078/f0r3FZxLAOkZf2ZLri1v7jzFdfG5cuXSUtLAyA4OLjUc1nmzd2xY0e5vxhQq9WoVKpyf16Xp6wEWCuB5TTtuLi4QnkwLI8VVUeUX77aAcUjCJUuHXLTii/o6FF4ny4NDHnwb4C2WH3srQOsWfHw9b8/xBr7koOzdz4PbrUt6iZBduK14xKgFUIIIYQQQghRDuPHjzffHv3WW28xY8YM+vbtyz333MOdd95Z7Erslo4cOWJ+Hh4eXkLJ6mnOnDk8+eSTxR5/6623GDp0aIXavnz5snnVez8/P7p3716hduLj44mMjASgbdu25ap3/Phx87aiKKSmprJnzx6++eYbcnJy8PDw4JtvvqlQv8rj7NmzVikHiuLi4kL9+vUr1P7//vc/8/OHHnqo1PItW7ZEq9Wi1+s5ePAgY8aMqdB5q5IEWCtB06ZNUavVGI1Gjh8/Tr9+/YosV/CDUqdOnUILXInyOet/P2H9fzR9m2A0QG6GKXB6/cO1duHKfk3AzulameICtNcHZ3UW5UoL0LYbCVic++TvsPrVa9vFBWi9gqHXBOu2Lh+FfJ11OQnQCiGEEEIIIW4xs3acZ9aOqFLLtajrzqyRHaz2jZm7n+Nxpd+uO6ZrCGO6XltlPDM3n15fbytT/2aOaE/LwGt/B246dZV3fz9eQg0TZwcNm1/tVqZz3IiXX36ZkydP8tNPPwEQHR3NDz/8wA8//ABA7dq16datG8OGDWPAgAFFpg1ISkoyP69Vq1aV9/lmadOmDT/++CMdOnQovXARFEVh3Lhx5lyi77//foXTXly8eNH8vDyv8ffff8/3339f5DG1Ws0zzzzDyy+/TKNGjSrUr/Lo27dvqWXuueceNm/eXOY2MzIyOHfuHFOnTmXu3LkANGzYkOeff77UunZ2dnh7e3P16lXOnz9f5nPeTBJgrQTOzs7cdddd7Nixg7Vr1/L6668XKqMoCuvWrQNMK6CJSqTWgJOn6VEWjy2w3i4uQFun5XXnsYPG9xUuV1SA1snLelt3XZniArR+TQsHWDdOgMhN1vuKCtA2fwDaPWFd7tjSogO5EqAVQgghhBBC3GQZunyupOtKLefvWTiwlZSVV6a6GTrrBWwURSlTPYA8g/VK5jq9sUx1XR1uTmhFrVYze/ZsHnvsMSZNmsTGjRutcrNevXqVX3/9lV9//ZX27duzePFiQkNDrdqwXIzIxcXlpvS7Mt1///20b98egJycHCIjI1myZAm///47jz/+OJMnT2bAgAHlbvezzz5j1apVAHTv3r1MQb/iJCRc+zvfy8urhJJlZzQaWbx4MY6Ojnz++edFpomsbrZt21ZsbmCVSsXgwYOZPn16mV+jggCrZS7a6kQCrJVk5MiR7Nixgy1btrB3717uuOMOq+O//fabOco+YsSIm9InvV5vtQpcTVFwTZV6bXYu4OoCrtelbrA8h3s9eGhu4bpGA+RlmgOuqtw0FI2zVV2VdyPUrR67VqYgtYEuDVXutW9hjQ7uGK67Lk1OCoWykRQRoDXUaYPRsm6+Du2y0UVerlIQoHVwR3H0wHDvl+Df+lqBlCjU57eYFg37dxEx5d/yEqA1qZJxKEQ5yBgUtiZjUNiajEFhazIGTdeuKApGo9Gci7EkLg4a6riXHhjydrYv1J63s32Z6ro4aKzqKopSpnoAdmqs6trbqcpU19nerkzXX1l69uxJz549SUtLY+PGjZw4cYKDBw+yY8cOc17LAwcO0LVrV/bv32+Vr9NyPZiMjIwbXpTq+te6rK9DXFwcKSkpRR7z8vKibt26RR5zd3enWbNm5u3w8HAeeeQRfvnlF5588kkGDx7MzJkzGTVqVJmvYcGCBbz//vsAhISEMH/+fIAKv6eJiYnm5x4eHiW2Y3nsv//9Lx988IHV8ZycHCIiIpg/fz6TJ09m8uTJHDhwgDVr1lgtkHV9W+V5LyzrFIiMjCQ4OLjMdcp7voCAAF544QXq1KlT5noFgdisrKxyX5vRaERRFPR6vdVCZqUpz+e7BFivk5KSgsFgMG8XvGnZ2dlWPySOjo5WK7SNHDmSKVOmcOzYMR588EHmzp1Lz549MRqNLFu2jKeffhqAfv360bNnzyrp+7Rp05g2bZq5/+vXry/0A1eTbNiwwdZdKN6JdYX3afqDC6aHJcWInSEHrSEbFQrZf/1ldThE3RwXP1+0huxiHwCnoi4RmX2troM+lXuL6Z7KIkCrAnbs2kGq87UcwXVT/qZ99PRiL8+gskOvcUan9WRbk0+sjvmn7sdVdwW9xvnaw67guQt6jTNGlbbGBGir9TgUtwUZg8LWZAwKW5MxKGztdh6DdnZ21KlTh8zMTPLy8kot/0grHx5p5VOmtq9fvfvr+8u+avj1ddc+175CdTsEOJa5bnlWG68sKpWK3r1707t3bwByc3NZunQp7733HqmpqVy+fJm3336bb7/91lzHw+NaCoTIyMhCM1zLy3Ix79zc3DK/Dm+++SaLFi0q8tjjjz/O9OnF/z1alMGDB3P//ffz+++/88ILL9C9e/cyzYxct24do0ePRlEUateuzbJly3B2dr6h99MyUJmUlFRiW2V5/erXr8+7775LYGAgL730Ejt37mTChAm89957VuUsc6bq9fpyX4NlMDEzM7Nc9S1nRhdo27Yt3333HWB6TeLj49mzZw8//PADcXFx9O/fn+XLl9O5c+cynaPg+jQaTbmvLS8vj5ycHLZv324167s0JS1kfz0JsF6nbdu2xMTEFNr/5Zdf8uWXX5q3R44cyZw5c8zbdnZ2rFy5ku7duxMdHU2vXr1wdnbGaDSi0+nMbS9YsOD6pivN888/z/PPP096ejoeHh706dPnhr+Nqo70ej3T/ppGnFccYV5hhHqE0sCjAUFuQWjVNXH1zv4lHtX/O4O2sVpDY/trQX906Rj80/+dNZuOKjet2Bm0nXv0B+9reYrUB69CdPHn1Cj5aPLTcXB0pn9/6/5pfl+OOuqPEvusaOwxth6Ksd9XVvvVG/9rSvng4AGO7tV6Bq1er2fDhg307t37tl01VtiWjEFhazIGha3JGBS2JmMQdDodsbGxuLq6Vjhfpag4RVHIyMjAzc3NfCv2s88+S4MGDcx/p/3555/89NNP5lXa27Vrx9atWwHTQkblWYSpKJaTuhwcHMocgyjpZ0ar1VYolvHAAw/w+++/k5WVxa5du0pd7Grr1q2MGjUKvV6Pl5cXa9euLXbR8vKoV6+e+blOpyvxWsrz+j3//PN89NFHJCcns3DhQr744gur45aTACvyGlq+J66urmWqX9QYLODu7s6dd95ptW/w4MEMHz6cLl26kJGRwTPPPMPRo0fLdK6C2dne3t7lvjadToeTkxN33313uT6ryhPIlQBrJQoODubo0aN89dVXLF++nKioKLRaLc2bN+fxxx9n/Pjx2Nvb37T+aLXaGvuLPiY/hm0Xt7Hp4rXcpHYqO+q71yfUM5QwzzAaeDagsVdjgj2CbdfRm0ILDkV8QGh94K7xpVc3GtCq1NYBy7AeMHh60QuHWTxUrrUKj7Hcwt9cXU9lyEOj0aK5vu7Bn0wLepXm4TnQfMi17YSzsPWzIvLNehbe51q7UoOzNfnnTNwaZAwKW5MxKGxNxqCwtdt5DBoMBlQqFWq12hzAEzdPwR23Be9BgX79+hEUFERsbCwpKSmkpKTg5+cHQLdu3Zg0aRIAa9as4fHHH7+hPlie9/p+lGTu3LnmhY4qS+3a1xaajo2NLbEv+/btY/Dgweh0OlxdXVmzZg1t2rSp9H6kpaWV2I/yvH5qtZqGDRuyd+9eLl++TEpKCj4+PlbHy9pWUSwDpGX9mS5uDBbVpwKtWrXis88+Y/z48cTGxvL111/z8ccfl3qugpQS9erVK/e1qdVqVCpVuT+vy1NWAqzXiY6OvqH6bm5ufPjhh3z44YeV0yFRpARjQqF9+Uo+kWmRRKZFsj5mPQB3+N/BrD6zrMrtu7wPXydfgtxr6ozXclIXkX/EN8z0qIie/4X2T5UanMWrvnU9va5swVUwBUotpV2AE7+Xre57CWBn8UXHvplw5q+iFwOzDNC61gKv4LKdQwghhBBCCCFsKCAggNjYWMA6cNa3b18CAgK4dOkSv/32G//73/+KzXd6q4mLu5b2znI25/WOHj3KvffeS2ZmJo6OjqxatarQOjo3okGDBjg7O5Odnc3Zs2crrV3A6vb28tzqXt2MGzeOSZMmERUVxTfffMOLL76Ir69vseWvXr1qnk3avHnzm9XNcpEAq7glPer8KC26tOBC1gUiUiOITDUFVqPTotEbr+UNCfO0DhIqisLLW18mPS8dO7Udwe7BhHqGmh4eppmvEni9QQFtTI/y0mhh3I6ig7G56dbbrnWs6+rSynYOrbN1cBUg/iREbi69bsM+MOw3q10tY+ehWbUG/BqBTxj4NjSlWrCr/is6CiGEEEIIIWqm7OxsTp48CZhu07ac5Whvb89rr73GK6+8gk6nY/To0axevbpMC//ExcVx5swZevToUWV9vxG//Xbt77WWLVsWWebs2bP06dOHlJQUtFoty5Yto1u3bpXaD61Wy5133snmzZvZv39/pbVr+b46OTmVGJCs7rRaLW+99Rbjxo0jKyuLb775hk8//bTY8pavY2UGwyuTBFjFLUmj0hDiEUIj30b0qt/LvD/fmM+FjAumgGtqJK39WlvVS8xJJD0v3Vw2IjWCiNQIqzIFgdcJnScUqi+qkFoD/hXMd9P4PnjpWOmzZikiNUBZg7PXz5oFAlIPoE5Mtd6pUoNnPfBpaAq4+oRBaA/wDin3ZQkhhBBCCCEEmBb46dmzJ++//z79+/cv9hZpo9HI+PHjzYsODRo0qFBuzBdffJFVq1axZcsW1q1bx5AhQ5g9e7Y5jcD1FEVh0aJFvPLKK7z11ls3PcA6Z84cHnvssRJzZ37zzTf89e+C0SEhIXTt2rVQmQsXLtCrVy+uXr2KRqNh4cKFhdYUqSxdu3Zl8+bNHD16lNzcXBwcbnwSzoQJE8jJyQFMM5HLEhSvzkaNGsVHH31EXFwc06ZN44033rBahM3Svn37ANOC83fffffN7GaZSYBV3JLUOTryE5PQ+lvPZLRT29HAowENPBrQu37vQvU0ag3Pt3meyNRIIlIjiE6PJt9oPa2+IPDqpnWz2r8pZhPfHf7OPOM1zNO0wJbMeK0GtI6moGZFPDALBnxTenC2znXB37xMHPNTC7enGCEl2vSI+HdF2QdnWwdY0+Lgn1+uzXr1CQN7l4r1XwghhBBCCHFb2LdvHwMHDqRu3brcf//93HHHHfj6+lKnTh3S09P5559/+Omnnzh27BgAHh4eRea2VKvVLFmyhAEDBrB3715WrVpFaGgow4YNo0ePHgQGBqLVarly5Qp///03y5Yt4/Tp06X27/Dhw1aLgRenR48eVgtBlWbChAm8+uqrPPjgg3Tp0oXQ0FBcXV3JyMjg2LFjLFiwgF27dgGmGbo//vhjoeBjUlISvXr1MqdNePXVV2nSpAnHjx8v9rxeXl4VTp8wePBgPvzwQ/Ly8tixYwe9evUqtU58fHyh/uh0Os6dO8e8efNYu3YtYAoyfvTRRyW2FRERUab3omPHjjRr1qzQ/rNnz5KZmVlq/dq1a1c4B3XBbOqXX36ZtLQ0vv32W95///0iy27aZFp/p2/fvjg5OVXofFVNAqw1mF6vR6/Xl17wFqPX63E9dpToCRPQBtfHKTwcx3btcAoPxy4goNC3c5bcNG6Mbjb6WltGPRczLnI+7TyRaZGcTzvP+bTzxGXFUcepjtXrdyLxRPEzXt2CzYHdZt7N6FK3S+VfuKg6GmdwcQYX/5LLWYwHvcqBda1m0rNtfbRp0aiSIlAlR0JSBKrkCFR5WdfKegRb1VXFHsBu6/+smlbcAlB8QlG8w8AnDOXfBx71KnVRLlFzFHw+1cTPeXFrkDEobE3GoLA1GYOma1cUBaPRaF7sRlQNtVpNnTp1uHLlinnG37Rp04ot37BhQxYsWEC9evWKfG+8vb3ZvHkzb7/9NjNmzCAjI4MZM2YwY8aMIttTqVQMHTqUhx56yKo9y+crVqxgxYoVpV7LsmXLCAwMLLWcpeTkZGbOnMnMmTOLLRMYGMisWbPo0aNHoWs+cuQI586dM29/8cUXfPHFFyWec8SIEfz888/l6meB1q1bEx4ezsGDB1mwYEGxs34t+/n999/z/fffl9iun58f8+bNo3nz5oWu0XJ7165d5qBzSSZNmkSTJk0A00zlAn379i21LsDXX3/NU089Zf4cuF5pnwtjxozh008/JTExkSlTpvDiiy8Wyp8bHR3Nnj17ABg6dGiFPmuMRiOKoqDX68s187c8n+8SYK1BCj5gDQYDAOvXr8fZ2dnGvaoanueiANBHx6CPjiF92XLTtocHOSHB5ISEkBMcQl4tPyjj6nKB//53t+pujC5GNqzdYHX8RM4JNGgwYLDan2/MJyItgog0U+A1WBNMulu6VZlDeYdwwIFamlp4q73RqG7tqfziXxoH1h+9AjgCLcC+BfgDdRQc81Nx1V3GNfcKsQfOY9BcS7gednU116flVmVcQpVxCaJ3mPfpNc781fJ7qwCrZ1YkKoxkOvijtys+cbu4fWzYsKH0QkJUIRmDwtZkDApbu53HoJ2dHXXq1CEzM5O8vDxbd6fGO3HiBPv372fbtm3s37+fiIgIEhIS0Ol0ODs74+/vT/Pmzenfvz+DBg3C3t7evDBQcT788EPGjBnDsmXL2LZtGxERESQlJaEoCl5eXjRt2pTOnTvz8MMPm2edWraZnZ1d7uvIyckptV+WfvvtN9avX8/evXs5f/48CQkJJCcnm/OQtmzZkr59+3L//ffj7OxcZNsV6adery9XP683fPhwDh48yPLly/n888+LTBNQWr/s7e3x8vKiSZMm9O7dm2HDhuHp6Vlkv8oy4/R6Op3O3FZFvizKzc0FMKeksJSfn1+m1++ZZ57hk08+ISkpicmTJ/PCCy9YHZ8zZw6KouDv70/37t0r9J7k5eWRk5PD9u3by7U4WHnGjUqxDFGLGiE9PR0PDw8SExNxd3e3dXcqnV6vZ/4LH+FxNoKmCafRKoZiy7r27Uudr76svHMb9cRmxBKVFkVkmmlhrai0KKIzrqUaeLjhw7zd4W2rej2X9SQlNwWwnvEa6hFqnvka5BaEnVq+87hV6PV6NmzYQO/evct/S0T6JVSX/jHNdE2KuDbrNSfFqpgxoB2GJ9db7dMsehj1+S0AKM4+FjNeTbNfFZ8w8AoGzXWLeYka54bGoBCVQMagsDUZg8LWZAyagjOxsbEEBweXmB9TVA1FUcjIyMDNza3EOzmFbWRmZhIWFkZCQgLz5s1j2LBhtu5SpavqMWg0GmnevDlnz57ls88+480336xQOzqdjujoaIKCgsr1WZWeno6vry9paWmlxtckmlODabXaGvuL/oRLfeoHJnO+wcNkqTywz84k5MoRWiSewdFw7VsX+2bNrF4DRa/n4vgXcGrdCqfwcJxatUJdjh8uLVoaOzSmsW9jq/16o57Y9FgiUiMIcA2wOmeyLtkcXIXCM14L2KntmNJ9CncHXkvYrDfoUalUEnitxir0c+ZT3/S4XlYSJEVA0jlIPIfazR/19W0nRZqfqrKTUGUnwcW91mVUGuj1Adz14rV9RiNkJYBrLUk5UMPU5M96cWuQMShsTcagsLXbeQwaDAZUKhVqtbrYRZdE1Sm4VbrgPRDVi7u7O++//z4vvPACn3/+OcOGDatx71NVj8Fff/2Vs2fP4uvry/jx4yt8DrVajUqlKvfndXnKStRG3JJCcuMwGC5hn3MJ0zw9DQmBdfgr5CHS1F7YZ2UQEn+Uq9k+vGNRT3fqFJlbt5K5dSsAKq0Wx1atcA4Px7lDe5zatkXjWv7brrVqLQ08G9DAs0GhY44aR76+52vTjNdU06O4xbVqO9e22rft4jbe2P4GwR7BhHpcW1yrgWcD6rnVk8BrTePiY3rUu6Po44oCnZ6DxLOQeM4UjM24XEQ5A7hctwJoajR82xYc3E2LalkusOXbELxDwb5mphQRQgghhBBCCFt45plnmDx5MidPnmTp0qU88sgjtu7SLUNRFD799FPAlMri+tys1Y1EZ8QtyU6Vdl0mVANKfhyO+XGY5qNqSAsIwDsl1apU6j9HrLYVvZ6cgwfJOXiQpB9/BLUaxyZNcGofTu3XXkNlf+O3WTtrnekT3Mdqn+WM14LA6/m084R4hFiVi0yNRG/Ucy7lHOdSzlkd06q15sBrS9+WjGg+4ob7Kqo5lQrufNZ6X26GKdCaeG3mK0kR4NfEulzivzOmc9Ph0iHT43oeQaaA6yPzwLHmpRcRQgghhBBCiJtJq9UyZ84cNm3aVK7cnwIuX77MQw89xNChQxk3bpytu1MqCbCKW1LQvb24u9OdxB4/zrm9B7l87gS5WUkWJQwo+bG4qRta1TverhfL787GLiuV0ISTtEuLxi8t/loBoxHdyZPkp6RQ5513rOrmHD+BnZcn2rp1b7j/Jc14teRq70qYZ1iRM14tA69Xsq4UCrDOPDoTg2IgzDOMUM9QyfFaUzm4QUBb06MkWicI7WkKwqbGAkWk306LhewkU5uWNn0MZ9dazHptCL7/zoJ19Ki0SxFCCCGEEEKImqZr16507drV1t245QQEBDBhwgRbd6PMJNoiblmuXt606tGTVj16ApCemEDkgUOc23eIq+dPkpeTQrM7w63q7N99irC0VYCW/Fp1WRd4L1e0vqiyU2mScpYOaTH4JV7E0KJ1ofNd+fBDdMeOYefvj3P79ua0AvYNGlRZQvFhTYcxrOkw6xmvqZFEpEZwPu080WnR5Cv5hHqGFqq7+Mxi4rOvBY8LZryGeZhSDEjg9TYT0tX0ANDnQHKU9YzXxHOmbc96hXO0XjkGV4+bHtdzqXUt1UDDPtB0QNVfixBCCCGEEEIIUY1IVEXUGO6+frS9ty9t7+0LQFr8VZw8rGfXNc+5gulGaT0qfTTe+mi8AbDH4FmXNbV7cLGFHxofb+Za1DNmZaE7eRKA/MuXSV+1ivRVqwDQeHnh3D4cp/BwnNt3wLFJY1R2lfujVdyMV71Rz4X0C4UCpOl56VbB1YKyRaUa+KzLZwwMHWjeztJnEZ8dL4HXmkzrBLWbmR6WFAXysgqXN+aD2s70/+tlxZseMbtMM18tA6xGIywdBZ71LWa+NgRnH1loSwghhBBCCCFEjSHRkxpMr9ej1+tt3Y1KV3BNpV2bs5d3oXKh7RqQEd+exNgzGPIyLErnodFH4aePwg8g1Y1cXT/UGg0Ahhwdu+4cSMOrEfhdOIcqL9dc05CSQsaGjWRs2AiA/9RvcenW7Yavs6zqudQDrK/TXrFn6X1LOZ92nsg0U37X82nniUmPIV+xDpLVd61vVXfPxT28vP1ltGot9d3rmxbX8gilgUcDGng0INA1UAKvlH0c3nLUDnD9NT32Kxj0kBqDKikCVXIEqqQISIpAlXweVZYpmJ/v1QDFsm56HNqTKwqdQnH0RPl3oS3FOxTFJwwlpFvh1ASiRDV2DIpbhoxBYWsyBoWtyRg0XbuiKBiNRvNq4uLmURTF/H95/YUt3Cpj0Gg0oigKer0ezb9xnrIoz+e7Sil4NcQtb9q0aUybNg2DwcDZs2dZuHAhzs6yKnhRFEUhLy2NzJgrZF++TF5aHIohx3xc5VKP0MF9zduJOohYf4JclR2RTnUgN4POWedplRxF3ctRaHU6c9mID/6L0eJ1dzt8BI+/95ATHEJOSAg5wfVRHBxuzoVex6AYSDImEW+IJ94YT7whngecH8BedW0xr226bWzQbSi2DQ0a/NR+BNoFcr/z/Teh16K6s8vPwjX3Cjn2PuRqPc37/dKP0znyizK1saHZV2Q71DJve2ZF4pkdRaajP5kOddBpvWXWqxBCCCFENWNnZ0edOnUICgrCvhIWCBZCiKqQl5dHbGwsV65cKddiY9nZ2QwdOpS0tDTc3UteCFoCrDVQeno6Hh4eJCYmljoAbkV6vZ4NGzbQu3dvtFptpbSpKAoJMRc4vesAF44dJah5a+554gHz8VX7LxA15XUgz7RD5YxOW5erjnU561QHZ00+/bhCEzJp++l7OGivfSMS/8EHpC///drJ1GocmjTBsV07nNqH49S2LRpv70q5jsqwOXYza6PXEpkWSWxGbKEZrwVa+LRgXt95Vvu+OPAFKbkpt8WM16oYhzWOYoS0i6iSIy1mvJpmv6rS464V09iT/0YsqK/93Ki3fIxm95RrZbTO4B2K4hOK4h1mmvXqEwbeobftzFcZg8LWZAwKW5MxKGxNxiDodDpiY2MJDg7G0dHR1t257SiKQkZGBm5ublW2LogQJblVxqBOpyM6OpqgoKByfValp6fj6+tbpgBrzYt6CDOtVlujf9FX9vXVbRhG3YZhwGOFjoXmpBBVEFwFULJxzDtH/bxz1E8HVC5csa/LMedAGqdl4OrvZy6af+WqdWNGI7knT5J78iRp8+cDYN+gAV6PPYb3iCcq7Xoqqm+DvvRtYJq9qzfoiUmPISItgvOp502La6WaUg2EeYUVev23x23nUtYlq332anuCPYIJ9TSlGgjzDKN1rdb4OvnetGuqSjX95+yG+YWaHvSx3p+XBUmRpqBrdhJah+t+yaWct9pU6bPh6jFUV49ZlwvtAU/8br3vwt/gWsuU+1Vd9ts/blUyBoWtyRgUtiZjUNja7TwGDQYDKpUKtVqNWq22dXduOwW3ZBe8B0LcbLfKGFSr1ahUqnJ/XpenrARYhSiDlt3b4lnrS07u3M/FU8fISDiPYryWhxUlC+fcs9TPPYsx50Grul/dMw59y4fpY7hMk4RIHE4dI+/sWasyeefPY8zKtNqnGAykLl+Oc3g49iEhNvk2SKvREuYVRphXmNV+vUFPdn621T5dvo74HOuFtQDyjHmcTTnL2ZRr1/xR548Y0nCIeTstN40DVw4Q6hlKoFvNnPEqrmPvAv6tTI+idHnZFDxNjICkc5AUASnRplmxlnwaWm8rCix4BHLTQGMP3g3AJ+zfRbbCLBbaqj6zxoUQQgghhBBC3NokiiFEGajVGuq3bEr9lk0BMBoMRB0+yald+7l05jgZSVGg6FHZuVE7pJ65nt5gxHP7X3hkn+GIfSCrneoS22oUbe/3pYfhCo2uRsCxw+hOnMQpPNzqnLlnz3Ll/f8CoPH2xjk8HOcO7XEKD8exSRNU5UjMXNm0Gi0eGg+rfY52juwbuo/o9Ggi0yKJTL32iEmPwaAYzGVDPUOt6h5NOMpLW18CrGe8hnmGmRbZ8gwlyC0IzW0wG1H8q2646WEpPw9SoiDxnCnomhgBIV2ty2QlmIKrAIY8SDhtelzPyRsenQ/Bd13bp9eBSg12kj9MCCGEEEIIIUTZSYBViApQazSEhrckNLwlAPl6PRH7j5ORlGw10/RSag7uuouojBm4607RTHeKZilgjPRgh0MgCx0DyGg9gg6P+DM8JBQXi3Nk7z9gfm5ITiZjwwYyNpgWn1K7uuLUri3O4e1NQde2batFvhOtRktDr4Y09LKeVag36K0Cr9cHWCNTI83Pi5rxCqbAaxOfJszvN79aXKuwATt78GtsehRLBV1euRaATY40BVqvl5MMLn7W+06tgt/HmlIL+Db8d7arxaxX19qy0JYQQgghhBBCiEIkwCpEJbDTamnSuW2h/f7O9ri4uJCTpgGuzeBUG9PwyknDK+cEpGzAcNyLo5kPETRysLmM5o478X31VXQHDpB96BDGjAzzMWNmJlnbd5C1fQcaP18abt9udV7FYLDpDNfrFRd4LRBeO5xnWj9DZGokEakRXEi/YDXjFUyBV71BXyi4+tq214hKi7o24/XfXK8y4/U25eoHvT64tm00QOoFU4qBxH9TDSSdg+Ro8Aq2rpt0zpSCICXK9Di33vq4vRv4hEK9TtDv86q+EiGEEEIIIYQQtwgJsApRheydtDz34xRys3Sc2HmIiP2HiI86SW7mReBaLkmNIQVfDyeruj/+k8KJ43l43TmMu8a8TSdVKk6nj5N94ADZBw9gSEgEwLl9+0JBxwujnsSYk2OVVsDOy6vKr7eiWvq1pKVfS/N2niGPmPQYc8D1fJppga3G3oVnLp5MOklsRmyRM15DPEJo4NmAMM8w7gm8p8j6ooZTa8A7xPRo2Lvkss4+UKeVafEtfVbh43kZcPkwOLgVPrZkJOhSTbNdfcKuzXz1CIJqnOxdCCGEEEIIIcSNkwCrEDeBg4sj7fp2pl3fzgBkpWVxYttBzh/6h/joU+hzLtP8no5WdRL3HqTllb9g7V9EbPBhr0MgabWCCeoylC7PvEU7TSbK0cNo/f2t6hl1OrIPHwa9Ht3x4yTPnQuAfVgozu3bm9IKtA8vVK86sdfYFznjVVEUq+18Yz4OGgc0Kk2RM17PpJzhTMoZALwcvawCrCm6FJaeXWqa8eoZSqBroMx4vd3dMc70UBTIuGyd6zXpnGk79YIpXcD1ondAdhKc32q9384RvEOvBVxbPAC1m9+UyxFCCCGEEEIIcXNIgLUG0+v16PV6W3ej0hVc0618bfbO9rTt14m2/ToBkJORjYOrs/maDEYFn4wL5vJaQxJ1spOoE30EJXoFf//ux5+OddEHhTEgpDXdLV6LvLg47OvXIy8i0uqceRGR5EVEkrr4VwDs6gZQ56uvcGzRoqovt0ot6b+EPEMeFzIuEJkWyfm08+b/x2bEmgOvwa7BVmPmePxxvv3nW/O2g8aBYPdgGrg3oIFnA0I9Qmng3oC6rnWLDLzWhHEoSuDkB0F+ENTZen++zrQYluX7npuBndFAkdlZ83UQf8L0APL9mqF4N7p2PCUKze4pKD5hKN5hKD5hphywGm2pXZQxKGxNxqCwNRmDwtZkDJquXVEUjEYjRqOx9AqiUhVMQCl4D4S42W6VMWg0GlEUBb1ej6Yc6RTL8/muUq6fEiZuWdOmTWPatGkYDAbOnj3LwoULcXZ2tnW3RAWlnIon80IseWlxKPnxQDE/qk6BhA3pZ97MNUBOPnjnZ+EUHY1TVDRO0dE4xsWhuu4DL/L99zC4upq3HaOicbwUR05ICLl16tzytzbnK/kkGhNJMCTQSNsIB5WD+dju3N38lfNXqW044sg7Hu+gVl17LXKMOTioHKz2iducomCfn4Fr7mVcc6/gqrtieq67jEtePOp/A/1bmnxCulM9czX/lH10jP7OqikjGrId/Mhw8CfTsQ6ZDv5kOvqT7NJIFtkSQgghRLViZ2dHnTp1CAoKwt7e3tbdEUKIIuXl5REbG8uVK1fIz88vc73s7GyGDh1KWloa7u7uJZaVAGsNlJ6ejoeHB4mJiaUOgFuRXq9nw4YN9O7dG6229FletzpFUUiISeTkjkPEHD9K+tWIfwOuJvXa9uf+V582b/9xIJaT078iy60Obk2b0bFLOHc2qoNjfh66I0fJOXgQ3aFDGDIyqPfbEqtzxX/8MelLfgNA7eaGY5s2OLVrh2P7cBybN0dVg17vy1mXOZp4tNgZrwXCPMNY0t/6dXptx2vsurSLUHUoT3Z8kruD7karrjmvjahkxnxIjUGVFIESco8pbcC/1Dsnodn2WalNKM4+5L98xmqf4cw69h09Q/vB49DaOxRTU4iqc7v9PhbVj4xBYWsyBkGn0xEbG0twcDCOjo6lVxCVSlEUMjIycHNzK7QuhxA3w60yBnU6HdHR0QQFBZXrsyo9PR1fX98yBVglRUANptVqa/Qv+pp+fZbqNgygbsMAYACKUSHu7BVObN9HzPGjtO15t9XrcGzXSTzyLuCadAF27uPYznns1dYm1zeE2m1ac/f9w2j14ouoVBT6AMz95x/zc2NGBtk7dpC9YwcAKgcHnFq1wrlDe1y798Cp5a2dWqCeZz3qedaz2pdnyCM6PZrI1EjzI9AtsNA4i0qPIteQy0nDSV7f9Trejt70D+nP4LDBNPFucjMvQ9wStFC7ielxvc7PQaNe1nlekyJMC23l55iLqXwaFhqHmv3fc3fMTpQZs1A1GwzNBkPQHbf8zHNx67mdfh+L6knGoLC123kMGgwGVCoVarUatfwb5KYruCW74D0QN99///tfPv74Y+677z7+/PNPW3fnpqvoGMzOziY4OJiEhAS2bNlCt27dqqiHJmq1GpVKVe7P6/KUlQCrELcYlVpFYBN/ApsMBgYXOl4r7Qq5luUx4qK/jMvly+gv72bjmh9Z6VAHt+AmjH/veewsbuUJ+OILsvcfIPvAAbIPHsSQlGQ+puTmkr1/P9n79wMqqwCrYjRiTE9H4+lZ+Rd8E9lr7Gnk1YhGXo2KLaMoCo29GpOem06SzvT6JOuSmX9qPvNPzaeRVyMGhQ7ivgb34evke7O6Lm5VDm5QN9z0sGQ0QnrctUW2nDwLVVUlRZj+n3EJ9n5verjWgWaDTMHWep1AFm4TQgghhKix0tLSmDVrFps3b+bUqVPEx8ej1WqpXbs2HTp0YNCgQTz00ENlzjl58eJFZs2axaZNmzh9+jSpqanY2dnh4+NDgwYNaNu2LV27dqV37954eHgUqh8cHExMTEyh/S4uLnh4eODr60vr1q3p0KEDDzzwAHXr1r3h16Aou3fvZvr06ezYsYOrV6/i6elJ69atGTVqFI8//nilnefChQt8+eWXAHzwwQeFjkdHRxMSElJsfWdnZ2rVqkX79u0ZNmwY999/f4nnK5ggdc8997B169Zy9XXChAl8+OGH5arz+++/F+pTce+xnZ0dHh4eNGzYkG7dujFu3DiCg4OLbdvZ2ZlXXnmFt99+m5deeol//vmnWs+ALQtJEVADFaQIKMsU5luRXq/nr7/+on///rftN8Ul0ecaiDoczbEdB4g7e4z8jBgUY0rhgmpnXln4q/lDzGhU2HDwLJ2a1cPdxQlFUciLiib74AFyDhwg+8BB9HFxANT7+SdcOnUyN5V77hznBw7CoWFDnNqH49y+Pc7t26OtXfumXLMt5OTmMHXVVK74XGHbxW3kGfOsjmtUGub2m0trv9Y26qGo0RSF/ANzSdzxE7UzT6IyFpF83cUPmg6EzuPBu8HN76Oo8eT3sbA1GYPC1mQMmm67jYqKIiQkRFIE3GQzZ87k7bffJsliUkxRmjVrxg8//ECXLl1Kbe+ll14iOzu71HM/+uijLF68uND+4oJvRdFoNAwaNIhJkyaVGIgrrwkTJvDxxx8Xu+DSfffdx9KlSytlvI4dO5aZM2dy7733smbNmkLHSwuwXq9Pnz4sX74cFxeXIo9X5wDr9ZycnJgxYwYjRowotkxGRgbBwcEkJyfz66+/8sgjj5Srf+VR0c+q8sTXZAarEDWM1kFDoztCaXRHKPAouiw9EQfPc3THPuLPn8KYHYNiTMPZt4HVN0Snr2Twz+RJHM2/Sp5rIK6hTWlzd0c63z8Er4cfBkB/+TLZBw7i1No6aJh98CBgCrTmnjtH6iLTL1ttUBDO4eE4d2iPc3g42vr1b/lvpQrYqe1orG3My11eJtuYzbrodayIXMHRhKMAeDh40MynmVWdzLxMXLQuNeY1EDakUqG0GcbeS17073EX2vMb4eQKiNgEhn/nsGclwIGf4M7nbNtXIYQQQghRqV577TW+/vprwDRz8NFHH2Xw4MHUr1+fvLw8zpw5w8KFC9m8eTMnT56kV69ezJ8/n4ceeqjI9hYtWsTYsWMBcHR05Mknn6Rv374EBgaiKAqXLl3iwIED/Pnnn/xjkVauOAEBAaxbt868rdfrSUlJISYmht27d/Pbb7+RlpbG77//zqZNm5g/fz4DBw684dflhx9+MAcRQ0NDeeedd2jZsiWXLl1iypQpbNmyhdWrV/PUU0+xcOHCGzpXXFwcc+bMAeDVV18ttfzgwYP55JNPrPalpaVx8OBBJk+eTFRUFOvXr2fcuHHMnz//hvpWmp9++okOHTqUWq5+/frFHit4j41GI5mZmWg0Gs6fP88vv/zCmjVryMnJ4amnnqJhw4Z0spicZcnNzY2xY8fy+eef88knn1RpgPVmkACrEDWco4uWFnc3psXdjQHITMnlzL6zuHlZ//hvO3QBbf4VwIBjZjT5R6I5cGQN+77Tku9eD58mzenUozPN7+uH+rrbjtUuLji2bInu5EkwXFskSh8bS1psLGl//AGAQ6NGNFi5okqv1xY8HDx4pPEjPNL4EaLSolgVuQpnrXOhha/e3PEmsRmxDAodxMAGA6ntUnNn+IqbyNEDWj9meuRmwNl1pmDruQ3gFQy+Da3L7/4Orp4wpREI7Q52skCWEEIIIcStYvr06ebgamBgIAsXLuSuu+6yyn/ZpUsXRo8ezcKFC3nyySfJzc1l+PDhhIWF0aZNG6v2DAYDr7zyCmAKeO3cuZNWrVoVOu+gQYP46KOPOHXqFMeOHSuxj1qtlhYtil6z48knn+Sbb77hgw8+YNKkSaSnp/Poo4+yfft22rdvX56XwkpycjJvvvkmAPXq1ePvv//G1/dayrYBAwYwZMgQVq1aZQ4o30jez+nTp6PX6wkICKBHjx6llvf09CzyNbnrrrt49NFHadmyJQkJCSxcuJAvvviCgICACvetNCEhIcW+P2VV8B4bjUbS09Nxd3fnjjvu4PHHH+fVV19l0qRJGAwGPv300xJz0w4dOpTPP/+cY8eOsXXr1irPxVqVJAuyELcZVy8Hwvu2pFHHplb7Q+0U1A6NQOVqtV+t6LFPiyRj70rW/+8tvh72MBH7rb+19Bg4kJDfltBo716CZs3C59lncG7fHpVFflcAbb2gQv2Jn/QNiTNnkv3PPyh5eYWO32pCPEJ4od0LjGk5xmp/Yk4iu+J2EZUWxZRDU+izrA/jNoxj9fnV5FgsZiTEDXFwg5YPwaO/wBuR8PCcwmUOL4AjC2HRo/BlGCx7Gk6vBr2MQyGEEEKI6iwmJsY8W9LFxYUNGzbQsmXLYssPHTqUn376CYDc3FyeeOIJrs8SuXfvXq5cuQLAuHHjigyuWmratOkNzzR0dXXl66+/5vPPPwcgJyeHMWPGlFKrZLNmzSItLQ2AiRMnWgVXwZSSYPr06eZ8tAW5UyvCaDSaZ68+9thjN7zAWO3atc230iuKwoEDB26oPVv7+OOPcXAwTeLYsmVLsekaAFq2bGkew7Nnz74p/asqMoNVCAFAn3tb0rvPFyRczODgthNE/vMPeQmRKPqLoGSZy6mNefjWD7Sq+8uvm3HLvcqdXe+gdudOuHa5CwBjXh66Y8fIPnCQ7AMHcO3S1aqeMS+P5LlzUXJNtzSrHB1xat3anFbAqXVr1M7OVXzlN0eKLoU2tdpw8KopnYJRMbL70m52X9qNq9aVPsF9GBw6mLa12koKAVE57F2gVhPrfZnxkBp7bTs3HY4tMT3sXaFRX9PM1rDeYF8zfvaEEEIIIWqKyZMno9PpANPq9Y0aNSI9Pb3EOsOGDWP+/PmsXbuW48eP8+eff1rdjn/hwgXz87CwsKrpeDHeeOMNfv/9d/bu3cuRI0fMOY0r4o9/75p0d3fngQceKLJMYGAgvXr1Yt26dWzatImMjAzc3NzKfa6dO3dy6dIlAB588MEK9fd6lrlac3NzSyhZ/Tk7O9OgQQNOnTpFdnY2SUlJ+Pn5FVv+wQcf5NixY/zxxx/odLpbNp+zBFiFEGYqtYpa9dzp90QneKITBoORSxGp/L3lKJeOH8OYFoXKTodnrWsfjtl5+USu2YhbzlEiVy/AqHHCMSCMZne0p02nDni3a4dzeDiMG1vofLoTJ8zBVQBFpyN7716y9+417bCzw7F5M5zbt8d7xIhbetGshl4NmXPvHGIzYlkVuYqVkSuJyzQtGpapz2T5ueUsP7ecILcglgxYgqu9ayktClEBrrXg9Qg4v8WURuD0X5Br+qafvEw4vsz00DrDE79DvTtt218hhBBCCAGYZjbOmzcPMC0gNG7cuDLXfemll1i7di0AP//8s1WA1d7irsNTp05VUm/LRqVS8eKLLzJ06FDAFCStSIA1Ly+Pffv2AdCpUyera7rePffcw7p168jNzeXAgQN079693OfbsmULYLpNPjw8vNz1i2K5cFS9evUqpU1bsnwPSlsE8M47TX9zZGZmsmPHDnr37l2lfasqEmAVQhRLo1ET1NiboMbdgG7o8wzkpFvfxv/3+SRc8y6at9WGHPJij3E49hiHl/6MonXBrV5jWt/VgaYdOuJR61qQ1KlNGxr8tZrs/QfIPniA7AMHyL90+Vrj+fnojhxFd+Qo3tetPqi/cgVUarS1a1XFpVeZILcgnmvzHM+0foZDVw+xInIF66PXk51vWq3Ty9GrUHDVqBhRqySji6gkWkdo3M/0yM+DqG1w8g9TmoCcFFMZxQi1r8vLlHbRlILA0eOmd1kIIYQQ4nZ34sQJkpOTAejatSseHh4l3nptqVevXjg5OZGTk8POnTutjrVt29b8/IcffmDQoEFlyilaWXr16mV+vmPHjgq1cfbsWQz/rgXSpEmTEstaHj916lSFAqwF/WzZsqX5VvgbER8fbw6eBwUF0a5duxtu05by8/M5d+4cAB4eHnh6epZYvmPHjubn27ZtkwCrqH70ej16vd7W3ah0BddUE6+t2lOBk4ed1Wvf0NORs80fJiv6LOqcCxjzL4JybVaqSp9FZuQhdkUe4uKpGAa/eO2bVkVRUAUG4hoUhOsDQwDQX76M7uAhcg4eJOfQIfTnz2MXGAje3lbnTZg5k7QFC9EGBeEYHo5Tu3Y4tQ/HLjDwptxiXxnjsLVPa1r7tOb1dq+zOXYzf0b9Sa96vazaVBSFoWuH0tCzIQNCBtC+dnsJtgqgsj4LVRDczfTo+yWqC7tRnVqJSjFiUDuARduaDR+gOrkCpUE3jE0GoTS6F5y8bugaxK1Nfh8LW5MxKGxNxqDp2hVFwWg0ljnYJyrmn3+urYPRtm1bjEajOZ9qwXtQHJVKRevWrfn7779JSEjg4sWL5kWU6tevz3333cfq1avR6XT07NmTDh06cO+993LHHXfQoUOHQvlMy6Ks48HHx4fAwEAuXrxIREREhcaRZZqDunXrlthG3bp1reqV93yKovD3338D0KZNmxLrWx5LSUnh6NGjVsfT09P5559/+Pbbb7l69Sr29vZMmTIFjUZTar8q0u8CkZGReHt7l1je3t6eRo0aldqHosbglClTyM42TSB68MEHS+2rh4cHISEhREVFceDAgSr5LCnoq16vN+fhLYvyfL5LgLUGmTZtGtOmTTN/c7N+/Xqca0j+yqJs2LDB1l0Q//JvAbQIQ5/TkEtxKjLiUrFPuYRd3kWM+XGAadZrjlrDX3/9Za536FQ6rsdX41DLH6+gAJxq+aN1dQO1Cjq0hw7t0WRmYpeaykmLegD1tmzBEdDHxqKPjSXj35w7+W5uZIeEkBMSQk5IMHm1a8MNJh0vSWWNQxUqBjIQzsJfZ69da0x+DGcyz3Am5Qx/Rv2Jh8qDtvZtaWvfFh+NT6WcW9zaKv+zsCeoAIufObVRz70n/0Rr1KOK2IA6YgNGNCS4NeOSZweueIaTZ1f+/FWiZpDfx8LWZAwKW7udx6CdnR116tQhMzOTvDIsWOtwaCYOh2aVWs5QqwVZg6wXvHFZORpN/PFS6+a2G0Nuu6ev7cjLxH1ez1LrAWQNnIWh9rVFo+zOb8J58zul1lO0LmSM3Fymc1RUXFyc+bmnp6dV7tWMjIxS61sG1GJiYnB1vXbX3JQpU7h8+TKHDh0CYP/+/ezfv998PCwsjO7duzNs2DBat25d7DkKAmMFK8uXlaenJxcvXiQ/P5+LFy/i7u5e5rpgmgFaQKPRlHhuy0BjcnJyufoJpkBpVpZpjRIPD48S62dmZpqfr1y5kpUrVxZbdsiQIbzwwgu0adOm1D7l5+eXu9+WeV3LsqBYUFBQoYAwFP8ex8fHExUVxaJFi5gxYwYAfn5+jB8/vkx99fHxISoqisjIyHJfW1nk5eWRk5PD9u3byc/PL3O9gkBxWUiAtQZ5/vnnef7550lPT8fDw4M+ffqU+4PpVqDX69mwYQO9e/cuNZeHsB1FUbhyKYs9u2OJPXIabWoMD4wZhr2Tk7nMnvXf4a7PRB93jvg40y0EGmcv6rVoSVi7tgQ2bYGbT+FvSxVFIfl8FDn796M7dsxqlp1dRgbuR4/i/u8vA69nxuHz/POVfn03axz+FfUXrgdcydSbfjmnKWlszd3K1tyttPZtzcAGA+ldrzdu9hLcut3c1M/CnBTU9iNQTq9ClWFK46HGQO2MY9TOOIZycS5K/btQmgzA2GyIzGy9TcjvY2FrMgaFrckYBJ1OR2xsLK6urmVamEaFHlXmldLLeQQV+ltWlZtWprqO6HGwrJsL6jLUA3Bx1IJlXXtVmeoq9m5V/re35Uw6Hx8f3N3dURTFvFBTaXfxWd6mbTQarfrr7u7O7t27mTNnDj/++KM50FogIiKCiIgIZs6cybBhw/j+++9xcXEpdA71vxNb1Gp1uV4PD49rKahUKlW5X0vLa3d3dy+xvo/PtUkqBoOh3Oe6fPlaSrs6deqUWN8yiF2adevW4e7uzqRJk0q9pd7Ozq7c/S5vKoPi3sOC9zg2NhYvr+L/zd+tWzemTp1Ks2bNynS+glnS8fHxVfKzpNPpcHJy4u677y7XIlrlCfZKgLUG02q1NfoXfU2/vpqgXrA99YK9YGirQsfiUnNwys7G9DF07RskQ3YKUfu2E7VvOwD27n40vbMTvUZbL5JV5+WXADDm5qI7epTsgwfJ3n+AnH/+wWjxLZNreLjVOMmLieHyBxNwbt8e5/btcWrdCrVF0Le8qnocDm40mL4N+rI1disrIlew+9JujIrpW8MjiUc4kniELw9+yYAGA5jQeUKV9UNUXzfls1BbC+77EvpNhLgDpgWyTq6AtFgAVIoBVfR2iN6Opv6d4H5r5UYWN0Z+HwtbkzEobO12HoMGgwGVSoVarTYHXkrk6A5uAaUWU7n4orq+PRffstV1dLeuq9aUqR6AWutgffeb1rls57R3KdzfSmYZdMrOzkatVptnExa8ByUpmHUJpmDr9eUdHBwYN24c48aN49KlS+zYsYMDBw6wd+9e/v77b3OAd8GCBVy+fJn169eXeKt1mcbDvyxn4Fr27ezZs8XOjA4MDDQHIi3v3M3Pzy/x3JaBamdn53L1EyA1NdX83Nvbu8T6lsdGjhzJnDlzrI7n5uYSExPDsmXL+PTTT5k7dy779+9n8+bN1C5lgefy9tsyCL1lyxa6detWrvrl4eHhwfPPP0+LFi1KL/yvghnWWVlZ5b62slCr1ahUqnJ/XpenrARYhRA2UcfdkZ7jRvHPvp5knIvAKT0G8mMx5l8GDOZyeekJnD5whl6jrevHnjyGT90gnD08ce7QAecOHeAZUPLz0Z06bV40y8kiaTtA9v79ZP/9N9n/5s1Bq8WpeXOc24fj1L49zu3aoalmM78d7Ry5N+Re7g25l/jseFafX83KyJVEpEYAkGvIRW+8fXN/iZtIrYagjqZHn0/g0qFrwdaUaPAKhjrXfaFybClkxkPTgeAZZIteCyGEEKK66Pwf06Mihi6uWD0HN3j1VMXqNr7X9KgGLPOgXrlSthm5lq5evWp+bjmLsygBAQE8+uijPProo4DpVvqvvvqKiRMnYjQa2bx5M4sWLWL48OHl7kdREhMTAdPMTDe3a3fm9enTh5iYmCLr/Pzzz4waNQrAqo7lbflFsQw0l2eGaQHL2Y85OTnlrm/JwcGBRo0a8fbbbxMeHk7fvn05efIkr776KvPnz7+htqtaQEAA69atw2g0kpmZSU5ODgcPHuSbb77hypUrPPLIIyxatMg8hkpT8Freyl9WSYBVCGETGrWKLnfWpcuddYE7Sc3IZfvui5w5fJm8qAicc2JR8i+i5F8mpIV1wOaPPVFETvkvKHrc/OoS2rYN9Vq0JrBZC5zc3HFq2QKnli3w+fcXriXdmbPWO/R6cg4fJufwYZg1G1QqHBo3xvWee6j17yzZ6qSWcy2ebPEko5qP4mTySVZErOCvqL8YHDrYqlxmXibPb3qee0PupV9wPzwdPW3TYVFzqVRQN9z06PUhXDkGWfGm/Zb2TDMFYte9DXXbQ7PB0GyQKRgrhBBCCCHKpFWra38TWS54VRYGg8GcT9PPz8+8wFVZeXt789lnn6EoCp9//jkAv/32W6UEWOPj47l06RIAjRs3rlAbgYGB5ucXL14ssWxsbKz5eVBQ+b/89/PzMz9PTk4ud/3i9OnTh9atW3PkyBGWLFnCDz/8UGQahupCq9XSokULcy5Wd3d3evbsyfDhw+nYsSNxcXGMHTuWTp06Ua9evVLbK3gtS0uPUJ1JgFUIUS14ujkwqG8o9A0FuhB1OYMduy6Sfj6Zzg81tyq7f8MBvBXTjM2MhDgOr4/j8PrVpnb86xHSug31WrQisGkLHK/7VrL2O2/jPXIkOf/OcM0+cJC8qKhrBRSF3NOn0RZxS0bmjp3YB9dHa/EL3FZUKhXNfZrT3Kc5r7d/HY3a+vacDTEbOBR/iEPxh/hi/xd0D+rOoNBB3FX3LrTqW/dbQVFNqVTgXzgVCGlxpuBqgbgDpseG98G/jSnQ2ux+8Am9WT0VQgghhLgltWjRAm9vb5KTk9m+fTtpaWlWMzdLsnHjRvNiPV27dq1wH55++mlzgDUiIqLC7ViyXCSuS5cuVseio6PL1EajRo3QaDQYDAZOnz5dYlnL402bNi17R/9lGWBNSUkpd/2SNGnShCNHjqDX6zl9+jTh4eGV2v7NEBAQwIwZMxg4cCDp6em8++67/PLLL6XWK3gtyxKMra4kwCqEqJZC/N0IeajwLzyjUSE1WYWfQweM+bEohqvAtZUgUy9f4J/LF/hn7UpAxbgZc3H1urZipkqlwj6wLvaBdfEYbJr1mZ+YSPbBQ+a0Armnz+DU3vqXmZKfz8UXX0TJzsauVi0cw9vhHFAXpZ+CrWk1hQOmRxKOmJ/nG/PZELOBDTEb8Hb05r4G9zE4dDCNvSv2DbEQZeZRF57bC6dWmtIIXLVY+ffyYdNj00dQuwUM+QHqlD1PkxBCCCHE7USlUjFixAgmT55MTk4OM2fO5JVXXilT3alTp5qfjyriLr+yspz5WtqiWmWhKArffvuteXvIkCEVasfe3p6OHTuyZ88e9uzZQ15eHvb29kWW3bZtG2C6Pb99+/blPpeDgwMNGzbk3LlznD17tvQK5WC5un15VrqvbgYMGECXLl3YuXMnCxcu5O233y5xsSuj0cj58+cBaN68ebHlqruqzcIshBCVTK1W8cXE+2ny/FPk3TWOxMDnMbgORuMQjkpjvbCOys7DKrgKsH/VCrYv+JmowwfJ05nyvNj5+uLetw913nmHBsuX02jv33g98ohVPd2pUyj/fuubHx9P5pq1BM6eTdyTT5F98GAVXnHFTOg8gaUDlzKi2Qh8HK/lWErWJfPLyV94aNVDPLTyIf48/6cNeyluC7WawD1vwLO7YPwh6PmBafaqpYTTpmCsJX0OKLb/AkMIIYQQorp48cUXzavBf/jhh2WaRbp48WJWrzbd7deiRQsGDBhgdVwpx7+3Dhw4YH7eoEGDMtcrzsSJE9m3bx8A7dq1o2/fvhVu6/777wdMq74vX768yDIXL15k48aNAPTs2bPMM4CvVzALeP/+/RWqXxRFUTho8XdlRdIXVCfvv/8+YAqefvrppyWWPXnypDl37h133FHlfasqEmAVQtxyXBzsuPeOIN56vgP/+/pehn89jFrDnyC17dPE134GXAahcWiHT2hHq3oHopPYtngF+1cuY/n/PuC7Jx9jwTuvsnPxPGKOHkafqwNA4+aGxsPDqq6djw++L4zHpXNnVBarVOoOHiRm2HAujHmanGPHqU4aezfm9Q6vs/HhjUzrOY0+9ftYpQc4k3KGK1nlT5AvRIX5hELXV2DcNnjxCPT+2JSXtUF3cPKyLrvmDfiuA2z6GC4flWCrEEIIIW57wcHBfPnll4BpMafevXtz7NixYssvWbKEkSNHAqZZnr/88kuhmadr1qzhkUceKTWva3JyMi+88IJ5e/DgwSWULllmZiavvfYab7/9NgDOzs7MmjWrwu0BjBkzBo9//4Z76623SEpKsjpuMBh47rnnMBhMCyq//vrrFT5XQYA1MTGRKMt0czdg+vTp5pQIbdq0KXee3OqmT58+5hnCv/76a4lfBhQE2Qvq3aokRYAQ4pYX4OnM0D5h0CcMo1HhRFwafx+8wuDO1vlbtu+NRpWfYN5WjAauRJ7hSuQZ9v6+BJVag39YY+q1bEXTLt3xDrg2o04bEIDfc8+Z6un1pKxZQ+wXX2L/74qXWTt3krVzJ+4DBxLwxcRKuWWmstip7bg78G7uDrybtNw01katZWXkSo4nHWdAA+tvsM+nnmfR6UUMDhtMc5/m1eo6RA3jFQx3vWB65OdZHzPkw6k/IScZdnxleniF/LtA1mAIaFt4MS0hhBBCiNvA+PHjiYyMZMqUKVy4cIEePXrw2GOPMXjwYOrXr2/O37lw4UI2bdoEmG5rnz9/Pm3atCnUntFo5LfffuO3336jdevW3HfffXTo0AF/f3/s7e2Jj49n586d/Pjjj8THxwMQHh5uDtwWRa/Xc/z4cavt1NRUoqOj2b17N0uXLiU1NRUADw8PFixYQNu2bW/odfH29mbixIk888wzxMTEcMcdd/Duu+/SsmVLLl26xOTJk9myZQsAjz/+ON26davwufr3749Wq0Wv17Np0ybGjBlTap3U1FSr1wQgLy+P6Oholi5dyqJFiwBQq9VMnDixxLauXLnCnDlzSj1ns2bN6NixY6H9UVFR+Pr6llrf19eXOnXqlFquOO+++y5DhgzBYDDwv//9j9mzZxdZrmCctm7dmpCQkAqfz9YkwCqEqFHUahUtgzxpGeRZ6NjVNCNa76fxzb0I+osY8y+iGK+t/KgYDVw6e5JLZ0/i5R9iFWDV5+WiUqmx02pRabW49etHdH4+XfINpMyYgf7flS+1tWtV66Ckh4MHjzZ5lEebPMqVrCvUcbH+hflH5B8sPrOYxWcWE+oRyqCwQQxoMIBazrWKaVGISmB3XY6srASo1QxidmHOsZwSBbsmmx4e9f5dIGuwaQasWm7IEUIIIcTtY/LkyTRp0oR3332X5ORk5s+fz/z584ss27RpU3744YdiF7fy8vLCxcWFrKwsjhw5wpEjR4osV6B3794sWrQIO7viw0mXLl2iZcuWJbZjZ2fHoEGDmDRpEvXr1y+xbFmNGzeOS5cu8fHHHxMZGclTTz1VqEz//v356aefbug8tWrVYtCgQSxbtoyFCxeWKcC6YsUKVqxYUWIZV1dXvv/++1JncZ45c4Ynn3yy1HO++OKLRQZYi3pdiqs/efLkMpUtyuDBg2nevDknTpzgl19+4YMPPii0iFV2drb5dRk+fHiFz1UdSIBVCHHbmDi2A1fSdGw7cYUjh66SEpVB7awsvPPiMOpjTYtmGVMBFSFtrFdE3/vHWg6snEvdxk0Jat6KgMbNUNRq3IcMxOv+waT+9hsp8xfgPXq0VT1DZibG9HS01fAWj+uDq4qisClmk3k7Mi2Sbw5+w5RDU+gU0InBoYPpHtQdRzvHm91Vcbtx94cnV0PGVTi9yrRAVvROUIym42kXYM93psfoDRBU+B+OQgghhBA12TPPPMMjjzzCrFmz2Lx5MydPniQhIQE7Oztq165Nhw4dGDx4MA8//DAajabYdu666y4SEhLYuHEjW7du5eDBg5w7d46kpCQMBgPu7u4EBwfToUMHHnvssQrN/HR2dsbDwwNfX19at25Nx44defDBB6vkNvgPP/yQvn37Mm3aNHbs2MHVq1fx9PSkdevWPPnkkzz++OOVcp6xY8eybNkytm3bxqVLlyp0LVqtFg8PD5o2bUrv3r0ZPXr0LZ8awJJKpeKdd95h2LBh6PV6Jk6cyLRp06zKrFixgqysLBwdHcsUNK7OVEp5MhqLW0J6ejoeHh6kpaXh7u5u6+5UOr1ez19//WWeli9ERRmNCicvp7P92FVOH40nLy6HQF0mft5ZjPvftW/PkrPymP7cuzjpTlnV13p40/+Z8YS16wCYApTXz15NmPodST/+iOdjj+E79mns/Pyq/sJuQGZeJutj1rMiYgWH4g8VOu6mdaNPcB+GNR1GQ6+GNuihKHDbfRZmJcLpP+HkSojaBsZ8cPOHl09az2CN3mnK11q/M6iL/2NC3LjbbgyKakfGoLA1GYOg0+mIiooiJCQER0f5Ev5mMxqNpKen4+7ujlru6LmpFEWhZcuWnDhxgk8++YR3333X1l2yiRsdg7169WLTpk2MGzeOGTNmVEEPTSr6WVWe+JrMYBVC3LbUahUt6nrQoq4H3NuInDwD+6KTaVHbejXJnWcScFJcQOUGSoZ5vz4tmRUTP6R+y7bcM2I0fvWCrerlp6SQPGeOKWfrL7+QunQp3sOH4TN6NBpPz5twheXnau/KAw0f4IGGDxCbHsvK8ytZGbGSS1mmFAgZ+gyWnVtGl7pdJMAqbi4XXwgfZXpkJ8OZNWDIK5weYMtnptQCLn7QZIApjUBwF9Dcnn/4CiGEEEKIyqdSqfjf//7HoEGDmDx5Mi+99BIuLi627tYt5e+//2bTpk3Y29vzzjvv2Lo7N0wCrDWYXq9Hr9fbuhuVruCaauK1CduyU0HnEE/AenylZ+n4x78LvlmdCcrNwC7/IobcoyiGqwDEHPuHeW+Mp9ndPej00FBcvLwBMCgKHo8/RuqCBSg5OpScHJJmziJl0WI8R4zA84nhqF1db/p1llUdpzqMbT6WMc3GcCj+EKuiVrHxwkYcNA50rt3Z6jU6nHCYixkX6VmvJ052Tjbs9e3jtv4s1LpBi0dMzy2vP/MqdjG7UYEpj+vBn+HgzyhO3iiN+mFsOggluCto7ItqVZTTbT0GRbUgY1DYmoxB07UrioLRaMRoNNq6O7edghuSC94DcXPdd999dO3alR07dvDdd9/x+uuv27pLN92NjMEJEyYA8MILLxAYGFilY9hoNKIoCnq9vsSUGdcrz+e7pAioQaZNm8a0adMwGAycPXuWhQsX4uzsbOtuCVEjGBW4lA1nUlRcSdDgnWRHk6wIDDm7wJhuLuff/X5c/K3TAGgyMvDeshWPv/9GbTCY9xucnUnudg+pnTqh2N8aAZ9cJZcEQwKBdoFW+xdkLeCU/hT22NPcvjlttW0JtgtGrZJblcTNozHmUiftHwJS91Mr7Qh2Sl6hMnkaZ654tOVMnQfIdqjeKTuEEEKI6s7Ozo46deoQFBSE/S3y71khKtOJEydYtWoVvr6+ZVrsSphkZ2czdepUAJ599tkqT2+Zl5dHbGwsV65cIT8/v8z1srOzGTp0aJlSBEiAtQYqyBGRmJhYY3Owbtiwgd69e9+2uY6E7Z2+lMpn8/ZS54qG4Kyj5Ov2oXJuwH9mfFooD2sB/ZUrpMz4gfQ//gCLQKs2JIR6f/yO6hbNm5SiS6Hv733JV6x/UQW4BHBfyH0MCBlAkFuQjXpXc8lnYSnyslBFbkR9aiWqiI2o9FnmQwoq8l88Dq61bdjBW5+MQWFrMgaFrckYNOU1jI2NJTg4WHKw2oCiKGRkZODm5lbs3yBCVKVbZQzqdDqio6MJCgoqdw5WX19fycF6u9NqtTX6F31Nvz5RvTUJ8OSxVgb8HuvIzOWu1L/QnIdGNLH65t5oNLDqmy9p1bM3IW3C0QYF4fzpJ/iNfZqE76aR/uefoCh4DByAvYODDa/mxvjZ+fHzvT/zR8QfrIteR6Y+E4BLWZeYeXwmM4/PpF2tdgwOG0zf4L64aCU3UWWSz8JiaD2h1UOmR142RG4yLZB1Zg0q/1ZovaxnYbPxQ0iJNuVsbdgb7GWclpWMQWFrMgaFrd3OY9BgMKBSqVCr1bLIkg0U3FJd8B4IcbPdKmNQrVajUqnK/XldnrISYBVCiBvQIdiLTm904UJSNvV9rQMy337xC4Z/dhKxbyeBzVrTfeRoagU3wL5+fep++QW+Y58madZsvEeMsKqXn5JC1u7duPfrd0vMalWpVLSp1YY2tdrwVse32BK7hRURK9hzeQ9GxfQL91D8IQ7FH6KVbyvCvMJs3GNx27F3hqYDTQ+9DrLirY8bjXBkEWRchhPLwc7JFGRtNhga9QUHt6LbFUIIIYQQQgig+v/lLoQQ1ZxKpSoUXL2SmoPuxD/m7Ysnj/DLmy+yZto3ZCQnAuDQsCEBEz9Hc91CV0kzZ3Hp1deIun8IGZs2cStlcnG0c6RfSD9m9J7Bhoc28HL4y4R6hALQ3Kd5oeDq/iv7OZ963hZdFbcrrSN41rPelxoD+bpr2/k5cGolLBsNX4TCoqFw5FfQpd3cvgohhBBCCCFuCTKDVQghqsDhCymc8u1P67RIyNmJYkwHFE5u38SZ3TtoP+gBOg5+EHtHJ6t6+cnJpCxYAEDu2bNcfP4/OLZsid+LL+JyV+dqndfmerWca/FUi6d4svmTnEg6QU5+jtVxRVH45O9POJ92npa+LRkUOoh+If3wcPCwUY/Fbcs7BF47B9E74eQKOLUKsk1fhGDIhTOrTQ+1Fp7eBP6tbdtfIYQQQgghRLUiM1iFEKIK3NsqgIkT7iGxZyfO+g5H43Q3qEx5Vg35eexdvpgfnxvNkY1rMVoseKXx8iLo++k4tmpl3qc7dozYMWO48MQIsg8evOnXcqNUKhUtfFvQoU4Hq/0nkk5wPs00e/VY4jE+3fsp3Zd055Wtr7Atdhv5xrKv7ijEDdNoIbQ7DJwMr52FkX9Ch6etF8JycINaza3rJZ6DrKSb2lUhhBBCCCFE9SIzWIUQoorU8XDks2FtOdsrjCnLvHE43oR6mQcw5B4GjORmpbNx5jRqhzSmTmgIYApGunTuTHCnTmRu2ULClG/JPXMGgOwDB4gZNhyXrl3xe+EFnFq2sN3FVYJA10De6PAGKyJWcCbFdI16o54NMRvYELMBH0cf7mtwH4NCB9HYu7GNeytuK2oNhHQ1PfpNhNh9ppmt9s6gue6fTmvehPNbIbiLKWdr04HgWssm3RZCCCGEEELYhgRYhRCiijWq7ca05+5kd2QiM5Z6Uj+qFd6ZuzDqz6HxaG0OrlpSqVS49eiBa7dupK9ZQ+LU78iLjgYga8cOdCdOELZ1C2p7+5t8NZXH09GTJ5o9wRPNnuBM8hlWRK5g9fnVJOuSAUjSJTHv5DyWnFnCtke34ax1tnGPxW1JrYH6nUyP62UnQ9Q2UAym/0dtg9WvQv3O0Ox+U7DV3f+md1kIIYQQQghxc0mKACGEuEk6h/oy5/WutH+mE/saDETv+Sj3PTfaqky+Po/Nc2abF8JSqdV43HcfDf5chf+nn6INCADA5+mnb+ng6vUaezfmjQ5vsPHhjUztMZXe9XtjpzZ9B9izfs9CwdWTSSfRG/S26KoQ1xgN0Hk8eFl+SaJAzC5Y8zpMagKz+8CeaaZgrBBCCCGEEKJGkhmsQghxE6nVKga3qcu9LeoQk5BFQ393q+PffvYTysk/ObL+T8LvG8KdDzyEvZMzKjs7PB98APeBA0j7/Q88Bg+yqqe/epXE6d/jO26sOQh7K9KqtXQL6ka3oG6k6lJZE72GZj7NrMpk67N5cu2T2Gvs6RfSj8Fhg2nm3eyWWgBM1BCuftBrAvT8AK4eN6UROPEHJJ27ViZ2r+nRdCA4e9uqp0IIIYQQQogqJAFWIYSwAQc7DY2uC65GXcnAcHonasBo0LN/5RKObFhD16FP0KpnX9QaDWp7e7wefaRQe4nff0/qr7+Stnw5no89hu/Yp7Hz87tJV1M1PB09ebzJ44X2b7qwiez8bLLzs1l0ehGLTi8izDOMQaGDGNBgAH7Ot/Z1i1uQSgV1Wpoe3d+FhNOmYOvJFRB/EuqGg2c96zr7ZkJuOjQdDL5htum3EEIIIYQQolJIigAhhKgmdkUkEuHzCGqHdhR8POflZLBp9nRmjX+WyIP7UBSlUD1jdjYZ69YDoOj1pPzyCxF9+hL/9dcYUlNv4hXcHMHuwfQL6YeDxsG8LyI1gkkHJ9FraS+e3fgsa6PWkmvItWEvxW1LpYJaTaHbW/DcHnh+P9z7uXUZRYE938Gmj+C7cPj+Ltj2BSScsU2fhRBCCCGEEDdEZrAKIUQ1MbxLCOGhPkxeXgvX4y0JzNiDUX8WgIykS/zxxUfUDm1O76fHUjsk1FxP7exM6Jq/SJr9E8nz56Pk5KDk5JA0cxYpixbj/eQovEeOROPqaqtLq1Qt/Vryhd8XZORlsC56HSsjV/JP/D8AGBUjO+N2sjNuJ818mvHrgF9t3Ftx2/NrVHhf4llIib62ffW46bHlU/BrAs0Gmx61mpkCtkIIIYQQQohqTWawCiFENdLU350fnr+TIa9052CLwaR5PIxKc20V8quRJ1jw9qtkpqRa1dN4elLr1VcIW78OryeeQKXVAmDMzCRx6ndE9upN0uzZGHNrzqxON3s3Hmr0EPP6zWP1kNWMazUOf5drr1W3oG6F6iTmJN7EHgpRDL/G8OIR6P0xBHawPpZwGrZNhO87w3ftIfFc0W0IIYQQQgghqg0JsAohRDXUpaEv8964m7bP9GRvg8fIde2PSu0BgMYnHFcvzyLr2fn5UefddwhdtxbPhx8GjQYAQ2oqyb/Mv1ndv+nqudfjP23/w9oH1zK7z2wGhQ5iYIOBVmVi0mPo+VtPxqwfw6rIVWTrs23UWyEAr2C46wUYsxFePmFKI1CvE2AxYzXjauHcrTmpphQDQgghhBBCiGpDUgQIIUQ1pVarGNIukH4t/Zm3K5gtq0Nol3SCB14cZlUuT6fj5PattOrZG/W/AVVtQAD+H3+Ez5jRJHw3jfQ//8T32WdROzhY1VUUBVUNugVZrVLT0b8jHf07Fjq2ImIFRsXI3st72Xt5L852zvQJ7sOg0EGE1w5HrZLvHIWNeATCnc+aHumX4fSfpgWyPALBzvpnlhXPw+Uj19II1G0Pahm7QgghhBBC2JL8i1wIIao5R62Gsd1CmfFhL+79cAxBjfytjn/3yQ9smv0dPz4/jnP7/rZaCMu+fn3qfvkFDVauwPOBIVb18i5cIGrQINJWr0YxGm/KtdhSbefaBLkFmbez87P5I+IPnlr3FP2X92f64enEZsTasIdCAO7+0PFpGPUnDJ5ufSw3EyI2QlqsaZGs2b3hm+aw5k2I3gVGg236LIQQQghxmxoxYgQqlYr//Oc/tu7KLeXixYs4ODhgb2/P2bNnbd2dSiEBViGEuEV4OGtpGeRpte945BWMEdsAyEq5wsqvP2Hem69z5bx13kaHhg3NeVkLJHz3HbnnIrj06mtEDXmAjM2brYKzNc2jTR5l9ZDVzOs3jwcbPoir9tqiX3GZcXx/5Hv6L+/PpIOTbNhLISxcPzM1OxGCu4Da4gakjEuwdwbM6Q9fN4E/X4Go7WDIv7l9FUIIIcRNl5aWxsyZM7nvvvsIDg7G2dkZDw8PGjVqxLBhw/j1118xGMr+BezFixeZMGECXbt2xc/PD61Wi5OTE4GBgdx99928+OKLLF26lLS0tCLrBwcHo1KpCj1cXV2pW7curVu3ZsSIEUydOpW4uLjKehnMIiIiWLRoES+//DJ33XUXzs7O5j7MmTOn0s+3b98+5s+fj729PW+99Vah4/9n777Do6i6AA7/drObXoAECIEQCE16L0oT6V1BgQ9RQKVIUUHsUkUUBZESiiAghIA0AZEqvXcpUkJLCAQIIaS3ze58fyxZE9Ibm3Le58nD7MzcO2dmL5PN2Tv37t+/P9XrkfS6VK1albfeeou9e/emeyw/Pz9TuUGDBmU51kGDBqUbS2o///zzT4p6nt3HwsKC4sWLY21tTenSpWndujXffvstQUFB6cZTrlw5Bg8ejE6nY9y4cVk+n/xIhggoxHQ6HTqdztxh5LrEcyqM5yYKjvzSDndcCuZBsVdxCz+Mog8EINj/Kqu+GINH3Zd4ZfAgHFxKpiin6HTo7j8wvY67do27I0ZiVbsWzqM/wKZZ00I1dEBStYrXolbjWnxc/2P2393Pn7f/5MSDExgUYy/eqk5Vk72v+qe9Ai3UFmaJNy35pQ2K58i+LPRdAzGhqK7vQH1lC6rb+1Hp443bo4Lg9K9w+ld0w4+Bc5U8DUfaoDA3aYPC3KQNGs9dURQMBgOGIvBEVH6yePFivvrqKx4/fpxsfUxMDOHh4Vy/fh0fHx9q1KjBggULaNGiRYb1jR07lujo5PMUJCQkcO/ePe7du8ehQ4eYM2cOffr0YfXq1ZmONSoqiqioKAIDA7lw4QIrV65kzJgxdO/enZkzZ1KhQoVM15WWAwcO8Morr6S5PS/a6FdffYWiKAwePBg3N7cU9Wd0vKioKK5fv87169fx9vbmrbfe4tdff8XCIuXfHUnrSvw/lxXZ6UiTlWum0+kICgoiKCiIgwcP8tNPP7Fq1So6dOiQZpnPPvuMpUuX8ueff3L8+HGaNEk5zFtuMRgMKIqCTqdL9fqmJSv3d5VSmLsrFTFeXl54eXmh1+vx9fXFx8cHW1tbc4clhMhDigJXw1Qc9bWg/kN/nKKOoBiSfKOsssCxcm1c6tVFrbVMUdj2+nVcdu7C+u7dZJuiPSsS3LEjsbnwYacgCDeE80/8P1zRXeEd+3fQqv7r7XtNd43N0Zupb1mf+pb1cbFwMWOkQiSn0UfjGvYPbqGnKBV+AQtFR7h1OfZVn5ZsP9ewsxiw4JFDTRS1fL8uhBAid2g0GlxdXXF3d8fS0jLjAiJXjB8/nnnz5gHG96BXr1507twZd3d34uPjuXHjBuvXr+fgwYMAWFlZsWjRInr27JlqfevXr2fIkCEAWFtb079/f9q2bYubmxuKonD//n3++ecfdu7cyYULF3jttddYunRpinrq1KlDQEAAZcqUYf369ab1CQkJhIaGEhAQwMmTJ9m0aRPh4eEAODg4sGjRIjp37pyja3L48GG6dzdOcqtWq6latSp2dnacOXMGMOZL+vfvn6NjJHXmzBnatWtnWvb09Ew3pnfeeYd3333XtE1RFEJDQzl58iQLFizg0aNHAHz88cd8/fXXKeq6c+cOdevWBeB///sf8+fPT7FPekaMGGFKim/YsAFXV9cMy3h6emJtbZ1sXfHixQGoX7++qQ0CREZGcuvWLZYuXcqpU6cAsLOz48iRI3h4eKR5jHfffZeNGzfSqVOnLCXtsyo+Pp6AgAAePHhAQkLmn/SKjo6mf//+hIWF4ejomO6+kmAthMLDw3FyciI4ODjDBlAQ6XQ6du/eTfv27dE+88izEM9LfmuHeoPCpn/usfHP69S7dx6rmBOgxJq2a22KM8RrIZpUPvgqikLUvv2EzJtH/PXkQwvYtmhBqanfoHF2zvNzyK8+PfQpfwf8bXpd27k23T2708GjA46W5rvH5rc2KPKBuAhUN/8GlRqlevI/oDS/tED16CqKlSNK1c4YXuiG4tkGNNZpVJYxaYPC3KQNCnOTNgixsbEEBARQoUKFFIkYkTcWLFhgGu+zXLly+Pj48NJLL6X69JmPjw/vvvsu8fHxWFlZcfToUerVq5dsH71eT/ny5Xnw4AEODg4cPHiQOnXqpHn8K1eucPHiRfr06ZNim6enJ/7+/nh4eHDr1q0064iMjGTSpEnMmjULABsbG/bv30+jRo0ycwlSdf36dbZs2UKjRo1o2LAh9vb2LF++3JTU/PXXX7P1aH1aBgwYwOrVq2natClHjx5NdZ/9+/fTtm1bACZMmMDEiRNT3e/y5cs0btyY2NhYHBwcCAoKSvGFhZ+fH5UqVQKM474uW7YsS/EOHjyYFStWAHDz5s1s9xpO7P3ZunVr9j4dYi4iIgIHBwdUKhWKotC3b182bNgAGBO7c+fOTbO+P//8k1dffRW1Ws21a9dSTVTnhtjYWPz8/HB3d8/SvSo8PBwXF5dMJVilC0MhptVqC/Uv+sJ+fqJgyC/tUAv0a1qRVxuUZ9lhTw5urUaD4DOoY/8B9KiL1cLGzi7N8pYdO1CsfTvCt28neM5c4v39AdAF3MHa2TnF+K1FhaIoqNQqLFQW6BXjUAEXH1/k4uOLzDgzgzbl29CzUk9edHsRjZl6BeaXNijyAW0JqJvyjx0e+cKjqwCo4sJRXfwd9cXfwdIeqnaCGj2hcjuwzN5TL9IGhblJGxTmVpTboF6vR6VSoVarUT87drjIdf7+/qbxKu3s7Ni9ezeurq6m9+BZAwYMQKVSMWDAAOLi4hg4cCAXLlxIlow9fvw4Dx4Yhw4bNmxYigTss2rWrEnNmjUzjDW99uDo6MhPP/1E6dKl+fzzz4mJiWHo0KGpjvmZWdWqVeOTTz5JM4bcbKNhYWFs3LgRgDfffDPNepOuT+s9AqhVqxZdu3Zlw4YNRERE4OvrmyLJndm60pL0Pc+ta6FWq01DCCSNafr06aYE6549e9I9VpcuXXB2dubx48f89ttvfPPNNzmOK61YVSpVlu/XWdlX7oBCCFGIWGsteL9NZeZP6UTsa29wqdSbYFWbPh8PSbZfTEQkgdevJVunUqtx6toVz7+2UubbqWjcylBy1OgUydWEkJA8P4/8QqVS8dPLP/H3G38zrtE4qhavatoWb4hnp99ORuwZQfv17dl3Z58ZIxUiHcXKQz8fqNMXrJJ88x4fCZfWw9q34MdKsHYghOX+hBNCCCGEyB0///wzsbHGp9QmTJhA1apVMyhhTAB26tQJgEuXLrF169Zk2+/cuWNarly5ci5Gm7FPP/2Upk2bAnD+/Hm2bdv2XI+fXZs3byYuLg6A3r1750qdFStWNC0n1l1QeXp6Yve0c09AQEC6+2q1WtMwCmvWrMnz2PKSJFiFEKIQKm5nyfhXazF5UjfaTfmUUu4lkm1fOHUBq7/+mN+nTCUs6GGybSqNhmK9e1Npxw4cuyQfCyn2mi83Wr/M/fET0N2/n+fnkV+42LgwsOZANvTYwLru6xhQfQAlrP+7psExwZSxL2PGCIVIh9YaXugKvX6BT25A/7VQ702wdvpvH100+O5Mvg6MAz0LIYQQwuwURTE94m1jY8OwYcMyXfajjz4yLT/7aHnSR9GvXLmSsyCzSKVS8eGHH5peb9q06bkeP7v27TN2rHB3d8fNzS1X6vR/+gQhQPny5XOlTnNRqVRoNMan+zLTA7RZs2YA3Lhxg+vPDFlXkEiCVQghCrHyzrbUrVA82bojZ6+j9zsMwN1/j7Pkg6Fs91pIXHRUsv3Ulpaonplh8dHcOSg6HaHr1nGzQ0ceTJtGQnBw3p5EPvNCiRf4rMln/P3G38xpM4d25dtR07kmL5R4Idl+q66s4sO9H7Lnzh50+qI7u7DIZzRWULUjvDofxt2AARugwdtgUwIqtwUr++T7bxkFPv3gn9UQE2qWkIUQQggB//77LyFPnyRr2bIlTk5OGZT4T7t27bCxsQGMEy8lVb9+fdPyokWL2Lt3by5Em3mJE0UBHDp06LkeO7sS42zcuHGu1Hf16lVTz+JmzZpRunTpXKnXXO7fv09YmHHi5cyM9dqkSRPT8oEDB/IqrDwnY7AKIUQR89fFUKzsWmAf/XQiLEXP5YNbuXp0L0169qVZr55YaFL+elAUBevq1Yk+dhxDVBSKTseTFSsJXbeeEgMG4PzuO1gUK/b8T8hMtGotbcq3oU35NugN+mTbFEVhw/UNXH9ynb0BeyluVZwunl3oUakH1UtUT3USAiGeO42lcezVyu2g6yyIeZJ8uy4W/t0M8RHgux3UWvB82Thm6wtdQetglrCFEEIUXL/9+xsrLq/IcL8aJWowt23yiXFG7xnN5ZDLGZZ9u8bbDKw50PQ6ShdFj009MhXfnFfmUNP5v/FFDwQcYMrxKRmWs9XY8udrf2bqGNl1/vx503KDBg2yVNbCwoK6dety/PhxHj16RGBgoKnnZcWKFenWrRtbt24lNjaWtm3b0rhxYzp37kyzZs1o3LgxLi4uuXouSZUsWZJy5cpx9+5dbty4kWfHyS1BQUHcvHkTSJ6czky5S5cumV4rikJoaCjHjh1j1qxZxMTE4OTkZJr4Ky/5+voSGRmZ7j52dnbJhi3Iiu+++860/Prrr2e4f+3atdFqteh0Os6cOcN7772XreOamyRYhRCiiPnmrUasr16aDX9Up37gKSyfToRlSIjm+IZlnN3xJ23efoearVsmSwSqVCpKjhxJ8f79CVm6lJCV3iixsSgxMTxevJgnq1dT4p3BlHh7IBb2aU+oVRhZqJP39H0c+5jQ2FDT6ydxT1h1ZRWrrqyicrHKvFr5Vbp6dsXFJu8+rAqRJRYasC+ZfF3ILbC0MyZYAQw6uLHb+PPnh1hUaElZpQYYOmCcak8IIYRIX5QuiqDooAz3c7VzTbEuJC4kU2WjdMmfylIUJVPlgBRPHcXqYzNV1k6b9599g5M8NebqmvL6ZCRpr8jHjx8ne7R92bJldOnShVOnTgFw6tQp0zJA1apV6dChA4MHD85ycjcznJ2duXv3LgkJCYSHh2c4W7s53b1717RcqlSpTJdbsGABCxYsSHWbWq1m+PDhjBkzJlPj6uZUx44dM9yndevW7N+/P9N1RkREcPPmTebOnctvv/0GQJUqVRg5cmSGZTUaDSVKlODhw4fcunUr08fMbyTBKoQQRYyFWkXfJuXpUa8sSw9V49jW2tQNPoY63jjpVXxUMDsX/MCpP7cxaMZ3KXpbaooXp9THH1Pi7bcJ/mUxoWvWoOh0GCIjCZ4zlycrVuKx2gerbH7jWRi42Liw6/VdHAs8xpabW9h7Zy/xhngAboTeYMbpGcw6M4uX3F7iiyZf4O7obuaIhUhF6Row9grcPQmXNxt/wp9OgqXoUd/eTyP2o8z/E97eDM6VzBuvEEKIfM9Oa0cp24yTUiWsSqS6LjNln012qlSqTJUD0Fok/8LQ2sI6U2VtNbaZqj8nIiIiTMv29vbp7Jm6pGXCw8OTbXNxceHIkSMsX76chQsXcvbs2WTbfX198fX1Zd68eQwYMICFCxeaJjHKDUlji4iIyNcJ1kePHpmWixcvns6emWcwGFizZg3W1tZ8//33WFlZ5Uq9eenAgQNpPpWnUqno2bMn8+fPz/Q1SkywPnjwIDfDfK4kwSqEEEWUjaUFI9tWoV/T8szd9QJ395yheuhRSDAmUOK1buk+yq4pWRLXr77EefAgghcsIHTjH6DXoyldGksPj+d1GvmWRq2hZbmWtCzXkvD4cHbc3sGWm1s4/8j4eJde0XPi/gkcrfLvB0ghUKuhfDPjT4dvIfAsXN5kTLaGPp11WKWG4hXMGaUQQogCYmDNgcke38+KZ4cMyCw7rR173tiTrbKt3Vuzxz17ZXObg8N/Q/Nk9Hh3apKWSS2BqdVqGTJkCEOGDCEwMJBDhw5x+vRpTpw4wfHjx9HpjL17vb29CQwMZNeuXVg8M19DdiVNHieNzdfXl/j4+FTLlCtXjmJmGJ4scRxcyFqCdeLEiUyaNCnZupiYGG7cuMHKlSuZNWsWP//8M6dPn2bnzp3Y2uZd0v727duZGhs1u9zc3Pjoo4+yNAFY4rWMiorKYM/8Sya5EkKIIs7Z3opJvWrx9eTX8W/7HnecuoLWg37jBifbLzo8nJjIiBTltW5ulPnmGypt+wvH7t0p+eGHqNTJf71EHj6CkpCQp+eRnzlaOtKnWh+8u3jz56t/MqT2EFztXHnZ/WWcrJJPULDo/CKWXFzCg6iC++2tKKTUaijXCDpMhQ8vkPDmHzxwrIuh6Qh4ZpgMji+EoKvmiVMIIYQohJKOg5qdXn4PHz40LTs7O6e7r5ubG3379uXHH3/k4MGDPHjwgC+++AL108/4e/fuZfXq1VmOIS2Jwx9oNJpkieQOHTpQu3btVH82bdqUa8fPCmtra9NyTExMjuqysbGhdu3a/PDDD8yfPx8wTkI2bdq0HNX7PDRq1IiLFy9y/vx5Dh8+zI4dOxg/fjxOTk7cu3ePTp06ZWnSssRrqdUW3GGnpAerEEIIACq42DHnncaca1cZVYKCk0vyCWx+mTwHw/3zNOj0Oi37904xEZalhwdlf/whRb0x588T8N57WFaogMvoUTh27pwiAVuUVHCqwAcNPmBU/VFExCdPWMcmxLL83+VE6iKZc3YOzco0o0flHrQt3xYbjY2ZIhYiFSoVSoWWnKj0MV0adiZZevXRNdjxmXG5Sgd46QOo0AJkcjchhBAi2+rUqWNaPnfuXJbK6vV6Lly4ABgnlcpKz0IwPr49bdo0FEXh+++/B2DdunUMGDAgS/WkJigoiMDAQACqVauW4/ryWsmS/41Zn7Q3a069++67fP7554SEhLB06VKmTp2aa3XnBTs7O2rVqoXBYDCNm9uxY0f69OnDSy+9REREBG+++SaXLl3K1JAPidfSHL2Sc0vR/QtXCCFEquqXL049z+TjXu06eJ6EuydR9DGc+WslXu++y7kd+1AUJcP6Hs2eA0C8nx+BH4/j9mu9iNi7N1NlCzO1Sp2i9+rZoLNE6oyPbykoHLt/jC8OfUGbtW2YeHQiZx6eKfLXTeRDzyZOT/7y3/L1XfBbN/jlZbi0AfRFtye7EEIIkRO1atWiRAnjZ/SDBw8SFhaW6bJ///030dHRALRs2TLbMQwZMsS0fOPGjWzXk9Tu3btNyy1atEi2zc/PD0VRUv0ZNGhQrhw/q5ImWJ88eZJr9arVaqpUqQLA/fv3efz4ca7V/TzVqlXL1AM3ICCAH3/8MVPlEq9l+fLl8yy2vCYJViGEEBn688xDEiz/m9FSF/uYvctmsvD90dw6dyndsi4j3se2USPT67hr17g7YiR+/foRdfSoJAyTeMntJbb12saIuiMoZ1/OtD5KF8XG6xsZtGMQXTZ2YcE/C4hNiDVjpEKko90k6DgNHP9rw9z/B9a/A3PrG4cPiMv62HFCCCFEUaZSqXj77bcB4+PUixcvznTZuXP/G782J4nJpD1f05urIbMURWHOnDmm16+99lqO68xrnp6epvFRfX19c7XuhCRDqiUU4OHVhg0bRsWnEx7PmjXLNAREWh4+fGiaeK1mzZp5Hl9ekQSrEEKIDE0f+Qq2A4dyvnRfDJr/kibRT/z44/vPWf7JeB7duZdqWdtGjSi/cgXuS5ZgXbu2aX3s+Qvceedd7rw9kOhnZiotytwd3Hm/3vts67WN5Z2W81rl15LNhns38i6bbmzC0sLSjFEKkQ4rB3hxJHz4D/RaAq7//b8n9I5x+IBZNeHMb2YLUQghhCiIPvzwQ9MM85MnT85UL9I1a9bw119/Acbehd26dUu2PSudHU6fPm1a9vT0zHS5tEyfPp2TJ08C0KBBAzp27JjjOvOaVqulWbNmAJw6dSrX6o2Ojuby5cuAcWzWpGPuFjRarZbPP/8cME5aNWvWrHT3T3odmzZtmqex5SVJsAohhMiQraWGD9pVZea3fQh/bQTXS3QF9X+zZj6+c44Vn4xgy0+pf5OuUqmwb9GcCmt/p5zXPKyq/tcbNvrUKfz7v0l0FseSKuxUKhUNSzdkSvMp7Ouzj+9afseLZV5EhYrulbqjVsmvcJHPWWihzhsw7BC8vRkqt/tvW2wo2BQzV2RCCCFEgVShQgXTI9eRkZG0b9+eixcvprn/2rVrGThwIACWlpasXLkyRc/T7du306dPnwzHdQ0JCeGDDz4wve7Zs2d2T4PIyEjGjRvHF198AYCtrS1LlizJdn3PW+IwCxcuXCAuLi5X6pw0aZJpoqeOHTtiYWGRQYn8bdCgQZQtWxYALy+vdIe0SEyyW1tb06pVq+cSX16QSa6EEEJkWkkHK755vQ63Wnvy05b6WB07jFvkCVBiAD0hEemXV6lUOLRti32bNoRv307wnLnE+/tjXbcONvXqPY9TKJBsNDZ08+xGN89uPIh6gFadfHbNkNgQphybwkcNPqKCUwXzBClEWlQq8HzZ+PPwXzg6F+6dhReS96Dh/gWIiwCPl2RCLCGEECINo0eP5ubNm8yePZs7d+7wyiuv0K9fP3r27ImHhwc6nY6rV6/i4+PDnj17ALCyssLb25t6qXzeNhgMrFu3jnXr1lG3bl26du1K48aNKVOmDJaWlgQFBXH48GF++eUXgoKCAGjYsKEpcZsanU7HpUuXkr0ODQ3Fz8+Po0ePsn79ekJDQwFwcnJi1apV1K9fP8fXZv369URG/jcM0eHDh1NdBnB1daVTp07ZOk7Pnj2ZPHky8fHxHDp0iHbt2mVYJigoKNk1AYiNjeX69eusWLGCHTt2AMYk45QpU9Kt68aNGyxfvjzDYzZp0oQaNWqkWO/r65vsOqWlTJkyODs7Z7hfaiwtLRk3bhxjxowhLCyMOXPmMH78+FT3TWynHTt2xMamAE/sq4hCJywsTAGUsLAwc4eSJ+Lj45VNmzYp8fHx5g5FFGHSDo1O+z1W3vpxt/LVoAnKjP6DlMjQqGTb42JiFIPBkGZ5g06nPFm/Xok6fTrFtkcLFylxfn65HnNhkbQNfn7wc6XW8lpKgxUNlEXnFynxCUW7XYrnI0f3QV1cynXeryvKREdF+aWNolzaqCj6hJwHKQo1+V0szE3aoKLExMQoly9fVmJiYswdSpGzYMECpUSJEgqQ7k/16tWVgwcPplnP4cOHFTs7uwzrSfxp3769EhwcnGpdHh4ema5Ho9EovXr1Uvxy8fN+Vo7funXrHB2rYcOGCqAMHjw4zX327duX6XgApWTJksrOnTtTrev27dtZqgtQZs2aZSo/cODAHJVP9Oz10+v1ypMnTxS9Xp9i36ioKMXFxUUBFGdnZyUiIiLV81KpVAqgrFu3Lv2LngPZvVdlJb8mzxcKIYTItoYeJfjt47a0HPc+7Sb/iJ2TrWmboigs+GwaXkNGcuVo6o8cqTQaivXujW3DhsnWRx0/zqNZs7jZpSv3x09Ad/9+np5HQRYRH8E/Qf8AEG+IZ+65ufTZ2ofzj86bNzAh0qN5ZgzhoKtwfZdx+d4ZWDcI5tSHE79AfNRzD08IIYTI74YPH87169eZPn06HTt2xN3dHWtra+zt7alUqRL9+vVj9erVXLx40fRIe2qaN2/Oo0eP2LJlC2PHjqV169a4ublhZWWFRqOhRIkSNGjQgGHDhrFv3z527dqV5V6Ntra2lClThtq1azNgwADmzJmDv78/GzZswMPDI6eXwiyGDh0KwMaNG7M9TIClpSWurq60bduWmTNncu3aNTp06JCbYZqVra0tY8aMAeDx48csWLAgxT6rV69GURTc3NxyNOxEfqBSFJm+ubAJDw/HycmJsLAwHB0dzR1OrtPpdGzbto0uXbqg1WozLiBEHpB2mLFN205w87dvTK+dXOvQZeQw3Kpm/CEqYNhwIg8cML1WabUU+18/XIYORVOAB3zPTUnbYIIqgQX/LGDF5RXoFT0AKlT0e6EfHzb4MNkkWULklly9D+p18O8fcGQOPHxmLDmb4tD4PWgyFOxL5ew4olCR38XC3KQNGh9xvn37NhUrVsTa2trc4RQ5BoOB8PBwHB0dUaul/9zzFBkZiaenJ48ePcLb25s333zT3CGZRU7aoMFgoHr16vj6+vLdd9+ZJsbKC9m9V2Ulvyb/A4UQQuSJHcdvoSSZCCvswQVWjx+Nz4QZhAWHplvWbeYMXEaNQm1nTAwqOh1PVqzkRvsOBM38CX1o+uWLGhuNDWMbjWV119VUL1EdAAWF1VdX03NTT/bd2WfmCIXIgIUW6vSB4YfgrU1Q6ZX/tsU8gYM/wqxasOUDSMidySSEEEIIIbLL3t7eNKbotGnTMBgMZo6o4Pn999/x9fXFxcWFUaNGmTucHJMEqxBCiDwxZdxrhLw2Fj+nl0GVOFi5gfvX9rNk1Hv8+fNy4mNTT5RY2NtTctRIKv29G+f33kX19FtGJSaGx4sXc6Ndex7Nn48+Uh4dTqq6c3V8uvowrtE4bDTGa/4w+iEf7PuAKcfSHyxfiHxBpYJKbeCtP2D4EajTD9RP52TVx8Gja6CxMm+MQgghhBAYh2nw9PTk8uXLrF+/3tzhFCiKovDtt98CMHnyZOzt7c0cUc5JglUIIUSeKOVozbQ+9flw6jButvmAILtGgIVxoxKL77H1eL37LkfW/51mHZrixSk1bhyVd++i+IABqJ4+fmeIjCR4zlxin5mJU4BGrWFgzYFs7LGR5m7NTevrlapnvqCEyA7XWtBrEXx4AV4aDZYOxn+TUhS4/jcY9OaJUQghhBBFllarZfny5UycOJGEhARzh1Og3L9/n9dff51vv/2WYcOGmTucXKExdwBCCCEKt8qlHJg/rCUnO9Rk3u/H8byyF4fYqwAYEkIJuP0wwzo0JUvi+vVXOL8zmOAFCwjd+Ae2TRpj16xpXodfYJVzKMeCdgv46/ZfHLp7iO6e3ZNtVxQFlUplpuiEyAKnstBhKrT6xJhkTer2AVjVG4pXhBdHQr03wdI29XqEEEIIIXJZy5Yt051ETKTOzc2NSZMmmTuMXCU9WIUQQjwXTSqW4LfPOtNk3MecrfwmcdpyoClFr1F9ku2X3vhFWjc3ynzzDZX+2orr118n26YoCnfHjCF0w0YU+QYZAJVKRTfPbkxvNT1FMnXC0QksOL8AnV5npuiEyCJrJ3h28oQjc4z/PrkN28bBrJqwbxpEPnr+8QkhhBBCiCJLerAKIYR4blQqFV1ql6Fd9b6sOt6U+sW1WNr8N+uuwaAw78PxONo60m30O7iUK5lqPZYVKqRYF7lvPxHbdxCxfQePlyyh5OhROHTqhEpmVE3h0N1DbLqxCYCdt3cy6aVJMoSAKJheGgWKAW49ncgtJgQOTIcjs6Hu/+DFUeBS2bwxCiGEEEKIQk8SrIWYTqdDpyt8PZMSz6kwnpsoOKQd5owKGNDUHUh+DddsOIAu6DyPgd/GncS9Tkc6Du2HrZNN6hUlEXH0iGk5/vZt7o39GMuFi3AeNRLbl18udI/D56QN3gi5gYXKAr2i52bYTd7e/javV3md0fVGY68t+APMi+cjX9wHy7c0/jy4iMWJ+agu/4HKkAAJsXBmGcqZ5ShVO6Nv/QWUqm6+OEWeyBdtUBRp0gaN564oCgaDQWZRNwNFUUz/yvUX5lBQ2qDBYEBRFHQ6HRYWFpkul5X7u0pJvBqiwPPy8sLLywu9Xo+vry8+Pj7Y2so4ZEKIgsGgwI7dN6gafBBIMmGN2hGnSs1wrl8etSb9JKnNrVs479qF7W2/ZOtj3N153LED0ZUrG2cpF9xPuM+mmE3c098zrXNUOdLNphs1LGuYMTIhss86/jGVHu3CI3gfWkOsaf3BqhN5YlfJjJEJIUThpNFocHV1xd3dHUtLS3OHI4QQqYqPjycgIIAHDx5kaUKy6Oho+vfvT1hYGI6OjunuKwnWQig8PBwnJyeCg4MzbAAFkU6nY/fu3bRv3x6tVptxASHygLTDvHH9YSRz/jiFy+ldFH86EVYiCys3Gvd8k8bdXkSlTjtJqigKMceO8XjOXOL+/TfZNutGjSj5+WdYVauWJ/E/T7nRBhMMCazxXcP88/OJ1f+XjHrF/RU+a/gZJW1TH6JBCMjn98HYcNTnfkN96heUYh7o396afPvjG+DoBlr5Irogy9dtUBQJ0gYhNjaWgIAAKlSogLW1tbnDKXIURSEiIgIHB4dC97SWKBgKShuMjY3Fz88Pd3f3LN2rwsPDcXFxyVSCVYYIKMS0Wm2h/kVf2M9PFAzSDnNXjXLFWTi6A0dvNuAXn/1UuvY3trq7AOjjAjm+9kfO7XiBdu+8xwsvvpBmPZatW+PYqhWRe/bwaPYc4q5fByD29GksDIZC9Z7lpA1q0TK49mA6VOzAN8e/4cg94zALewP2curBKdZ0W0N5x/K5Ga4ohPLlfVDrDK3GwkujUEUFoU4an6LAH+9BxH1oPASaDAE7F/PFKnIsX7ZBUaQU5Tao1+tRqVSo1WrUMu79c5f4SHbieyDE81ZQ2qBarUalUmX5fp2VffPv2QshhCiyXqrkwtKvelNn3HgueLyKzqKEaVtc+FV8z13JsA6VSoVDu3ZU3LwJtxkzsPTwwL5tW2zq1Em2nyE+PtfjL2jK2pdlQdsFTG85nRLWxmtdp2Qd3B3czRyZEDmksQSncsnX3dwLDy9B9GM48D3Mqglbx8Djm+aJUQghhBBCFHjSg1UIIUS+pFar6FmvLJ1qDcb76MscX7uFqsHHUWuK0+W9HpmuR6VW49StK46dOqKPiEi2TTEY8OvTF6uqVSg5ciSWHh65fRoFhkqlootnF15ye4mfz/7Me7XfS/GYT4IhAY1aPjqIAs6pHNR6Hf79AxS9cUKs00vh9DJ4oSu89AGUb2ruKIUQQgghRAEiPViFEELka1YaC95tVZkZP47G0P8L2o75GI3lfzM/JugNeH00mc0//U5MZNq9UVUaDZrixZOtC9+2nbirVwnf8ic3u3Tl/vgJ6O7fz7NzKQiKWRdj0kuTKOeQvNffobuH6L2lN+eCzpkpMiFySclq8Pqv8OE/0GwEaO2eblDg6lZY2gF+7QBXt5kzSiGEEEIIUYBIglUIIUSB4GSj5bMe9anXsGqy9b+t3E7s/VPcOLGShcNHsm/lXhJ0+kzVqcTHY+HkZHyh1xO6bh03O3biwbRpJAQH5/YpFFjRumimHp/KrbBbvL39bb459g0R8REZFxQiPytWHjp9B2P/hbYTwd71v20BJ+DiOvPFJoQQQgghChRJsAohhCiwYnV6rh49Znpt0N3n7NafmD/0E87sOI9iUNItX6zXa1T6ezcuo0ahtjP2YlPi43myYiU32ncg6KdZ6MPC8vQcCoLQuFDT2KwAa33X8uqmV9njv8eMUQmRS2yKQ8ux8NEF6OkFJZ9OoPfS6OT7JcRD1OPnH58QQgghhMj3JMEqhBCiwLLWWtBj3Idcrvo6cUkmwtJF+7J/2XgWjPyGG6f9063DwsGBkqNGUunv3Ti/9y4qa2sAlJgYHv/yCzfatSd85648PY/8zs3eDe8u3nzW+DNsNDYABMUE8dH+j/ho30cERQeZOUIhcoHGCuoPgPePwTu7oGyD5NsvrTdOiPXXOAi5ZZ4YhRBCCCFEviQJViGEEAVay6olWTx5INU/nsoVt/bo1bZPtxiICTnJ5hljWfrJHB7eTr/nmaZ4cUqNG0fl3bsoPmAAKq3WWEtkJFaeFfP4LPI/C7UFA2oMYFPPTbQs29K0fs+dPfTc1JO119ZiUAxmjFCIXKJWp5zkSlHg6FxIiIFTi2FOA/j9Lbh72jwxCiGEEEKIfEUSrEIIIQo8tVrFaw3LM++HURR7dzK3ijVB4els90ocT+7s4tLhs5mqS1OyJK5ff0WlnTtwer03Tj17YlWlSrJ94m7fRolPe0KtwszN3g2vtl782OpH07ABkbpIvjn+DT+f/dm8wQmRVxJioWLr5BNiXdkCS9rC0s7GCbEM8gWDEEIIIURRJQlWIYQQhYa11oKh7arz7azPiX/9M+7b1UABsKpIm/5ts1SX1s0Nt6lTKfPdtGTrFZ2OgGHDudmpM6EbNqIkJOTeCRQQKpWKThU7seXVLbxW+TUAbDQ29K3W18yRCZFHtDbQ+funE2JNAPvS/227cxTW/A+8msCZ5aCLNVuYQgghhHi+3n77bVQqFaNGjTJ3KAXK3bt3sbKywtLSEl9fX3OHkyskwSqEEKLQKWZryZdvvMhHMycR0m4U7UaNQG3x36+8WF0Ciz+bweF1J0nQ6dOtS6VSJXsd+scf6O7cQRcYyP2vvuJW9x6Eb9uGUgR7rzlZOTGl+RR+7fArXzf7mrL2ZZNtj9JFmSkyIfKITXFo+TF8dBF6zAOXav9te3wdto6FKBmTWAghxPMRFhbG4sWL6dq1KxUqVMDW1hYnJyeqVq3Km2++ye+//45en/5n3aTu3r3LpEmTaNmyJSVLlkSr1WJjY0O5cuVo1aoVH374IevXrycsjUlgK1SogEqlSvFjb29P2bJlqVu3Lm+//TZz587l3r17uXUZANDpdOzYsYMxY8bw0ksv4eLiglarpVixYjRo0IBPPvmEW7dydwz1kydP4u3tjaWlJZ9//nmK7fv370/1eiS9LlWrVuWtt95i79696R7Lz8/PVG7QoEFZjnXQoEHpxpLazz///JOinmf3sbCwoHjx4lhbW1O6dGlat27Nt99+S1BQ+p+HypUrx+DBg9HpdIwbNy7L55MfSYJVCCFEoVWuuC3ThnSibpPqydb/+ssfhPvt58T6qSx4fzIX9/miGJRM1WldsyZ2LVqYXsffvs29sR9zu1dvIvbuQ1EyV09h0qRME3pU6pFsXbQumt5bejPl2BTC48PNFJkQeURjBQ3eghHHof9a8Hh6T6jVC4qVT75vfPTzj08IIUSht3jxYqpUqcKnn37Kjh078Pf3JyYmhvDwcK5fv46Pjw/9+vWjTp06HD58OFP1VatWjcmTJ3P48GGCg4NJSEggNjaWe/fucejQIebMmcMbb7zBsGHDshRrVFQUgYGBXLhwgZUrV/LBBx/g4eFBr1698PPzy+YV+M+jR48oU6YMnTt35ueff+bYsWM8fvyYhIQEwsLCOHfuHDNmzKB69erMnj07x8dL9PXXX6MoCu+88w7lypXLcvmoqCiuX7+Ot7c3bdu2ZeDAgVlKiOcnOp2OoKAgDh48yNdff0316tXZtSv9iYK/+OILtFotf/75JydPnnxOkeYdjbkDEEIIIZ6nsBgdj07swQEAA/ERZ9m18BJHN7agw5D/UbFOmXTL29SsSfkli4k+dYqg2bOJOX0GgLirV7k7YgTWdetQ6qOPsHvxxTw/l/xs7rm53Iu8xzrfdewP2M+XTb+knUc7c4clRO5Sq6FqR+PPvTNg5ZR8e0IczG0I7k2g+QdQtqF54hRCCFGojBs3jpkzZwKg0Wjo27cvPXv2xMPDg/j4eK5du4aPjw979+7l8uXLtGvXDm9vb15//fVU61u9ejVDhw4FwNramsGDB9OxY0fKlSuHoigEBgZy+vRptm7dyrlz5zKMz83NjZ07d5pe63Q6njx5gr+/P0ePHmXdunWEhYXxxx9/sGfPHry9venevXu2r0dcXByPHxsntK1Xrx49e/akadOmlC5dmrCwMLZv387cuXOJjY3lo48+wsbGxnS+2XXy5El2794NwMcff5zh/u+//z4jRowwvVYUhZCQEI4dO8asWbMICgpixYoVuLu7M3Xq1BzFlpGdO3fi5uaW4X6VK1dOc1ujRo1YtmwZBoOByMhIFEXh1q1bzJ8/n+PHjxMSEkKvXr24ePEiFSumPmGwh4cHvXv3Zs2aNUydOpUtW7Zk+5zyA0mwCiGEKFLsrTTUeH8c+1etoeKj06hJAOKJDNrLxu9OUbJiRzoNfY1SFZzSrce2cWM8Vq4k6shRHv38M7GXLgEQe/4Cdwa/Q8kxY3AZlrMPbgWZu4M7NhobYhJieBTziDH7x/CK+yt82fRLStuVzrgCIQqa1JKnF9ZCRCBc3mT88WgOL30AVToYk7NCCCFEFs2fP9+UXC1Xrhw+Pj40b94cdZLfKy1atODdd9/Fx8eHwYMHExcXx4ABA6hcuTL16tVLVp9er2fs2LEAODg4cPjwYerUqZPiuD169GDKlClcuXKFixcvphujVqulVq1aqW4bPHgws2bNYuLEifz000+Eh4fTt29fDh48SKNGjbJyKUxUKhXt27dnypQpNGvWLMX2Nm3a0Lt3b9q0aUNMTAyffvop//vf/3BwcMjW8QBTT9imTZumm4hMVKpUqVSvSevWrenRowcNGzYkNjaWOXPmMGHCBCwtLbMdW0aqVq1KhQoVclSHnZ0dtWrVwmAwEB4ejqOjI82bN2fAgAH06dOH9evXExUVxcyZM5k3b16a9fTv3581a9bw119/cevWLTw9PXMUlznJJzshhBBFioVaRZ8XK/PTrC+we3sCdx1qovB0nFVDBI9ursf7y7Fs/HEL4cEx6dalUqmwb9GcCuvWUm7eXKyqVDFu0Ghw7NI5j88kf+tfvT+be26mVblWpnV7A/bSc3NP1lxdg0EpemPWiiJIpQK7Uv+99j8Cq/vC/GZwdoVMiCWEECJL/P39Tb0l7ezs2L17N7Vr105z//79+7N06VLA2MvzrbfeSjGc1YkTJ3jw4AEAw4YNSzW5mlT16tXp06dPTk4De3t7Zs6cyffffw9ATEwM7733XrbrK1u2LLt27Uo1uZqoadOmph6kYWFhpt6n2REWFsaGDRsAePPNN7NdT6IaNWrQtWtXACIiIrh69WqO6zQXlUplel8B/v7773T379SpE87OzhgMBpYtW5bX4eUpSbAKIYQokqy1Fozo2oAJs78htuuHPLb+b9xERf+Q26d/4drJy5mqS6VS4dCuHRU3b8JtxgxKjhyBpbt7sn0ijxwh7saNXD2H/K6MfRnmvTKPH1v/iLO1M2Cc+OrbE9/y9va3ufGkaF0PUQTVH/B0Qqy54FL1v/XB12DLaPi5NhycAdEh5otRCCFEgfHzzz8TG2v8cm7ChAlUrVo1gxLGBGCnTp0AuHTpElu3bk22/c6dO6blzPTEzE2ffvopTZs2BeD8+fNs27YtT4/Xpk0b0/LNmzezXc/mzZuJi4sDoHfv3jmOC0j2GH1i3QWVp6cndnZ2AAQEBKS7r1arNQ0PsWbNmjyPLS9JglUIIUSRVtzOkq/fbsfwn2YQ1OxtojTGRKDKtiqNOjXIUl0qtRqnbl1xef/9ZOsNcXHc//IrbvXoSeBnnxGfwQeNwkSlUtGpQic2v7qZ3lX++wB6/tF53t7xNtE6mQBIFHJaa2jwNow4Af/73ThMQKKoINj7DRz52WzhCSGEKBgURWHFihUA2NjYZGmiqY8++si0/GwvwaSPol+5ciVnQWaRSqXiww8/NL3etGlTnh4vaeLSwsIi2/Xs27cPAHd390yNZZoZ/v7+puXy5cuns2f+p1Kp0GiMI5JqtdoM90/seXzjxg2uX7+ep7HlJUmwCiGEEEB5Z1umj+lDz+9m8bBWDzqNHIZKrTJtj4yJZ+WUpVw66I/BoKRTU0qh69eT8PAhGAyEbd7Czc5duD9hIrqnj2MVBU5WTkx6aRJLOy7Fw9EDgOF1hmOrtTVzZEI8J2o1VOsEg7fBe3uhxqugUoNaA02eGa/ZIENoCCGESO7ff/8lJMT4xEPLli1xckp/voCk2rVrh42NDQCHDx9Otq1+/fqm5UWLFrF3795ciDbz2rX7bxLUQ4cO5emxDhw4YFquXr16tutJjLNx48Y5jgng6tWrpp7FzZo1o3Tpgj1fwf379wkLCwPI1FivTZo0MS0nfY8KGpnkSgghhEiibvkS1B2fcnKqxfN+x/DvRnZe3s2xP16h/Tuv4lHLBZVKlUotyRXr1QslJobHi5egDwuDhARC164lbNMmiv+vH85Dh6Jxds6L08l3Grs2ZkOPDWzw3UDfan2TbYvWRZOgJOBo6Wim6IR4Tso1hD6/QchtuHMcnMol335oJtzaD80/gMrtZUIsIYQQnD9/3rTcoEHWnrKysLCgbt26HD9+nEePHhEYGGjqeVmxYkW6devG1q1biY2NpW3btjRu3JjOnTvTrFkzGjdujIuLS66eS1IlS5akXLly3L17lxt5OJzW/fv3Tb13S5YsmWy4gKwICgoyDS+QNDmdmXKXnk6KC8YeyaGhoRw7doxZs2YRExODk5MTs2bNylZcWeHr60tkZGS6+9jZ2SUbtiArvvvuO9Py66+/nuH+tWvXRqvVotPpOHPmTI7G4zUnSbAKIYQQGXgYFk3sue1YAigRhAduZuN3xyhVpSsd3mlPKY/0E4JqGxuc33uPYn37ErL8N0KWL8cQFYUSH0/Ibyt4sm49Jd56C+d3BmORhd4IBZWVhRX9q/dPsX7OuTns9NvJF02+oL1H+0wlr4Uo0EpUNP4kpYuFk4sg6hH4H4aSL8CLo6BOH9BYmSdOIYTIJY+XLSdk+fIM97OuUQP3BfOTrQt4fwSxlzMeH7/EoEE4Dx5keq2PjOLW0wmEMlLOywubWjVNryP27ePBpMkZllPb2lJpe96OHxocHGxadnV1zXL5pL0iHz9+nOzR9mXLltGlSxdOnToFwKlTp0zLYJx1vkOHDgwePDjLyd3McHZ25u7duyQkJJhmpM9NiqIwbNgwIiIiABg/fjzW1tbZquvu3bum5VKlSqWzZ3ILFixgwYIFqW5Tq9UMHz6cMWPGZGpc3Zzq2LFjhvu0bt2a/fv3Z7rOiIgIbt68ydy5c/ntt98AqFKlCiNHjsywrEajoUSJEjx8+JBbt25l+pj5jSRYhRBCiAw42lhh3/t97v75O6Vi/ABQ9EE8vLqMVV8dwLPRq7QZ8CKOLjbp1mPh4EDJ0aMoPuBNQn79lRDvVSixsSjR0TxetAhDTDSuX375HM4o/7nw6AI+V3xQUPj4wMe87P4yXzX9Cle7rP8BIUSBFnoHrJ2MCVaAR1dhyyjjWK1Nh0Gjd8CmuHljFEKIbDJERhqHTcqAPpUEoj4kJFNlDSl65imZKgeg6OKTv46Ly1RZ9dMJffJSYnIQwN7ePsvlk5YJDw9Pts3FxYUjR46wfPlyFi5cyNmzZ5Nt9/X1xdfXl3nz5jFgwAAWLlxomsQoNySNLSIiItcTrNOmTePPP/8EjBNdZSbpl5ZHjx6ZlosXz53fxwaDgTVr1mBtbc3333+PlVX+/0L1wIEDaXaGUKlU9OzZk/nz52f6GiUmWB8U4CHUJMEqhBBCZMDG0oLRvVvyuGMT5q/ahv7QZhx0xl4EBt0tbhybxe2ze6jTrhcv9aqDtX36g7lrihen1LhxFH/7bR7/spjQ338HtRrndwvm4zC5oZRtKVqXa83+u/sB2B+wn1MPTvFB/Q/oW60vFursT0QgRIFSsiqMPAW+2+HIHAg4blwf+RD2TIGDM42TZr04AooV7EkwhBBFj9reHk0mxpe0KFEi1XWZKatOkXxUZaocgEprmfy1lVXmjmmb92PKOzg4mJYzerw7NUnLpJbA1Gq1DBkyhCFDhhAYGMihQ4c4ffo0J06c4Pjx4+h0OgC8vb0JDAxk165dOZooKqmkyeOksfn6+hIfH59aEcqVK0exYsUyrHvVqlWMHz8eMA6H4OPjgzoHQ+8kjoMLWUuwTpw4kUmTJiVbFxMTw40bN1i5ciWzZs3i559/5vTp0+zcuRPbPGxTt2/fztTYqNnl5ubGRx99lKUJwBKvZVRUVF6FleckwSqEEEJkkrO9FeOHvcbt19qxcMlanC7uwsoQBSjo485zbttV3KrO4IVmmRuvSFuqFK5ff4Xz4EHEXLyItnTyx4xCN2xAiY+nWO/eqCwt06ilcHC1c2XOK3PY5b+L7058x+PYx0Tpovju5Hf8dfsvJr04iSrFq5g7TCGeD7UaXuhq/Ak4CUfnwpU/AQV0UXBiAVz4HT6+BprCfW8QQhQuzoOTP76fFc8OGZBZFvZ2VDmwP1tlHdq0wSGbY3XmtqTjoGanl9/DJD1xnTMY+9/NzY2+ffvSt69xvPyQkBBmzJjB9OnTMRgM7N27l9WrVzNgwIAsx5GaxOEPNBpNskRyhw4d8Pf3T7XMsmXLGDRoULr1/vXXXwwePBhFUXB1dWX37t3ZGl4hqaRDC8TExOSoLhsbG2rXrs0PP/xAlSpVGDp0KIcPH2batGlMnTo1R3XntUaNGrFs2TIMBgMRERFERkZy5MgR5syZw7179+jUqRO7d++mZcuWmaov8Vpqtel3VMnPZMR8IYQQIosqlnJg+pfv0uGb2dyr0Bq9yvhBQO1QlWpNK2S5Pm3Zsjh26pRsnT4yiqAZM3kweQo3O3chdOMfKAkJuRF+vqVSqehYoSObX91M7yq9TesvPLpAnz/7MOfsHOL0cWaMUAgzcG8CfVfC6DPQ6F3QPP3DrsFbklwVQogipE6dOqblc+fOZamsXq/nwoULgHGCp6z0LATj49vTpk3j008/Na1bt25dlupIS1BQEIGBgQBUq1YtV+oE2L9/P6+//jo6nY7ixYuzc+dOKlWqlON6S5YsaVpO2ps1p959911KPO25vXTp0lyrN6/Y2dlRq1YtatWqRc2aNWnfvj1Tpkzh8OHDODg4EBcXx5tvvpliOIq0JF7LzPRKzq8kwSqEEEJkU6PKrsz8fhy1xk7ncZkG9PhwaLKxiEIiYtgw6y/8LgajKEqW6o46eAD9kycA6O7d4/6XX3Krew/Ct29HMRhy9TzyGycrJya9NImlHZdSwbECAAlKAosvLmbrza3mDU4Ic3GuBN1+gjH/wstfQNPhybdHPYZf2sC5VZCQ+uOUQgghCq5atWqZEnAHDx4kLCws02X//vtvoqOjATLdozA1Q4YMMS3fuHEj2/UktXv3btNyixYtkm3z8/NDUZRUf9LrvXry5Em6d+9ObGws9vb2bN++PVmCOieSJlifPP2snhvUajVVqhif1rp//z6PHz/Otbqfp1q1ajFt2jQAAgIC+PHHHzNVLvFali9fcIc/kgSrEEIIkQMqlYouTaoy7ecpVKqVfGiAX2etwO/4Av6YPoU1U3fy0C9z3+ACOHbpQoV1a7Fr3ty0Lv72be6NGcvtXr2J2r8fspi0LWgauzZmfY/1DK0zFI1KQ03nmrxa+VVzhyWEedm5wMufg+MzvY9OLYbAs7B5BMyuA4dnQUyoWUIUQgiR+1QqFW+//TZgfJx68eLFmS47d+5c03JGj9WnJ2nP17QmOMoKRVGYM2eO6fVrr72W4zovXLhAp06diIyMxNramj///JOmTZvmuN5Enp6epvFRfX19c61egIQkT6slFOAn14YNG0bFisa/i2bNmmUaAiItDx8+NPV0rVmzZp7Hl1ckwSqEEELkgdv3H2O48jdgnAgr8JIXq8d/z59zjxL2KDpTddjUrk35X5fgsXIFNg0bmtbHXb3K/dEf4LZ8OfokkwIURlYWVoyuP5q13dcytfnUFJNdXQ25muXewUIUSvcv/LcccR/+ngSzasKOLyE0wGxhCSGEyD0ffvihaYb5yZMnZ6oX6Zo1a/jrr78AY+/Cbt26Jduelc9Rp0+fNi17enpmulxapk+fzsmTJwFo0KABHTt2zFF9vr6+dOjQgSdPnqDVatmwYQMvv/xyjuNMSqvV0qxZMwBOnTqVa/VGR0dz+fJlwDg2a9IxdwsarVbL559/DhgnrZo1a1a6+ye9jrmZDH/eJMEqhBBC5AFHezvim3Qj1sLu6RoFffwFfA//yPJPZrPP+yIxEZl7jNe2cWM8vFfivngx1rVqmdbbX71G8PfT8yD6/KdK8SpULl452brzj87T588+jN47mgdRWZ/sQYhC5X8+8M4ueKEb8LRXUXwkHPeC2XVhw3tw/7xZQxRCCJEzFSpUMD1yHRkZSfv27bl48WKa+69du5aBAwcCYGlpycqVK1P0PN2+fTt9+vTJcFzXkJAQPvjgA9Prnj17Zvc0iIyMZNy4cXzxxRcA2NrasmTJkmzXB3Dnzh3atWvHw4cPsbCwwMfHhy5duuSozrQkDrNw4cIF4uJyZ36ASZMmmSZ66tixIxYWFhmUyN8GDRpE2bJlAfDy8kp3SIvEJLu1tTWtWrV6LvHlBY25AxBCCCEKI2cHa8aPGYjvvW4sX/QbTr6H0Cg6QEdCzBHObj3PpQMtaPZqF+q180Brlf6HKJVKhX3LFti1aE7E7t3cHz+BOJUK5zFjns8J5TMJhgQmHZ2EgsKBuwc4tekUHzT4gH7V+qXo5SpEkVG+KZRfBcE3jInVf3wgIRYUPVxcZ/x5YznUzPkjmEIIIcxj9OjR3Lx5k9mzZ3Pnzh1eeeUV+vXrR8+ePfHw8ECn03H16lV8fHzYs2cPAFZWVnh7e1OvXr0U9RkMBtatW8e6deuoW7cuXbt2pXHjxpQpUwZLS0uCgoI4fPgwv/zyC0FBQQA0bNjQlLhNjU6n49KlS8leh4aG4ufnx9GjR1m/fj2hoaEAODk5sWrVKurXr5/ta/L48WPatWtHQIDxiY2PP/6YF154IVkMzypevLgpAZhVPXv2ZPLkycTHx3Po0CHatWuXYZmgoKAU8cTGxnL9+nVWrFjBjh07AGOSccqUKenWdePGDZYvX57hMZs0aUKNGjVSrPf19SUyMjLD8mXKlMHZ2TnD/VJjaWnJuHHjGDNmDGFhYcyZM4fx48enum9iO+3YsSM2NjbZOl5+IAlWIYQQIg9VLevMtCljOXbpNf5YspRS9/9BhQJKJPHhOzi06hyunt/jXr1kxpVhTLQ6duiApnJlDuzaTXWX7H3oKegsVBaMqDeCaSemERwTTHRCNN+f/J5tt7Yx8aWJVC1e1dwhCmE+LpWh2yx4+Us4tQRO/gIxIWDtBJXbmzs6IYQQOfTzzz/zwgsv8NVXXxESEoK3tzfe3t6p7lu9enUWLVqU5uRWxYsXx87OjqioKM6fP8/58+k/7dC+fXtWr16NRpN2OikwMJDatWunW49Go6FHjx789NNPeHh4pLtvRi5evMj169dNr3/44Qd++OGHdMsMHDgwU0nK1NSvX5+GDRty5swZfHx8MpVgXbBgAQsWLEh3n5IlS+Lt7Z3htTty5AhHjhzJ8JizZs1KNcGa2aEYZs2axUcffZSpfVMzdOhQvv32W4KDg5k9ezZjxozB3t4+2T5+fn4cO3YMgAEDBmT7WPmBDBEghBBCPAcv1qrI9FlTqPL+ZB45/jdmlYWTe6aTq0lp3d2JL+OabJ0+NJRH8+ejFOBB8TNLpVLR3qM9m1/dzOtVXzetvxB8gb5/9mXO2TnE6XPnkS0hCiz7ktDmCxjzL3SdaUy4WiX/w4Z90+DIbIjN/GzUQgghzG/48OFcv36d6dOn07FjR9zd3bG2tsbe3p5KlSrRr18/Vq9ezcWLF9NMrgI0b96cR48esWXLFsaOHUvr1q1xc3PDysoKjUZDiRIlaNCgAcOGDWPfvn3s2rUry70abW1tKVOmDLVr12bAgAHMmTMHf39/NmzYkOPkqrkMHToUgI0bN2Z7mABLS0tcXV1p27YtM2fO5Nq1a3To0CE3wzQrW1tbxjx92u7x48epJphXr16Noii4ubnlaNiJ/EClyMwQhU54eDhOTk6EhYXh6Oho7nBynU6nY9u2bXTp0gWtVmvucEQRJe1Q5IROb2Dlxj3c2fkH73z2NeWr/Dcj6+OwKE5v+Je6barhWtEp7TqeaYOKTsedoUOJPnYcu5deouysn7BwSrt8YXP6wWkmH5uMX7ifaZ2HowcTX5xIY9fG5gusEJP7YCEQ+cg4EZY+DiwdoOFAaPY+OJUzd2SZIm1QmJu0QeMjzrdv36ZixYpYW1ubO5wix2AwEB4ejqOjI2q19J97niIjI/H09OTRo0d4e3vz5ptvmjsks8hJGzQYDFSvXh1fX1++++4708RYeSG796qs5Nfkf2A+ERkZyaRJk+jWrRuurq6oVCoGDRpk7rCEEELkAa2FmnfeaM/ExV7JkqsAv0z/lUs7v+P3SV78Nf8MoQ+jM1VnzMWLRJ8+A0DU0aP49e1H3O3buR57ftXItREbemxgeN3haNTGR9b8w/2ZdWZWlmbHFaJIubUf9E8n24uPgGPzjBNibRwKD9KeNEUIIYQo6uzt7U1jik6bNg2DwWDmiAqe33//HV9fX1xcXBg1apS5w8kxSbDmE8HBwUyePJmzZ8/SqFEjc4cjhBDiOXh2FtfLt++juXUA0JEQe5SrB3/gty+WsN/nCtHh8enWZdugAR5Lf8WieHEA4v388Ovbj6ijR/Mq/HzH0sKSkfVGsq7bOuqVrIeFyoKJL05McZ2FEE/VeQNGnYKGg8HCyrjOkAAXfoeFLWDFq3BjD8iXFEIIIUQKw4cPx9PTk8uXL7N+/Xpzh1OgKIrCt99+C8DkyZNTjM1aEEmCNZ8oU6YMd+/eJTAwUP5jCiFEEWVpYUGYex0UniYElSgSonZxdut0ln3iw6m/bqOL06dZ3rZxYyqsW4tVlcoAGMLDuTNkKCE+Ps8j/HyjcvHK/Nb5N37r/BvVSlRLts33iS/3I++bKTIh8iGXKtD9Z+M4ra0+BZvi/227tQ+8e8GaovnYoxBCCJEerVbL8uXLmThxIglFYA6E3HT//n1ef/11vv32W4YNG2bucHJF2tO+iefKysqKsmXLmjsMIYQQZlS5fCmm/jiR/ScusXv5EkqE3ABA0QcTG7qewz6nObvzFVq80ZTKjVOfGMuyXDk8Vq8mcNwnRO7fD3o9D6d8Q9z167h++SWqIjJGnFqlpm7JusnW6fQ6Pjv4Gfci7/FB/Q/43wv/w0JtYaYIhchn7EvCK19Bi4/gHx/jcAFP/IzbPF82Y2BCCCFE/tWyZct0JxETqXNzc2PSpEnmDiNXSQ9WIYQQIp95uWktps6fRfkB4wizKW1ab0jwI/LhMnYvXkBIYFSa5S3s7SnnNY8S775jWhe6eg13hg7FEBOTp7HnZ6uurOJG6A1iEmKYfmo6b21/i2sh18wdlhD5i6UdNBkCo89CnxVQqS3Uf6YH66NrsOtrCLtnnhiFEEIIIfKZQplgjY6OZvv27UydOpVevXrh4eGBSqVCpVJlOkMeERHBpEmTqF27Nvb29jg5OdG4cWNmzpxJfHz64+AJIYQQOaVSqXij+8t89csi7DsOJFrj8HSLgkUxO1zc0x+nSGVhQelPPqHMtGmmXqvaUqVRFeEZfntX7U2fqn1Mry8GX6Tf1n7MPjub2IRYM0YmRD6ktoAaPeGtjcaka1LH5sHRuTC7DmwcBg8umSdGIYQQQoh8olAOEXDy5Em6dOmS7fL+/v68/PLL+Pn5AWBra0tcXBynT5/m9OnTrFq1ij179lC8ePEUZRVFIS4uLlPHUavVWFpaZjtOIYQQhZ+VpYZh77zBk9e7sPSXFcRdOMKAj99LsZ+iKKlO5lSs12tYVvDg8a9LcZ0yuUhP+ORg6cD4F8fT1bMrk45N4nbYbRKUBJZcXMIuv11MeHECTcs0NXeYQuRv8VHw72bjsiEBLqwx/lRqCy+NNg4nUITvM0IIIYQomgplD1aA4sWL07ZtWz755BNWr16Nq6trpsolJCTQvXt3/Pz8KFOmDLt37yYqKoro6GjWrFmDg4MD586dY8CAAamW9/f3x8bGJlM/DRo0yM1TFkIIUYgVd7Tj43Hv8+nS5ZT2SD7+qs+vu1g/8xhx0bpUy9o2aIC71zzUVlbJ1usCA1GK4OzgDUo3YH339YyoOwKN2vhd852IO7y36z3GHxlPWFyYmSMUIh+ztIPRp6HVJ8knxLq5B1a+CotawoW1oE/9fiSEEEIIURgVyh6sLVu2JCQkJNm6zz//PFNlf/vtNy5evAjAhg0bePHFFwFjb9O+fftiMBjo378/27ZtY8+ePbRt2zZZeRcXF5YtW5apY6XWA1YIIYRIj0aT/Ff3n1tPcn/XQlRqO7wnhdB73CsUK2WbYT1xt27j17cvjp064Tr+a1RF7IkKSwtL3q/3Ph0rdGTSsUmcCzoHwNabWxlQfQBOVk5mjlCIfMy+FLzyNbQYA+dWGYcMCPU3bntwETYOgb8nw6A/oYSneWMVQgghhHgOCmWC1cIi+zMC//bbbwC0adPGlFxNql+/fnz11Vfcvn2bFStWpEiw2tvbM2jQoGwfXwghhMisBIPCxc3LcCAexRBP6J3lrJoYyqsfdaVstbS/xDPExXF3xAgMERGErltHvJ8fZefMRlMEv/jzLObJ8k7LWe+7nllnZtHvhX5UK1HN3GEJUTBY2kHTodD4XbiyBY7MgcCzxm0WWijmYd74hBBCCCGek0I7REB2REdHc+TIEQA6d+6c6j4qlYpOnToBsGvXrucWmxBCCPEsjVpFr08/I8ra2bhCiSU2ZC0bpntz+UhgmuXUVla4jBxh6rUafeoUfn36EnfjxvMIO99Rq9T0qdaHza9uZlidYcm26fQ6/rj+B3qD3kzRCVEAqC2g5mswZC8M2gZVOxvHY1U/0+nh2Hx4eNk8MQohhBBC5KFC2YM1u65cuYLBYACgVq1aae6XuO3BgweEhIRQokSJXDn+vHnzCA0NJSEhAYALFy4wdepUAFq1akWrVq1y5ThCCCEKj+pVKlB21mzmfT0R+8c3AQO6qJ3s+uUJj+/1pfnrVVGrU04449S9O5bu7gSMGo0+OBhdQAB+fftR9qeZ2Ldu/fxPJB8oZVsqxbpfL/2K1z9e/H7tdya/NFl6twqRHpUKKjQ3/jw7vnPQVdj5hXG5cntjArZiK5kQSwghhBCFgiRYkwgM/K+3T9myZdPcL+m2wMDAXEuwzpgxA39/f9Prc+fOce6ccUy4iRMnpplgjYuLIy4uzvQ6PDwcAJ1Oh05X+CYYSDynwnhuouCQdijMLWkbdHSw45MZ3zN72kwsrx8HQB97ktNbnhB0tx+dhtTB0jrlr3xNzZqUW+3Dgw8+IO7KVQxRUQS8PwLnsWMp9vZbqIp44uNJ7BOWXFwCwL+P/6Xv1r68Vf0thtYairXG2szRmZ/cB0VWqI8vxNSf9cZuuLEbxbUO+mYjUar3BHXW/yyRNijMTdqg8dwVRcFgMJg6K4nnJ3Gy0sT3QIjnraC0QYPBgKIo6HS6LA0rmpX7u0opItMHV6hQAX9/fyZOnMikSZNS3cfHx4c333wTgOvXr1O5cuVU99u9ezcdOnQA4OjRo6mO1fo8TZo0icmTJ6dY7+Pjg61txhOdCCGEKDzOnf4Xe99jqDD+eldZlMa6TDfcWqrT7Cimio/H9fe1OFy6ZFoX1qgRD197FTRF+7vYOwl32BS9iSBDkGldCXUJetr0pJK2khkjE6JgsdDH4vH4IJUe7cA2PjjZtmhLF26W7Ii/c2v0FvLlhRAFiUajwdXVFXd3dyyL2ISZQoiCIz4+noCAAB48eGB6ajwzoqOj6d+/P2FhYTg6Oqa7b9H+q6mQ+OKLLxg7dqzpdXh4OO7u7nTo0CHDBlAQ6XQ6du/eTfv27dFqteYORxRR0g6FuaXVBrt06cL2Hfu4smoBGkWHog/Cvb41nbq2S7c+pUcPQuYv4MmiRQA4nTlDjREjsGncKE/PoyB4R/8Oyy8v59d/f0Vn0BFiCGFZ1DK6V+zOmAZjKGZVzNwhmoXcB0XW9QJDAglX/0R9bB7qB+cBsI0Ppva9VdR6vBV928ko9QZkqjZpg8LcpA1CbGwsAQEB2NvbY20tX5A8b4qiEBERgYODQ5F/8kiYR0Fpg7GxsdjY2NCqVass3asSnxDPDEmwJuHg4GBajo6OTnO/pNuSljEXKysrrKysUqzXarWF+hd9YT8/UTBIOxTmllob7NG9A+XLu/PXjG9wq/UK3QekPnHjs1zHfIRttaoEfvElJUePwvEl8z6hkV9otVpGNhhJ50qdmXx0MmeDjLOk/3n7T47cP8LnTT6nc8XMXePCSO6DImu0ULcP1HkD/A7D0Tlw3ThxrCo2DI19Schie5I2KMytKLdBvV6PSqVCrVajVssc2s9b4iPZie+BEM9bQWmDarUalUqV5ft1VvaVBGsSbm5upuV79+5Rp06dVPe7d+9eqmWEEEKI/KJe3epUnv8LdvZ2ydbH6xLwOx9MlQalUaUy+ZVjly5Y16qF1t39eYVaYHg6ebKs0zI2XN/ArNOziNBFEBIbwvUn14t0glWIbFGpoGJL40/QFTg6DwLPQrVn/i8F/gNx4VChpUyIJYQQQoh8K/+ml82gevXqpoz7pSTj0D0rcZurq2uuTXAlhBBC5DZ7B/tkj+ooisKsL7zYNns2Wxf+gy5en2o5y/LlUzziE7JqFcGLF1NEhm5Pk1ql5o2qb7D51c2092iPh6MHw+oOM3dYQhRsparDq14w7CCon5l4Ys8U+K07/PIyXNoA+syPmyaEEEII8bxIgjUJW1tbmjdvDsCOHTtS3UdRFHbu3AlgmuhKCCGEKAgWLt6EJmA3+viL3DiymDXTDhH5JC7DcpFHjvBw2nc8mvkT9z//AkNcxmUKu5K2Jfnp5Z9Y1WUVVhbJh+n54/ofXHl8xUyRCVGAWTzzGN7Df+HmHuPy/X9g/Tswtz4cXwhxkc89PCGEEEKItMgQAc8YOHAghw4dYt++fZw4cYKmTZsm275u3Tpu3boFwNtvv22OEDNNp9Oh0+nMHUauSzynwnhuouCQdijMLTttsFQxC26hRo0BQ8IdHl37Fe9JYfT4oCUly6c9pnj0lSugN/Z2Ddu8mTg/P1x//hmNi3POTqIQsFXbJnsPboXd4pvj32BQDLz5wpsMqz0MG42NGSPMO3IfFHmumCeqVxdhcWweqocXjetC78COz1D2fwf13sZSV1XaoDAbuQ8az11RFAwGg2ksRvH8JD5ZlPgeiOdv4sSJTJ06lS5duvDnn3+aO5znLrttMDo6Gk9PTx49esSePXt4+eWX8yhCI4PBgKIo6HQ6LCwsMi7wVFbu7yqlkD7r9+TJE/T6/x59bNCgAQEBAXzyySd8+umnpvXW1tbY29ubXickJNCgQQMuXrxI2bJl+e2332jbti0Gg4ENGzbw3nvvER4eTufOndm2bdtzPaeMeHl54eXlhV6vx9fXFx8fH2xtbc0dlhBCiHzkXsBDQo/uwkofa1yhskbr0J2SjVywdU370Vv7CxdxXbsWdeIfk05O3Bs0kHgZizyZjdEbORt/1vS6uLo4PW16Ullb2YxRCVHAKQoukVeoHLSN0uEXkm2Kt7Dlsltf/J1bg0oezhPiedNoNLi6uuLu7o6lpaW5wylywsLCWLt2Lbt27eLatWsEBwej0WgoVaoU9evXp3PnzvTs2TPTCaV79+6xcuVKDhw4wPXr1wkLC0Oj0VCiRAk8PDyoU6cOL774Ii+//DJOTk4pytepU4eAgIAU6+3s7HB0dKREiRLUqlWLBg0a0K1bt1yd0+bOnTvs27ePs2fPcunSJYKCgnj8+DGKolCiRAnq1KlDz5496d27d65NShcQEECTJk2IjY1lz549NGjQIEVMdevWTbO8ra0tLi4u1K9fnzfeeIOuXbume7zixYsD0Lx5c7Zu3ZqlWL///numT5+epTLe3t4pYkrrPdZoNDg6OlKpUiWaN2/O4MGDKV++fLr1z5o1iylTplCrVi0OHjyYYoiy3BQfH09AQAAPHjwgISHzww1FR0fTv39/wsLCcHR0THffQptgrVChAv7+/hnuN3DgQJYvX55snZ+fH23atMHPzw8wNnqDwUBsrPGP0fr167Nnzx5T485vwsPDcXJyIjg4OMMGUBDpdDp2795N+/bti+xsncL8pB0Kc8tJG7xz5y6rp07GJjr46Ro1Wtv2NOnVhQYd3dP8cBN7+TL3R3+APigIAJWNDaW/+w77tq/k5FQKFZ1ex/LLy1ny7xJ0hv++8e5aoStjG4yluHX+/OyQHXIfFGYRdAWLE/NRXVqP6un/MYNzFfRDDoCFJHfE8yX3QYiNjSUgIIAKFSpgbW1t7nCKlMWLF/PVV1/x+PHjdPerUaMGCxYsoEWLFhnWN3bsWKKjozM8dp8+fVi9enWK9Z6enpnKwwBYWFjQvXt3Zs6cSYUKFTJVJj3jx49n2rRpGe5Xu3Zt/vjjDypWrJjjYw4bNowlS5bQsWPHVDvg+fn5UalSpUzX1759ezZs2ICdnV2q2xMT5a1bt2bv3r1ZinXy5MlMmTIlS2U2bNjAq6++mmxdZt9jGxsb5s+fn+6T3xEREXh6ehISEsLq1avp06dPluLLitjYWPz8/HB3d8/SvSo8PBwXF5dMJVhliIBUVKhQgQsXLjBjxgw2btzI7du30Wq11KxZk//973+MHj26QHw7p9VqC/Uv+sJ+fqJgkHYozC07bbBSpYp8OHsuc78aj1XQDcCALnonx9c/4cmDV2k/qCYabcqeDtq6dbFev467o0YTe+ECSkwMDz76iJJjxuA8dEiefutcUGi1WkY0GEGnSp2YfHQyZ4OMvVn/8vuLo/eP8knjT+jm2a1QXSu5D4rnqmwd6LUQ2k3AsHsSqotrMXSdhdY69T9GhXgeivJ9UK/Xo1KpUKvVpgmjRd4bN24cM2fOBIw9B/v27UvPnj3x8PAgPj6ea9eu4ePjw969e7l8+TIdOnTA29ub119/PdX6Vq9ezfDhwwHjU76DBw+mY8eOlCtXDkVRCAwM5PTp02zdupVz586Z3vO0uLm5meauAeOXEU+ePMHf35+jR4+ybt06wsLC2LRpE3v37sXb25vu3bvn6JpYWFhQt25dWrRoQb169ShTpgylS5cmIiKCmzdvsmzZMo4ePcrFixfp0KEDFy5cSDORmRn37t3jt99+A4zvR2rXI+m6nj17MnXq1GTbw8LCOHPmDD///DO3b99m9+7dvP/++3h7e2d4/Kz+f0v62XPp0qU0btw4wzIeHh5pHifxPTYYDERGRmJhYcGtW7dYuXIl27dvJyYmhvfee49q1arx4osvplqHk5MTQ4cO5fvvv2fatGn069cvS+eUFWq1GpVKleX7dZbu7YoodMLCwhRACQsLM3coeSI+Pl7ZtGmTEh8fb+5QRBEm7VCYW260wQSdTpk95TtlRp+upp+ZgycoMZHp16mPiVHufjxOuVztBdPPgx9+yHYchZXeoFfWXVunvLjqRaXW8lqmn6G7hirB0cHmDi/H5D4ozC0+Pl75e838lG3w4RVFuX3YPEGJIkXug4oSExOjXL58WYmJiTF3KEWGl5eXAiiAUq5cOeXgwYOKXq9Pdd9Vq1YplpaWCqBYWVkp586dS7FPQkKC4urqqgCKg4ODcv78+XSPf/nyZeX3339PdZuHh4cCKB4eHunWERERoYwdO9Z0HjY2NsqpU6fSLZMRnU6X4T4ffvih6ZizZ8/O0fG+/PJLBVDc3NzSvP63b982HW/gwIFp1vXgwQOlZMmSCqCoVCrl3r17qe6XWFfr1q2zHO/EiRNN5fft25fl8omefY/1er3y5MmTZNcg6XvbtWvXdOu7cOFCrsSVkezeq7KSX5OvmIQQQogiykKj4YPxn1O2c38UVCgqK9oN64O1Xfrf1KqtrXH78QdKfvSR8bWtLU49ej6HiAsWtUrN61VfZ/Orm+ng0cG0PjAyEHtL+3RKCiEyK9L6mfH7DAbYMgqWd4EtH0DME/MEJoQQecDf35+PP/4YMI5runv3bmrXrp3m/v3792fp0qUAxMXF8dZbb5kmJUp04sQJHjx4ABgfea9Tp066MVSvXj3Hj3Lb29szc+ZMvv/+ewBTb8ec0GgyfkD7888/Ny0fOnQo28cyGAymoSb79euX497bpUuXNj1KrygKp0+fzlF95vbNN99gZWUFwL59+9Kd/Kp27dqmNvzrr78+l/jyiiRYhRBCiCKu36D+NBk6jiZDxlCnac1k2wyG1IdqV6lUuAwfRtm5c3CbMQPralWfR6gFUknbksx8eSZzX5mLq50rE16cgJWFlbnDEqJw+ncj3D1lXD77G8xrApc2QOGcdkIIUcT8/PPPprlhJkyYQNWqGX/+evPNN+nUqRMAly5dSjE50p07d0zLlSs/30k5P/30U5o2bQrA+fPn83wicQcHB9Ny4nXMjsOHDxMYGAhA7969cxwXkGxM2Li4uFyp01xsbW3x9PQEjJNEZTROcOI13LRpU47eF3OTBKsQQgghaNW2Na3aJp/84GFwBPM/XcM/f99J0dshkWP79ji80ibZOkWnI+r48TyLtaB62f1ltr22jcauyce8uhV6i5/O/ERMQoyZIhOiEKn5GnT+ESyf/hEdFQTr3wGfPhB6J/2yQgiRjymKwooVKwDjBELDhg3LdNmPnj51BLBs2bJk25LOL3PlypWcBZlFKpWKDz/80PR606ZNeXq8NWvWmJZfeOGFbNezb98+wDg+Z8OGDXMcF5Bs4qjy5cvnSp3mlLRdZTSOabNmzQCIjIzMUc9ic5NJrgoxnU6HTqfLeMcCJvGcCuO5iYJD2qEwt7xug7HxCSz9bCqWkf9ywPsuDwM607pfFSw06X83qygKj76ZSvi6dZQYMYLiw4cVqgmdcoPO8N97ZlAMTDw6kX8e/cOu27v4ssmXvFgm9YkA8hu5DwpzS7MNNhgMlTtisfMz1L7bjeuu70Lxaoqh9RcYGg8BtfwZJHJO7oPGc1cUBYPBkO5jwCLnLl26REhICAAtWrTAwcHB9AV44nuQlldeeQUbGxtiYmI4fPhwsn3r1q1rWl60aBHdunXjlVdeyXG8mW0PSY916NChXG9HT548wc/PD29vb7y8vABj8m/YsGHZPlZiErB27dpotdo060m6Pr33KCgoyJQ8d3d3p169ehnGltXYk3aWyK3/rwaDIdU2mJCQwPXr1wHjRFaOjo7pHq9Ro0am5f3799O2bdscx5ZWrDqdDguLlJP5piUr93f5ZFGIeHl54eXlhV6vB2DXrl3Y2tqaOaq8s3v3bnOHIIS0Q2F2edUGr17xxzLyXwASYg5wZW8It662wrVRPGrLtMvZ+vpSbt06AELmz8fv8GEe9nkDpYjOrpyRwIRALkVeAuBe1D1G7htJXW1duth0wU5dMGZFl/ugMLc026Dd/yhTsQq1767ERvcElS4ai7/HE3HkV855vEe4TcHvISTyh6J8H9RoNLi6uhIZGUl8fLy5wynUjid5OqhmzZqEh4ebXkdERGRYvlatWpw6dYpHjx5x7do1ypQpA4CzszMdO3Zk586dxMbG0r59exo0aEC7du1o1KgRDRo0wNnZOVMxJibRDAZDsvjSY2VlhZubG4GBgdy4cSPT5dIzYsQIVq9eneo2W1tbFi5ciIuLS7aOpSiK6b149n14VmRkpGk5ODg42XsIxvft/PnzLFq0iIcPH2Jpacn3339PTEwMMTFpP9mUkJCQ5diTDjtw5coVrK2t093f0tIy1SEj0nqPk7ZBLy8voqOjAejRo0eGsVpYWODh4YG/vz8nTpzIlTbwrPj4eGJiYjh48CAJCQmZLpd4HpkhCdZCZOTIkYwcOZLw8HCcnJzo0KEDjo6O5g4r1+l0Onbv3k379u0z7GouRF6RdijMLa/bYOfOCj6LtDw+/BcA+viLxN4NI1jpxasfNKZY6dS/wFM6dybU0ZHHP88GRcHxwgVK6vWUmTMbTalSuR5nYdA+vD1TT07lTNAZAM7rzuOv9mds/bF0rdg13/YAlvugMLfMtcEuEPsR+v1TUZ9ZhgqFYjF+tGzaAKVck+caryh85D5oHMcyICAAe3v7DBM2AOf3BHB+T0CG+7m4O9Dl/eSTN21bcJHggIwTiXXbulO3rbvpdXxsAmumnMywHEDn4bUpWf6/cTr9LgZzcLVvhuW0Vhb8b2LTTB0ju5Imejw8PHB0dERRFCIiInBwcMjw80JiQhWMyaakuYIVK1bQrVs3Tp0yjmF99uxZzp49a9petWpV2rdvz6BBg2jQoEGax0ic7EmtVmcpF1GyZEkCAwNNia+c5jHS+v/Yt29fpk+fjru7e6rbMyMkJISoqCgAypYtm26s9vb/TWq6bdu2dMeY7dOnD+PGjcvUkAMajSbL1yhx0imAUaNGZbi/h4cHt27dSrH+2fc4sQ1qNBpu3brFihUrmD17NgClSpViwoQJmYrV1dUVf39/AgIC8iSPFRsbi42NDa1atcrUvSpRVpK9kmAtxLRabaH+RV/Yz08UDNIOhbnlZRscNPp9dnl4cN5nEWpFjyHhDhEBv7H2u0h6jGqFe/USqZYrNWwYNlWqEDjuEwzR0cT9+y93/9efcl7zsElnptuiqrJzZZZ1WsYfN/5gxukZRMRHEBoXyoTjE9juv53xL47H3SH7fwjkNbkPCnPLsA1qnaH7LKj3P9jyAXi8hKZi8+cXoCj0ivJ9UK/Xo1KpUKvVmZpJXRerJyo0456u9sV1KeqLjdRlqqwuVp+srFqlzlQ5AMVAsrKGBDJVVmttkeOZ5DOStDekg4MDarXa1Jsw8T1IT9IJniIjI5PtX6pUKY4cOcLy5ctZuHBhsuQqgK+vL76+vnh5eTFgwAAWLlyInV36T9pk5XokTURGRUVRrFixTJdNzbRp0/jkk08AY4LswoULLF68mN9//5179+6xdOlSqlSpkq26k07YVKJEiXTPMyvXYOvWrdja2jJr1qxMnX9W21t2vrBP7xj+/v7pPmr/8ssv4+XllemJ00qUMP5d8eDBgzz5v6RWq1GpVFm+X2dlX5nkSgghhBBp6tCjC50/nYxOY+yxqhieEPvYm00zt3LxwN00yzm88goeq1ejLVsWgISgIPwHvEV4Hs8OW1CpVCp6VenFlle30KlCJ9P6Y/eP0WtzLzbd2GS+4IQoLNybwLCD0OGb5OsNetg3DSKDzBOXEEWIpY0Gu2JWGf7YOKQcj8jGwTJTZS1tnulHpiJT5eyKWaG2SJ6E0mjVmSvrZEVeezZBmlVJy6TWQ1Cr1TJkyBDOnDnDvXv3WLNmDePGjaNly5bJkkze3t706NHDNDRhbkj6eHnS2Hx9fbl06VKqP6GhoWnWV7ZsWWrVqkWtWrV46aWXGD58OCdPnmTYsGEcPnyYpk2bcv78+WzFmjgOLkDx4sUzXW7gwIEoipLsJzY2lmvXrjFt2jRUKhXLly+nefPmPHz4MFuxZda+fftSxPLsj5+fX7brd3JyYuTIkdSoUSPTZRKvZWLv4IJIerAKIYQQIl21GtSj1A8/8+uEr7CMfARKLPER69m3PAwruz5UbVQ61XLW1apSYd1a7o7+gJgzZ1Di4rg39mPibtzAZdQoVHnc06MgcrFx4cfWP9K9Une+Of4ND6IeEKuPzdc9WIUoUDSWwDOJm9NL4cB0OLHImHyt/xbk06E5hCjo6rUrT7122Rv/uOuIOtkqZ2mtYdD32eu1XqGOC4PquGSrbG5zcfkvjgcPHmS5fNKkXUZjqrq5udG3b1/69u0LGJOKM2bMYPr06RgMBvbu3cvq1asZMGBAluNITXBwMGB89D1pIrlDhw74+/unWmbZsmUMGjQo08ewsLBgzpw5bNu2jYCAAN5//32OHj2a5ViTPl6e3jipmWFlZUXVqlX54osvaNiwIR07duTy5ct8/PHHeHt756juvObm5sbOnTsxGAxERkYSExPDmTNnmDVrFg8ePKBPnz6sXr3a1IYykngtC/LTAPKXjRBCCCEyVKqsG2PmzIWyVZ+uMRBvuIJnndSHCUikKVGC8suW4tSrl2ld1ImTkIXB5YuiVuVasannJt6s/iZ9qvahYemMx+MSQmSDXgdH5xiXY0Nhy2hY3g2Cr5s1LCGEeFadOv8lmM+dO5elsnq9ngsXLgDG8U7d3NyyVL5EiRJMmzaNTz/91LRu3dNJTXMqKCiIwMBAAKpVq5YrdabF0tKSTp2MTwodO3aMe/fuZbmOkiVLmpaT9mbNqQ4dOlC3bl0A1q5dm+97cmq1WlMv4Ro1atCmTRs+/fRTzpw5Q9myZVEUhaFDh3Lnzp1M1Zd4LXM6PIQ5SYJVCCGEEJlibWfPmB9/xKnBy+isivP2d1PQWGb8LbPa0pIy306l1GefoXV3p9yc2agsUz76J5Kz09rxeZPP+brZ18nWGxQDnx/6nCP3jpgpMiEKEQstDNkHdZL0sPE/DAteggM/QILMii6EyB9q1aplGqfy4MGDhIWFZbrs33//bZokq2XLltmOYciQIablGzduZLuepHbv3m1abtGiRbJtfn5+aT7CnpXeq0klTZBmNvmXVvknT55kK4a0vPDCC4BxAr2rV6/mat3Pi5ubGwsXLgSM499+9dVXmSqXeC3Ll89eD/f8QBKsQgghhMg0tYUF7376MR96zaeUW/JH5m7feELg9dBUy6lUKpwHD8Lzzy1onnksTZHerOl6dlKC9b7r+evWXwz/ezifH/qckNjc6z0hRJFk5wK9foEBG6GYh3GdPh72fQuLWsKd4+aNTwghMH4eePvttwHj49SLFy/OdNm5c+ealrObmASS9XzNzqRJz1IUhTlz5phev/baazmuMyNJe60mnVwrs6ysrEwTZPn6+uZaXAAJST4TJxTgz8fdunUzJct9fHy4fPlyuvsbDAZu3boFQM2aNfM8vrwiY7AWYjqdDp1OZ+4wcl3iORXGcxMFh7RDYW7mboMaa+tkxz53wZ+DP/2Ild0rtHnrJao1c029oIUF+iTl9KGh3Bs8mOLvvYdD1655HXahcPjuYdPyX7f+4vDdw4xtMJZuFbvlyh87mWXuNihErrdBj1Yw9BDqQz+iPj4flaKHR1dhaUf0DQZj6PSDjM0qkpH7oPHcFUXBYDCYZrQXeWf06NEsWLCAuLg4Jk+eTPfu3SldurTpPUjNmjVr+OuvvwBjL9guXbok21dRlEx/fjh58qRpuWLFium+55lpD9OnTzfV2aBBA9q3b5+n7SgqKort27cDYGNjg6enZ7aO16JFC65fv86pU6cyfQ3Se48St585c8b0umzZsjm+vs/Wn7Rsblxng8FgqvfZ8/vqq6/o3LkzBoOBqVOnpjum7KVLl0yTsDVu3DhP2kBirDqdDgsLi0yXy8r9XRKshYiXlxdeXl6m2fx27dqFra2tmaPKO0kfJRDCXKQdCnPLD20wKi6BW3/uwCbhPvFha9i7LJzTR8pR7IX49HMRej3llvyK7a1bPPz8Cy7v3MXjDu1BJr9K1yvKKxSzKcaO2B3EKDGExYcx8fhEVpxeQQ+bHjhbpD9xRW7LD21QFG253wYb41h1EvUCllE82tijxv9OABef/lEuxLOK8n1Qo9Hg6upKZGQk8fEypEZeK1GiBFOmTOGzzz4jMjKSdu3a4ePjQ+3atVPd/48//mD48OGAcfzR+fPnExERkWyfXbt2sXr1asaMGZNsnNdnPXnyhNGjR5ted+jQgfDw8GT7JCbGDAZDim1JRUZGMn36dObNmweAra0ts2bNSrdMeh4/fsyRI0fo0aNHmvvExsYyYsQIgoKCAOjRo0e2O6U1atSIZcuWERwczMWLF/Hw8Eh1v8SkIRgTdemd3+LFi/Hz8wOgdu3a2Nvbp7l/QkJClq9VXFycaTk6Ojrb1zqt9/jZdtWsWTPq16/PuXPnWLt2LePGjcPT0zPVOg8ePGhafumll7IdW3ri4+OJiYnh4MGDWeodnDi0RmZIgrUQGTlyJCNHjiQ8PBwnJyc6dOiAo6OjucPKdTqdjt27d9O+ffsCPcOcKNikHQpzy09tMCosnCUH/oZgAB26qM2EXW2No01bOr1bA61V6t8SKzodj86cJfzpI0HO+/ZRXqWi9HfTUBfiLwhzQ1e6MiJmBDPOzmCn/04AbibcZEH0AobWHsqAFwagUeftx7z81AZF0ZTnbdAwFP2ZX1GfWYr7oMW4WzlkXEYUKXIfNCatAgICsLe3Tza7usg748aN4969e8yZM4e7d+/yyiuv0K9fP7p3746Hh4dp/M41a9awZ88ewPhY+4oVK2jevHmK+qytrdm0aRObNm2ibt26dOnShUaNGlGmTBksLS0JCgriyJEjLF682JScbNiwIcOGDUOjSf5ZQ/30S3K9Xp9sfFOdTkdoaCh+fn4cO3aMDRs2EBoaCoCTkxMrV65MMf5qVoSEhDBw4EAqV65Mr169aNy4MWXLlsXKyorg4GBOnTrF0qVLTY+hly1blpkzZ2Y7X9KrVy8++ugjdDodJ06cSDPBnXQIgqioqBRjvsbHx+Pn58eGDRtYs2YNYLyGP/zwQ7qxBQcHs3HjxgzjrFGjBk2aNAGMbSDRw4cPMzX+rIuLC66uyZ9KS3yP1Wo1jo6OKIpCREQEDg4OKXpCf/311/Tu3Ru9Xs+8efNYsmRJqsc5evQoAHXr1k3zWuZUbGwsNjY2tGrVKkv3qqwkeyXBWohptdpC/Yu+sJ+fKBikHQpzyw9tsJiLMx/O+plfv/2OqKunAUiIOcCd0yGsC+lGrw8b4lAilQ8yWi1uU7/Bplo1Hn7/PRgMRO3dy723B+I+3wtt2bLP+UwKFletKzNenkGPuz2Yenwq96PuE6uPZc4/c9h9Zzcru6zEysIq44pyKD+0QVG05V0b1MJLI6HpMLQWz/zZdHoZJMRCk6GgzvyjhqJwKsr3Qb1ej0qlQq1WmxIvIu/Nnj2b6tWr89VXXxESEoK3t3eaj2BXr16dRYsWpTm5lbOzM3Z2dkRFRXH+/HnOnz+f7rHbt2/P6tWrsUxnwtLAwEDq1q2bbj0ajYYePXrw008/pdkDNLMS296NGzf44Ycf0t33xRdfxNvbm7I5+Jzp6upKjx49TInRoUOHphsXwJYtW9iyZUu69drb27NgwQI6deqU7n7Xrl3j3XffzTDODz/8kGbNmgHJx8x97733MiybWP7nn39Oc7tarTb1aE28DyT12muvUbNmTf7991+8vb2ZNGlSikmsoqOjTddlwIABeXYfUavVqFSqLN+vs7Kv3AGFEEIIkWNaSyuGTZyAR9tXTev08RcJubGKVd8c5OHt1L/9ValUlHj7LdwXLULtYOwdFnftGrf79CX67LnnEXqB16pcKzb13MSA6gNQYfzwXNOl5nNJrgpRJDybXA27C7u+hh2fw5K2cP+CeeISQhRpw4cP5/r160yfPp2OHTvi7u6OtbU19vb2VKpUiX79+rF69WouXryYZnIVoHnz5jx69IgtW7YwduxYWrdujZubG1ZWVmg0GkqUKEGDBg0YNmwY+/btY9euXTg7Z204IltbW8qUKUPt2rUZMGAAc+bMwd/fnw0bNuQ4uQrGmecPHTrE+PHj6dixIy+88ALFixdHo9FQrFgx6tSpwzvvvMP27ds5cuRImo+qZ0ViUvXAgQMEBgZmqw6tVouLiwstW7ZkypQpXLt2jQEDBuQ4tvxCpVLx5ZdfAsZezNOnT0+xz+bNm4mKisLa2prBgwc/7xBzlUpJOtKtKBQShwgICwsrtEMEbNu2jS5duhTZb4qF+Uk7FOaWn9vg4R07ObZ8PmrFOCa4Sl0CrdNrdHrvJao0Kp1mubhbtwh4/310/sZHllRaLa7fTKHYq68+j7ALhYuPLuL1jxc/tP4BR8v/PgNkZQKLzMrPbVAUDWZrg6eXwdaP/nutsoAXR8LLX4ClDG9SlMh90PjY7e3bt6lYsaIMEWAGieNgOjo6Sg/i50xRFGrXrs2///7L1KlT+eqrr8wdklnktA22a9eOPXv2MGzYMBYuXJgHERpl916Vlfya/A8UQgghRK5q0akjr375DQlaY6JBMYQQH7qKGzfvplvOytOTir//ju3TR5kUnY77n39B9KlTeR5zYVG7ZG0Wtl+YLLkK8Pu13/n04Kc8jnlspsiEKEQaDYbBO8ClmvG1ooejc2B+M7jxt3ljE0II8VyoVCq+++47AH7++WeioqLMHFHBc/z4cfbs2YOlpaWpp2tBJglWIYQQQuS6KnXqMGTGbAxOpQDQlKxJpz4NMixnUawY5Rf/QrH/9QPA6bXXsGnUKE9jLeweRj3k57M/s/32dnpu7smmG5uQB5iEyCGPF2H4IWjzFVg8HYcw1B+8e8OGIRD5yLzxCSGEyHPdu3enZcuWBAcH4+XlZe5wCpzJkycDxrFenx2btSCSSa6EEEIIkSdKuJbhg5/nsmnFKnoNHpTiEfWEeD0ay5STw6i0WspMnIhd06bYv/JKrj/aXtT4hfuhURs/8oXFhTH+yHi23trKhGYTKO9Y8D/MCmE2Gito/SnUfA3+/Aj8DxvXX1wLN3ZD/7Xg3sSsIQohhMhbXl5ebNiwAXt7e3OHUqBER0fTrFkzmjVrxpgxY8wdTq6QBKsQQggh8oyVrR19h6ecWXXJgp0Ybmp57cPGlHR3SLWsYyozqEYeOYLa2hrbhg1zPdbCqmmZpmzuuZnpp6az/fZ2AE7cP0GvLb0YXnc4A2sORKsummMHCpErXKrAoK1wzts4+VVsKGhtoVR1c0cmhBAij9WuXZvatWubO4wCx9bWlokTJ5o7jFwlCdZCTKfTodPpzB1Grks8p8J4bqLgkHYozK0gt8HNW44Ttn8hKosS/D4tio7vNaZCnYxno42/dZt7H36EITaWUhMm4Pjaq3kfbCHhqHHk2xe/pYtHF6admsb9qPvE6eOYfXY2229tZ3zT8dR0rpmlOgtyGxSFQ75rg7X7gWdbLHZ/jaF6TxS1NSSNTVFAeuQXKvmuDZqBTqdDURQMBgMGg8Hc4RQ5iUP+JL4HQjxvBaUNGgwGFEVBp9NhYZHyCbq0ZOX+rlJkEK5Cw8vLCy8vL/R6Pb6+vvj4+GBrKzOZCiGEyD8UReHiHxuxjQ0xrlA5oLXvSbEaTjh6xqebeyi1YSPFTp40vQ5p2YLgLl1AZs3Nkjgljj2xezgWdwwF48dAFSr62vallmUtM0cnROFkEx9Mo9teXCrXnyd2VcwdjhC5RqPR4Orqiru7O5aWluYORwghUhUfH09AQAAPHjwgISEh0+Wio6Pp378/YWFhODo6pruvJFgLofDwcJycnAgODs6wARREOp2O3bt30759e7RaeaRRmIe0Q2FuBbkNPvS7zeppU1BHhz5do0Vr14UqLzWjTf+qWGhTT5gqCQkE/ziDMB8f0zrbFi0o/cN0LBxSH2ZApO3fx//yzYlv8A31xcXGhQ1dN+BgmfnrWJDboCgcCkwbVBQs1vZHfWM3CioMDQdjePlrsC58n9OLmgLTBvNQbGwsAQEBVKhQAWtra3OHU+QoikJERAQODg4yZr0wi4LSBmNjY/Hz88Pd3T1L96rw8HBcXFwylWCVIQIKMa1WW6h/0Rf28xMFg7RDYW4FsQ2Wq1KVEbPmsmTieBIe+AE6dFGb8T0cypNHMbw2qh42Dqn0gtFqcZswHpuqVXkwdSokJBB9+DD33nob9wXzsSwEs48+T/Vc67Gm+xpWXl5JBccKlLArkWx7giHBNDlWegpiGxSFS75vgzGhEPUIABUKFmeWYuG7Hbr8CNW7mzc2kSvyfRvMQ3q9HpVKhVqtRi1PlDx3iY9kJ74HQjxvBaUNqtVqVCpVlu/XWdk3/569EEIIIQotu2LFGfHjTErU/m+G7YSYAwRd/gPvqcd4HBiZZtni/fpSfskSLJycAIi/eRO/N/oQdeJkmmVE6rRqVIAjLAABAABJREFULe/UeodXyr+SbP2DqAd03diVP67/gTzsJEQO2RSD9/ZAx2nGya8AIu7D7wNgdX8Iu2fW8IQQQgiRc5JgFUIIIYRZaC2tGPTl11Tr1Mu0Th9/kci7v7Nu5jH0+rQHyrdr1pQKa3/HslIlY7mwMO68+y6hGzbmedyFnaIofHviWwKjAplwdALv7noX/3B/c4clRMFmoYEXR8LIE1Cl43/rr/0FXk3hxC9g0JsvPiGEEELkiCRYhRBCCGE2KrWaboPfofXQDzGojDN6GhICqNBUjYVF+h9TLD08qLBmNXatWhpX6PVYOJdIt4zImM6gw05rZ3p96sEpem3uxeILi9EZiu5M2ULkimLlof/v8PoysCtlXBcfAds/gfWDzRubEEIIIbJNEqxCCCGEMLtGbdvTZ8K3GCxtKdWsN536tM1UOQsHB9wXLKDEoEGUGjcOh5dfzttAiwBLC0u+b/k9C9stpKx9WQDiDfHMOTeHvlv7cuHRBTNHKEQBp1JBrV4w6iQ0HPTf+rr/M1tIQgghhMgZSbAKIYQQIl/wqFGLUfOX8NaYQcnWx+n0HNpyk9io1HtPqiwsKP35Zzi/+06y9YqikBAcnFfhFnrNyzZnY4+NDKwxELXK+JHx+pPrDNg2gO9OfEeULsrMEQpRwNkUh+6zYfB2aP4hVOucfHtCvHniEkIIIUSWSYJVCCGEEPmGjYNjsteKovDz53M4s2EVq6aeIPRhdKbrerJyJTe7diPq2LHcDrPIsNXaMq7xOHy6+lC9RHUAFBR8rvowYu8ImQBLiNzg8RK0n5J8naLA72/CH8Mh6rF54hJCCCFEpkmCVQghhBD51q+/bcbi7h70sScJD9iAz7dHuHvtSYblok6c5OH30zGEhXHnvSGE+Pg8h2gLr5rONfHp6sPYhmOxtrAGYFCNQahUKjNHJkQh9e9GuL4Lzq+GeY3gn9XGpKsQQggh8iVJsAohhBAi36rurAKMSTyD7jqxwWv446dD/HvoXrrlrGvWwL5VK+MLvZ6HU77hwZQpKDqZpCm7NGoNg2sNZmPPjXxQ/wPauLcxd0hCFF4GA1g7GZdjQmDTcFjREx7fNG9cQgghhEiVJFiFEEIIkW81796T7uO+xmBhCYCif0h82Gr2/naY/b9fw2BIvUeXhb095bzmUSLJuKxPfFZzZ+hQ9GFhzyX2wsrdwZ0hdYakWO8X5vf8gxGisKrzBow8BTV7/bfu9gFY8BIc+gn08mWREEIIkZ9ozB2AyDs6nQ5dIeypk3hOhfHcRMEh7VCYW1Fqg571GvDmN9NZPW0KRD4BJYL4iDVc2BlOcGAknd+tgaVN6h9pSnz0EZoKFQmaMgV0OqKPHed2n76UmTsHy4oVn/OZFC5J2+CGGxv47tR3fN3ka16t9Kp5AxNFRqG/D1qXgFd/QVXzdSx2fIoq/C4kxMKeySgX16HvMgulbENzR1mkFfo2mAk6nQ5FUTAYDBgMBnOHU+QkjoWe+B4I8bwVlDZoMBhQFAWdToeFhUWmy2Xl/q5SZHaCQsPLywsvLy/0ej2+vr74+Phga2tr7rCEEEKIXJEQE82tvbtRhwWZ1mlsWmNRvC5lmsaisUn7I421nx9uK1aiiYoCQG9tzf03+xNdtWqex13Y3Um4wy+Rv5hed7fpTlOrpmaMSIjCx0Ifywv3N1Dp0S5UPP1jFhX7XphGhE1ZM0cnijKNRoOrqyvu7u5YWlqaOxwhhEhVfHw8AQEBPHjwgISEhEyXi46Opn///oSFheHo6JjuvpJgLYTCw8NxcnIiODg4wwZQEOl0Onbv3k379u3RarXmDkcUUdIOhbkV1TaYEB/Hhtk/8fD8SdM6xf5Fhv80Fivb9B/M0QUGcn/0B8T7+gKgLV+e8pv+QFWErl9uSmyD7dq1Y96leXhf9TZtG9dgHP1f6G/G6ERRUCTvg/f/QfPXGFQPL2Ko8Sr615aYO6IirUi2wWfExsYSEBBAhQoVsLa2Nnc4RY6iKERERODg4CATT5rJwIED8fb2ZsSIEcydO9fc4Tx32W2Dd+/epUqVKiiKwoULF6iax50eYmNj8fPzw93dPUv3qvDwcFxcXDKVYJUhAgoxrVZbqH/RF/bzEwWDtENhbkWtDWq1Wt78/Gt2r/qNi1s3oKis6Di8F/ZONhmX9fCg4mof7n36GdHHjlFu3lws5UmPHLO0tOTTJp9irbVmyUVjsmfG2RnoVXreqfVOBqWFyLkidR8s3xiG7oeTi1DXeh110vNWFIh+DHYuZguvqCpSbfAZer0elUqFWq1GrZYpXp63J0+esGTJEvbu3cuVK1cICgpCq9VSunRpGjduTI8ePXj99dcz/Uj03bt3WbJkCXv27OHq1auEhoai0WhwdnbG09OT+vXr07JlS9q3b4+Tk1OK8hUqVMDf3z/Fejs7O5ycnHBxcaFu3bo0btyYXr16UbZs3vfANxgMNG/enOPHj5vW5VY/w5MnT7Jq1SosLS354osvUvwf2L9/P23apD0pqJ2dHW5ubjRt2pTBgwfzyiuvpLmvn58fFZ8ObzVw4ECWL1+epVgHDRrEb7/9lqUy586do169esnWpZVE1Wq1FC9enBdeeIEOHTowZMgQSpUqlWbd5cuXZ/DgwSxatIhPP/2ULVu2ZCm2rFKr1ahUqizfr7Oyr9wBhRBCCFGgqNRqOrw1mHbDx9B69CfUblw92fa4aB1KGpNfqe3sKDd3DhXW/o61DA+Qa1QqFR/U/4ARdUeY1s06M4uF5xeaMSohCikLDbw4EhxKJ19/aQPMqQ8nF0M+HgdPCJE7Fi9eTJUqVfj000/ZsWMH/v7+xMTEEB4ezvXr1/Hx8aFfv37UqVOHw4cPZ6q+atWqMXnyZA4fPkxwcDAJCQnExsZy7949Dh06xJw5c3jjjTcYNmxYlmKNiooiMDCQCxcusHLlSj744AM8PDzo1asXfn5+2bwCmTN//vxkydXc9PXXX6MoCu+88w7lypXLcvmoqCiuX7+Ot7c3bdu2ZeDAgej1+jyINO/pdDqCgoI4ePAgX3/9NdWrV2fXrl3plvniiy/QarX8+eefnDx5Mt19CwLpwSqEEEKIAqlum7Yp1t1/FI7P1O1UrvoCXd6rhdYqZY8NlVqNVeXKydYp8fHcnzgJ53ffSbFNZI5KpeL9eu+jtdAy++xsALz+8UJn0DGq3ih5dFGIvBQdAts/g7hw2DYOLqyF7rOhdA1zRyaEyAPjxo1j5syZgHEc3L59+9KzZ088PDyIj4/n2rVr+Pj4sHfvXi5fvky7du3w9vbm9ddfT7W+1atXM3ToUACsra0ZPHgwHTt2pFy5ciiKQmBgIKdPn2br1q2cO3cuw/jc3NzYuXOn6bVOp+PJkyf4+/tz9OhR1q1bR1hYGH/88Qd79uzB29ub7t2758KVSe7evXt8+eWXqFQqnJ2dCQ4OzrW6T548ye7duwH4+OOPM9z//fffZ8SI/76IVhSFkJAQjh07xqxZswgKCmLFihW4u7szderUXIszNTt37sTNzS3D/Sqn85m4UaNGLFu2DIPBQGRkJIqicOvWLVNCOyQkhF69enHx4kVTz9tneXh40Lt3b9asWcPUqVPzvBdrXpMEqxBCCCEKhTidnuWff4tl5FX8w9uzOjiGXh/Ux764VbrlFEXhwTdTCfvjDyJ276bsTzOxb9XqOUVd+LxX+z20ai0zTs8A4JcLv6BRa3i/7vtmjkyIQkylhmqd4dxK4+u7J2FRS2j+EbT6BLQyNqYQhcX8+fNNydVy5crh4+ND8+bNkz2e3qJFC9599118fHwYPHgwcXFxDBgwgMqVK6d45Fuv1zN27FgAHBwcOHz4MHXq1Elx3B49ejBlyhSuXLnCxYsX041Rq9VSq1atVLcNHjyYWbNmMXHiRH766SfCw8Pp27cvBw8epFGjRlm5FBkaNWoUERERvPPOO9y8eZMDBw7kWt2zZxu/TG7atGm6ichEpUqVSvWatG7dmh49etCwYUNiY2OZM2cOEyZMyNNJ46pWrUqFChVyVIednR21atXCYDAQHh6Oo6MjzZs3Z8CAAfTp04f169cTFRXFzJkzmTdvXpr19O/fnzVr1vDXX39x69YtPD09cxSXOckQAUIIIYQoFE78/TeWkf8CenTROwi5tRvvb44T5B+ebjlDVDQxly4ZlyMjCfg/e/cdHkX1tnH8O1vSC4HQe5EiIgpSFEFAaWLFDkhRmqJi92cHEQuIgBq6gkoTUbHTi4hKsQAWpEMgdEIKaVvm/WMhkpcWQpLJbu7PdXG5c3bO7j3hYd08O3tmwAMcnjo139bnKo561u/J/5r+D4BIZyStKqlhLVKgQkvAze9Bz2+g1PFf9L1uWPEWjLsStuVfU0FErLNz587ssyXDw8NZuHAhDRo0OOP+Xbt25YMPPgAgMzOTe++995T3N6tWrWLfvn0A9O/f/7TN1ZPVq1ePO++880IOg4iICEaOHMkbb7wBQHp6On369Lmgx/z/Pv/8c+bOnUtsbCzDhw/P18dOSkris88+A6Bbt24X/HgXX3wxnTt3BiAlJYWNGzde8GNaxTCM7L9XgEWLFp11/44dO1KqVCm8Xi9Tpkwp6HgFSg1WERERCQhXt7+Oi67pkL3tyVhF+oEvmf3mL2xeu/+M8+wR4VSbPo3Idu18A14vB954k70vvoiZlVXQsQNWt3rdGHzlYMa1G0f9UvWtjiNSPFRvCQNWQqunwXb8whxHtsFHN8HcB31LCYiI3xo9ejQZGRkAvPTSS7m68nq3bt3o2LEjAH/++SfffPNNjvt37dqVfTs3Z2Lmp6effppmzZoBsG7dOr777rt8edzk5GQefvhhAEaMGEGpUqXy5XFP+PLLL8nMzATgtttuy5fHPPlr9Cce21/VqFGD8PBwAOLj48+6r9PpzF4eYtasWQWerSCpwSoiIiIBwWa3c+MDD3F19z6Y+Nb79Lo2kXX0E+ZPXM0vX28741mptrAwKo4ZTeyD/32NPWnOZ+y87z7cR9SQyKvbat9Gw9INc4x5TS9eUxfgESkwzhBo+zwM+BEqN/9v/I/psHiIdblE5IKYpslHH30EQGho6HldaOrRRx/Nvv3/zxI8+avo//zzz4WFPE+GYTBo0KDs7blz5+bL4/7vf/8jISGBVq1a0atXr3x5zJMtXboUgMqVK+dqLdPc2LlzZ/btKlWq5MtjWsUwDBwO34qkTqfznPs3b+77f9WWLVvYvHlzgWYrSGqwioiISMAwDINmN97CLU+9CA7f2qumZz+ZyTNY8+Uavp34J27X6a/OathslH7kESqMfAsj2Dc3fe2v7LjjTjI2bSq0Ywhkpmny2qrXeHHli3i8/nmVXBG/UaYu9P4ebhgFwVEQEg1tnrc6lYjk0V9//cWR4x/6tmzZkujo6FzPve666wgNDQXgxx9/zHHf5Zdfnn17woQJLFmyJB/S5t51112XfXvFihUX/Hg///wz48ePx+l0Mm7cuAt+vNM5kbNJkyb58ngbN27MPrO4efPmlC1bNl8e1yp79+4lKSkJIFdrvTZt2jT7dn6uk1vYdJErERERCTi1rmhKj9feYsarL+NOPgJmClkps9i2qjNrKkdw5fWnv5opQHTnzgRVqcLuBwfiPngQ15497Lz7Hqp8/BGh9fVV9wsx6tdRfPLvJwC4PC6GtRyG03buMxtEJI9sNrjiPqjdCQ79CxFlct5/4B/fmq12/TsUKerWrVuXfbtRo0bnNddut9OwYUN++eUXDh48SEJCQvaZl9WrV+eGG27gm2++ISMjg2uvvZYmTZrQqVMnmjdvTpMmTYiNjc3XYzlZ6dKlqVSpErt372bLli0X9Fgul4u+fftimiZPPvkkF198cT6l/M+BAwfYunUrkLM5nZt5fx5f8x98HzofPXqUn3/+mVGjRpGenk50dDSjRo3K98z/36ZNm0hNTT3rPuHh4TmWLTgfr7/+evbt22+//Zz7N2jQAKfTicvl4tdff8339XgLixqsIiIiEpBKV61OnxFjmP7qy6TEbwNcZHl/p0m7XuecG9qgAdXmfMruBweS8ddfBF9cj5CLLirwzIGuYZmGOP5x4Pa6+X7H97i8Loa3Go5TzR2RghVV3vfnZGlHYGpniKwAN42Bio2tySbFytpvvuDXb+eec78y1Wty69Mv5Rj7YvgrHNi+9ZxzG3e+hStuuDV7Oys9jSmPP3CWGf+55akXKVvjv3VIt/66mkWT4845zxkSyn2jxufqOfLq0KFD2bfLlSt33vNPPivy8OHDOb7aPmXKFK6//nrWrFkDwJo1a7Jvg++q8+3bt6d3797n3dzNjVKlSrF7927cbnf2Fenz4s033+Svv/6ievXqvPjii/mc0mf37t3Zt8uUKXOWPXMaN27cGc+otdlsDBgwgMceeyxX6+peqA4dOpxzn2uuuYZly5bl+jFTUlLYunUr7777Lh9++CEAF110EQMHDjznXIfDQcmSJdm/fz/btm3L9XMWNWqwioiISMAKLxFD79dG8MXot9ixcTP3vzYUhzN3b3+cZctSddrHHBg1itgBAzBOWqNM8ubaKtcyps0YHlv6GFneLBbtWsTjyx5nZOuRBNn18xUpVAtehLTDvj+TroVm/aHtCxAcaXUyCWBZ6WmkHjl8zv0iS516xmR6clKu5malp+XYNk1yNQ/A43bl2HZnZeVqbtDxr98XpJSUlOzbERER5z3/5DnJyck57ouNjWXlypVMnTqV8ePH89tvv+W4f9OmTWzatIn33nuP7t27M378+OyLGOWHk7OlpKTkqcG6efNmhg0bBsB7772XvSRCfjt48GD27ZiYmHx5TK/Xy6xZswgJCeGNN94g+PhSVUXZ8uXLMQzjtPcZhsHNN9/M2LFjc/0zOtFg3bdvX37GLFRqsIqIiEhAcwYFc8eTz5KemkJYVM71ylb/thfH4Swuv67Kad8k2kJDKffcc6eMZ27bjj0qEkcBfmUuULWq1Ip3277LI0sfIdOTybLdy3hk6SOMbj2aEEeI1fFEio8r7oO962D/BsCEVePhn6+h80io08nqdBKggkLDiCh57iu6h0adur5oaFR0ruYGhYbl2DYMcjUPwO7I+Y0KR1BQruY6Qwq+wRoZ+d+HH+f6evfpnDzndA1Mp9NJ37596du3LwkJCaxYsYK1a9eyatUqfvnlF1wuX/N52rRpJCQksGDBAux2ex6O5FQnN49PzrZp0yaysrJOO6dSpUqUKFEie7t///5kZGRw2223cf311+dLrtM5ctLFT8+nwfryyy8zePDgHGPp6els2bKFjz/+mFGjRjF69GjWrl3L/PnzCQsLO/0D5YPt27fnam3UvKpQoQKPPvroeV0A7MTP8tixYwUVq8CpwRrAXC5X9otgIDlxTIF4bOI/VIdiNdXg+XOGhuX4ef32xw5+GDOGkNC27N+VTJuutbE7zn39T09iIvH9+oHHQ/l33yG4Tp2CjF1kXUgNNinThHdav8OgZYPI8GSwcs9KBi4ayKhrRhHqKPhfUiUw6HXwApW9FHovwLZ6HLYfRmC40yF5D8y8G2/dm/C0fw0iz/9ryMWJatB37KZp4vV68Xq959y/0fU30+j6m3P12P//8W5+8oVc5zp5riM4hL5xU/I0t/rlV+R6bm6O/0KULFky+/bevXvxer2YpgmQ/XdwNvv378++HRMTc9b9y5Urxx133MEdd9wB+JqKI0eOZPjw4Xi9XpYsWcL06dPp3r37GR/jfH4eJ5Y/cDgchIeHZ89t3749O3fuPO2c999/n169egHwwQcfsHTpUiIjIxk1atQ5n/tC/q6CTvpG07Fjx876WCffd7q/o+DgYOrXr88bb7xBzZo1GTBgAD/++CPDhg1j6NCh5/VY53KiVk481oXW6xVXXMH777+PaZqkpqaSkpLCypUreffdd9mzZw8dO3Zk/vz5tGzZMlePl56eDvga/QXxb+nEvxeXy3VeHwycz+u7GqwBJC4ujri4ODwe31V5FyxYUKCfelht4cKFVkcQUR2K5VSDeeNyufnry++IcO0nyz2DLT/fzPZNeyl3RTrn+qZ6uZkziTq+/tbOrt3Ye/ddHCvGF7+6kBrsHtqdj1I/IossVu9fTbfPunFvxL0EG0X/q3FSdOh18ELVIqz2UBrGT6VMiu8CLLaNX+HZvIi/K9zJjlKtwTj3h0/FWXGuQYfDQbly5UhNTT3jmYaSP2rUqJF9e+3atTm+5n/yGaCn4/F4WL9+PeBbDiAiIuKUZQLOxuFw8Mwzz5CRkcHo0aMBmDVrFjfddFOO/U40xrxeb64f/8RFtwBq1aqVY97ZGm0ZGRnZ+7755psAXHXVVSxYsOC0+5/81fMpU3xN87CwMDp1Or8z9k/usSQkJJz1ONPS/luuIjMz86z73nHHHTz77LMkJibywQcf8NRTT+W4/+QzkF0u13n9/Z2Yc/Jjne/8/y84OJgqVarkGGvevDmdOnWiY8eOpKSk0K1bN3766adcLflwoskeGRl5wdlOJysri/T0dH744Qfcbneu5538d3guarAGkIEDBzJw4ECSk5OJjo6mffv2eV4cuihzuVwsXLiQdu3a4XTqohhiDdWhWE01eGFSjxzmwLJ5pB8EzAyyUubg9LTj8NrLuOWhBpQod+YPKN1NmrD3kUFk/vkntqwsKn70MSUHPULM/fefcS2qQJRfNXj1oat5aOlDpLpS8YR6uLrt1ZQOLZ2PSSVQ6XUwn5k9cf81B/vCFzDSDuP0pHHpwblcfPuzEJa7r1cXN6pBX5MrPj6eiIgIQkK0zEtBat68OSVLluTIkSP89NNPmKZJVFQUKSkpREZGnvU9yPz587MbRS1btsxzn2DgwIHZDdadO3ee8jg2my37v7l9jq+//jr7dqtWrXLM27FjR64e40TzcP78+cyfP/+c+5+4Sn3VqlW56667cvUcJ5z81fqMjIyzHufJzdjg4OBz/kxq167NqlWr2LdvHy6Xi1Kl/nvtPXmdWqfTed5/hye/RkVERFxwr8jhcBAVFYVpmjlqsHnz5gwbNoxHHnmEPXv2MHHiRF555ZVzPl5SUhLg+/kWRB8rIyOD0NBQWrVqdV6vVef1QURegol/cDqdAf0/+kA/PvEPqkOxmmowb2LKluO+N0cxZ/hr7N+4HvDgSpuHd18is4e7uPGBS6ly8ekbCs4KFag27WP2vvAiyd98A8CRMe/g3rad8q8OxeYHFybITxdag43LN2Zyh8kM/Xkoo9uMply4vpIs50evg/no8q5Qp6PvAlh/TMPoMAxntP5NnktxrkGPx4NhGNhstuzmmhScHj16MHr0aNLT03n//fd5/PHHAbL/Ds4kLi4u+3bv3r3z/HdVqVKl7Nvnes7cPIdpmrz33nvZ2126dCn0Ojrf56tVqxZhYWGkpaWxefPmXP8MzvXzAnKcWen1enPsf76P9f+d3IDPr3+vNpst+yzjkzMNGDCAUaNGsX37dkaPHs2jjz5K7FmuW7B///7sRuYll1xSIDVgs9kwDOO8X6/PZ1+9AoqIiEixFBIeQdeXhnJx247ZY56MVWQlfs2X76xl3dL4M861hYRQYcRwSj/6aPZY8tdfs7NHD9wnXV1Wcqd+qfrM7DxTzVWRoiCsJNwSB32XwGXdct537DDs+NGaXCLCoEGDsq8wP2TIELZs2XLOObNmzeLbb78FfM2rG264Icf9J6/NeS5r167Nvn3ykgV59eabb7J69WoAGjVqRIcOHfL0ODt27MA0zbP+ueaaa7L3PzGW2zNkT+Z0OmnevDkAa9asyVPe00lLS+Pvv/8GIDQ09KwNyaLO6XTyv//9D/CtUztq1Kiz7n/yz7FZs2YFmq0gqcEqIiIixZbNbqdjv4Fc06Mv5vFP9r2uTbiSZ7Ni5u8smbnxjHMNwyB2QH8qvvsORqjvwkwZ69az/Y47yTj+Blly7/9/tTHDncGrv7zK4fTDFiUSKeYqNvZdfv1kC56HqZ3hy4GQduT080SkwFSrVo0RI0YAvnU027Vrx4YNG864/+zZs+nZsyfguzjTxx9/fMr/b7///nvuvPNOfv/997M+95EjR3jkkUeyt2++OXcXKzud1NRUnnzySZ599lnA91X6yZMn5/nxCtuJCzetX7+ezMzMfHnMwYMHZ1/oqUOHDud1IaaiqFevXlSsWBHwnUF9YgmA0znRZA8JCaFVq1aFkq8gaIkAERERKdYMw+CKzjdTsnwF5r79BqYrE9Ozn8zkmSTx5DnnR7VrR9CMSsQ/OBD33r249+0jbc0aQi6+uBDSB6ZMTyaPLn2UlQkrWbNvDZPbT6Z0mNZlFbFU/GpYN9N3+/dpsGk+dHwDLrnt1EasiBSYhx9+mK1btzJmzBh27dpF27Ztufvuu7n55pupWrUqLpeLjRs3MmPGDBYvXgz41v+cNm0al1122SmP5/V6+fTTT/n0009p2LAhnTt3pkmTJpQvX56goCAOHDjAjz/+yMSJEzlw4AAAjRs3zm7cno7L5eLPP//MsX306FF27NjBTz/9xJw5czh69CgA0dHRTJ8+ncsvvzz/fkgF7Oabb2bIkCFkZWWxYsUKrrvuunPOOXDgQI6fCfjWBd28eTMfffQR8+bNA3xNxnOtWbplyxamTp16zuds2rQpF5/m/eimTZtyXDTrTMqXL59jHdjzERQUxJNPPsljjz1GUlIS77zzDi+++OJp9z1Rpx06dCD0+EkL/kgNVhERERGgRqMm3DvsLWYNe5mspCM4y9bllrsb5WpuSL16VP90NrsfepigWjWJ6dGjgNMGtiPpR9iatBWAbUnb6D2/N5PbT9YSAiJWqngFdB4Ji4ZAZjIcOwif3e9runYeCTHVrE4oUmyMHj2aunXr8vzzz3PkyBGmTZvGtGnTTrtvvXr1mDBhQvZZl/9fTEwM4eHhHDt2jHXr1rFu3bqzPne7du2YOXMmDseZ20kJCQk0aNDgrI/jcDi46aabePvtt6latepZ9y1qLr/8cho3bsyvv/7KjBkzctVgHTduHOPGjTvrPqVLl2batGnn/NmtXLmSlStXnvM5R40addoGa26XYhg1ahSPnrQc1vnq168fw4YN49ChQ4wZM4bHHnssx8W6wLe8w88//wxA9+7d8/xcRYGWCBARERE5rnTV6tw3fAwXd7iJgSOfP+VrdClHMs441xEbS5UPp1L+pZdOmWcevwCA5E75iPJM6TCFihG+r5btTN5J73m9SUhNsDiZSDFms0GTPjBwFdS78b/xLYtg7JWw8h3wuM88X0Ty1YABA9i8eTNvvvkmHTp0oHLlyoSEhBAREUHNmjW5++67mTlzJhs2bDhjcxWgRYsWHDx4kK+++orHH3+ca665hgoVKhAcHIzD4aBkyZI0atSI/v37s3TpUhYsWHDeZzWGhYVRvnx5GjRoQPfu3XnnnXfYuXMnn332md81V0/o168fAJ9//nmelwkICgqiXLlyXHvttYwcOZJ///2X9u3b52dMS4WFhfHYY48BcPjw4dM2mGfOnIlpmlSoUOGClp0oCgzzfFY0Fr+QnJxMdHQ0SUlJREVFWR0n37lcLr777juuv/76Ynu1TrGe6lCsphosfGPj5uP500bbbvWpf3WFXM9L/eEHDsWNpeI77+AsW6YAExauwqjBval7uX/B/cSn+C44Vj68PO+3f5/KUZUL5PnEv+h10GIbv4Vvn4SUkz74KHcp3PQOVPCfr/peCNWg7yvO27dvp3r16oSEhFgdp9jxer0kJycTFRVVIFdelzNLTU2lRo0aHDx4kGnTptGtW7dzTwpAF1KDXq+XevXqsWnTJl5//fXsC2MVhLy+Vp1Pf03/AkVERETO4fMvfiT9h/G4k+ey9KM/WD57E17vuT+jztyyhT2PP0H6unXsuOMO0jf8ec458p/yEeWZ2nEq1aKqAbD32F56ze/F9qTt1gYTEajb2Xc2a9P+wPGz9vethwP/WBpLRKQwREREZK8p+tprr+HVt5XO2yeffMKmTZuIjY3loYcesjrOBVODVUREROQsPG43u+a9D7jwuneSlTKL9Qv/5It3/yAr4+xfhzW9XuzHP+12HzjAzu7dSf7uu0JIHTjKhJVhSscp1CpRC4ADaQe4b/59bD261eJkIkJIFFw/HPosgjL1oXoraHiP1alERArFgAEDqFGjBn///Tdz5syxOo5fMU2TYcOGATBkyJBT1mb1R2qwioiIiJyF3eHg9seexBHme+Nneo+QlTKDhA1/Mv21NSQfTj/j3JDatan26WxCG/kulmVmZrLn8Sc4+M67Wpf1PMSGxvJ+h/epE1MHgEPph5iwfoLFqUQkW6UroP9yuH0K/L81qFkzGVL2W5NLRKQAOZ1Opk6dyssvv4zbrTWoz8fevXu5/fbbGTZsGP3797c6Tr5Qg1VERETkHCrVrU/PN0YTUfb42qtmBlkpc0iJ/5Xpr65m37akM851lCpFlalTiL711uyxQ2PHsuexx/Gmn7k5KzmVDCnJ+x3e5+JSF9OkXBOGXDXE6kgicjK7E8Jjc45tXQLfPgFxTeDXqaAPlkQkwLRs2ZLBgwfTtWtXq6P4lQoVKjB48GCee+457Ha71XHyhRqsIiIiIrlQomw5er7+NhXqNzw+4sGVNo/MIz/w6Yi1bFy174xzbUFBlH9tGGWefjr77K6U+fPZ2a07rn1nnic5RQdHM6n9JN5r+x6hjlCr44jI2ZgmLHnVdzsjCb4eBFM7w8FN1uYSEREpAGqwioiIiORSSHgEdz43hEuu7Zg95slYhTvlWxZ8vJ7MNNcZ5xqGQan7elNp3Fhs4eEAZPz9N7t634epr5XlWlRQFGHOsBxj+4/tZ93BdRYlEpHTMgzo+mnONVl3/QTjW8CyN8CdaV02ERGRfKYGq4iIiMh5sDsctO87kNY9+mafjep1baZu22CCw5znnB/ZujXVZs3EWakS2GyUffZ/GA5HQccOWIfSD9FnQR/6LujLmn1rrI4jIicLLwW3jod750JMdd+YJwuWvQ7jr4adP1kaT0REJL+owSoiIiJyngzDoHHnm7n16ZewBQVToc3dtO9yTa7nB190EdU+nU3Ft0cS0apVASYNfBPWTWBH8g7S3ek8uOhBfk742epIIvL/1WwDD/4MVz8OtuMfKB3aBFM6wVePgCvD2nwiIiIXSA1WERERkTyq0agJfcdM5J4B3XKMZ7o8zJ64noO7Us441xETQ1THjjnGTNMkcdYsvMeOFUjeQPRkkydpVcnXpM7wZPDQ4odYsXuFxalE5BTOULjuZei3HCpe8d944g5wBFsWS0REJD/o+2gBzOVy4XKdeS04f3XimALx2MR/qA7FaqrBoiM4MirH34Npmrz9v/cIO+Jl1h+NaN+7LjUuK52rxzr60cccGjGCIzNnUf7dd3CWL19QsS9YUalBGzZGtBjB/1b+j6W7l5LlzWLQ0kEMv3o411TK/VnF4n+KSg3KeSpVB3p8i+3XKdhWjsTdcQT46TrUqkHfsZumicfjwev1Wh2n2DFNM/u/+vmLFfylBj0eD6Zp4nK5sNvtuZ53Pq/vhnnipyF+Ly4ujri4ODweD5s2bWLGjBmEhYWde6KIiIjkm7//3kHQHwsBsAddiiOsDZG13UTXzDqxZOtp2dLTqT58BPa0NADcEREk3HsvGdWqFkZsv+cxPXya9il/uv4EfI3XO8Pu5JKgSyxOJiJnYvNm4bUF5RgrnbyByIw9bCvdHgx94bKos9lslC9fnkqVKhESEmJ1HBGR08rIyGD37t3s27cPj8eT63lpaWl07dqVpKQkoqKizrqvGqwBKDk5mejoaA4dOnTOAvBHLpeLhQsX0q5dO5zOc19MRKQgqA7FaqrBomv1F7P55bOZ2ds2R1Wc4Z2p2rgi7e6ti9155oZB1rbt7H34YVy7dvkGnE7KDH6ZqJtuKujY560o1qDb62bwL4P5bsd3ANgNO0OvHErHah3PMVP8UVGsQblAWcdwTGyJkbQLb7mGeDqPgnKXWp3qjFSDvrPWtm3bRkxMDKVL5+7bGpJ/TNMkJSWFyMhIjLN9iitSQPylBg8fPszhw4epXr06NlvuP7xLTk4mNjY2Vw1WLREQwJxOZ0D/jz7Qj0/8g+pQrKYaLHpa3NmNmHLlmTf+HUyPG697J1kps9ix9hbmHMqgy8OXExYVdNq5zjq1qT77E3Y/+hhpv/wCLhcHnn8Bz/btlH7sMYzz+EpTYSlKNejEyWstXyPIEcTcLXPxmB5e+PkFqpSoQsPSDa2OJwWkKNWgXKAtKyApHgDbvnXYPmgHVz4IrZ+FoHCLw51Zca/B6OhoUlJSKFOmTJFusASiE1/JNgzjvJpGIvnFX2owLS2N8PBwgoPPb83v83ltL7pHLyIiIuKnLm7VljtfGoYzPBIA03uErJQZJG7fxLRXV3F4T+oZ59pLlKDKpImUuPuu7LHDk99n90MP40nVxa/OxW6zM+SqIdxR+w4AulzUhUtji+4ZcCJykno3wn3zoXQ937bpgZ/ehbHNYfMia7PJGUVHR+NyuUhISEBfkBWRoiYxMZG0tLQC/4a3zmAVERERKQCV6tanx+uj+PS1l0netwfMDLJS5mB62jHrdTddHruc8jVLnHau4XRSfvBggi+6iP2vvQ4eD6lLl7LznnuoPGE8zgoVCvdg/IzNsPFi8xe5ouwVdKzeUWdUifiTKs2g/w/w0xhYPgI8mXB0F0y/DS65HTq+DhFlrE4pJwkLC6NSpUrs3r2b9PR0oqKiCAsLw2636/W3gHm9XrKyssjIyCjSZw9K4CqqNWiaJm63m6SkJFJSUoiJiSE6OrpAn1MNVhEREZECUqJsOe59bSRfvv0Gu//8A/DgSptHpiOTqLItzjm/ZLduBFWrxp7HHsebnIw3IwMjNLTAcwcCwzC4vsb1p4wfTj9MqdBSFiQSkVxzBEGrp+DiW+GbR2HHCt/4n3NgyyK4/X2odZ2lESWnyMhIqlatSlJSEkePHuXw4cNWRyoWTNMkPT2d0NBQNbPFEkW9BoODgylbtiwxMTEF/lxqsIqIiIgUoJDwCG5/djBLpkxg/aLv8dqC6TzgBsIjTr8O6/8X0aIF1T6ZRcLTz1Dh9ddwFMIbxED1z+F/uH/B/dx/yf3c3+B+q+OIyLnE1oKeX8MfM2DB85CeCO5MKFXL6mRyGmFhYYSFhVGuXDlcLlf22oxScFwuFz/88AOtWrUq1usAi3WKcg3a7XYcDkehNX7VYBUREREpYHaHg+v6PEipipWIrFCFiy6rk+P+1MQMHEF2QsJP/8Y0uHp1qs3+5JQ3iJ6UFGxhYUXy4ldFzZGMI/Rb2I+UrBRG/zYal9fFgIYDrI4lIudiGHB5N6jdAeY/B2XrQ0w1q1PJWRiGQVBQ7j5ElAtjt9txu92EhIQUueaWFA+qwf8UnQUSRERERAKYYRg0uv5mLrrs8hzjCfuP8s6L3/Dx0FUc3Z921vknM7OyiH/gAeIffBBP6pkvmiU+JUNK0rN+z+ztuD/ieOe3d3RBFhF/ER4LXSbCVY/kHM9MhWm3w65frMklIiKCGqwiIiIilslwuZn63DCCD04n/eBGpg9bTfzGI7mau+/VYaSv/ZVjy39gx913kxUfX8Bp/V+fBn146oqnsrcnbZjEqF9Hqckq4k/+/1c9l70OWxbCBx3g60ch/agVqUREpJhTg1VERETEIn8uWUBw6j+AC1fql7iT1zJ39O+sW7b7nHOjru+E7fjVULO2bGXHHXdybPXqAk7s/3rU78FzzZ7L3p7y1xSGrxmuJquIP/K4IH7Vf9u/ToG4pvDXXNC/aRERKURqsIqIiIhYpGGba6nd/OrjWybu9GV40pawYuY/LJ6+Ea/nzBcICW/enOqzPyGoRg0APEePsuu++0mcPbsQkvu3e+rew8tXvoyB70y4af9M49VfXsVr6oIsIn7F7oT75kPHN8EZ7htL3Q+f9oSZ90DSuT+sEhERyQ9qsIqIiIhYxBkUzA2DnqZ5l7uyxzyZ63ClzuWf5duYM+Z3MtPdZ5wfVLUq1WbNJPzq401at5t9L73MvmGvYbrPPE/g9tq380qLV7KbrLM3zeaVn1/Rmawi/sZmh+YDYOAqqN3xv/FN30NcM/hlPHg91uUTEZFiQQ1WEREREQsZNhst7rqXTgMfx7A7APC6d5KVMov9/+xk2qurSD6Ufsb59qgoKo8fR8mePbLHEj/+mPgBD+BJTi7w/P7sllq38HrL17EbdgDqlqx7ysXERMRPlKgM98yCOz6EiLK+saxUmPcMfHSzlgwQEZECpQariIiISBFwcau23PnSMILCIwEwvUfISplJ2v5t/Lhgx1nnGg4HZZ99lnJDXwGHr0l77McfOTL1w4KO7fc61+jM8FbDeabJM9xd926r44jIhTAMqH8LDFwNV9z333jtDqdeHEtERCQfqcEqIiIiUkRUqlufe197mxLlK/oGzHSyvGvpcFedXM2PueMOqnzwPvYSJQhr0oTYAf0LMG3gaF+tPd0v7n7KuJYLEPFToSXghlHQex5ccjs0eyDn/V6ttywiIvlLDVYRERGRIqREufJ0GzaSKpdchj2mLL3fGIzdnvu3bOFNm1Lt09lUfGcMRlBQASYNbAt2LODRpY+S5cmyOoqI5FXVK+H29+H48ivZFjwPn/eDY4esySUiIgFHDVYRERGRIiYkPIIuzw6mz+sjKFkmJsd9C3/cxRdj15GVcZaLX1WujCMm57yMTZvY/8abmC5XgWQOJEt3LeWZH55hSfwSHlnyCBnuDKsjiUh+2fMbrBoP6z+B966AP2ZofVYREblgarCKiIiIFEF2h4OImJI5xlb9uoV1E95mzx+7mP7aGlKO5K7x5z5yhN0PPMiRqVPZ1a8fnqSkgogcMCKCInDanQCsTFjJQ4sfIs2VZnEqEckXKXshOMp3Oz0R5j4AH90Eh7dam0tERPyaGqwiIiIifsCVlcmiscMxsraQmTKD1IRdTBu6iv3bk885N2PDBlwHDgCQ9vMv7LjzLjK3bS/oyH6rSbkmTGg3gXBnOACr9q3igUUPcMx1zOJkInLB6naGh9b41mY9YfsPMPZK+OEtcGtZEBEROX9qsIqIiIj4gdQjhykVdPwXf28KWSmzcCVv5tMRa/l39b6zzo245hqqfjgVe0nfGbFZO3ey4667SP1xZUHH9luXl7mcie0mEumMBOC3A7/Rf2F/UrJSLE4mIhcsooxvbdZun0GJKr4xTyYsGQoTr4H41dbmExERv6MGq4iIiIgfiClXge6vvU3ZGhcdH3HhSv0ST9pvLHz/L1bO3XrWq96HNWpEtdmzCa5dGwBvSgrx/ftz5ONpZ51XnF1a+lImdZhEdHA0AOsOrqPvgr4kZWqJBZGAcNF18OAvcNXDYBz/1fjA3/B+e9i2zNJoIiLiX9RgFREREfETETEluWvIG9RufvXxERN3+jLc6Uv4/fttfDl+Pe4szxnnB1WqSNUZM4ho29Y34PGwf9gw9r08WBe/OoP6perzfvv3iQn2XTTsr8N/0WdBHxIzEi1OJiL5Iigc2r8KfZdC+Ya+sfINoerVZ58nIiJyEjVYRURERPyIMyiYGwY9TfMud2WPeTLX4Uqdy+7f9zBjxFpM75nPSLVHhFPpvXcp1bdv9tjR2bPZ1a8fptdboNn9VZ2SdfigwweUCikFwPak7WxP0hq2IgGlwmXQZwl0eA1uHAN2R877M7U8iIiInJkarCIiIiJ+xrDZaHHXvXQa+Dg2h68J4HXvJCtlFhG1QjBsxjnnl3nicSoMfxMjKAiAyDZtMGx6a3gmtWJqMaXjFCpFVGJMmzE0KtvI6kgikt/sDrhyoK/ZerLdv8Ko+rB6EnjP/C0BEREpvhzn3kVEREREiqKLW7UlqnQZ5r41jMzUFBwVatPlroa5nh99000EValCyuIlxNx7bwEmDQzVo6vz1S1f4bQ7rY4iIoXF44KvB0FGEnz3JKybBTe9A2XrW51MRESKEJ2mICIiIuLHKtW7hO7D3qZh+84MfPN/Oe4zTZOd/x4560WsQi+7jDJPPI5h5DzrNf3Pv3Txq9M4XXN1+j/T2ZO6x4I0IlLg3JlQ8aQz1veshQmtYNEQcKVbl0tERIoUncEawFwuF64AvGDFiWMKxGMT/6E6FKupBuVk4aViuaZHH7ymifekmhg/YSnGBjtVGpemffc62J25+2z92PLl7H34EaK63Erp55/HcJ7aVFQN+nzw1we8t+49pv45lQnXTqByZGWrIxUbqkEpFLZg6DQSo/7t2L97HOPwZvC64ce3sf/5OaVi78Hlamd1Simm9DooVgv0Gjyf4zJMnZoQMOLi4oiLi8Pj8bBp0yZmzJhBWFiY1bFERETEAn9vPkzQmq+wOWvgDO8A0QYVmmRgDzr7Wz97airVho/AnpkJQFr16iTc2x1veHhhxPYrWWYW41LGcdB7EIBII5L7Iu6jtL20xclEpCDYvC4u2v8NF+3/GrvpBsDE4K+Kd7O1dEcwzr7+tYiI+Je0tDS6du1KUlISUVFRZ91XDdYAlJycTHR0NIcOHTpnAfgjl8vFwoULadeuHc7TnFEjUhhUh2I11aCcjTsriw8ef4CMo0cAMOzlCIq4GUd0NF0evpSY8mdvlqZ89x0HXnwJMysLAEfFipR/9x2CL7ooex/VoM/h9MM8sOQBtiRtAaBUSCnGtx1PzRI1LU4W+FSDYplDm7B/9zi2+F+yhzzNHsR73SsWhpLiSK+DYrVAr8Hk5GRiY2Nz1WDVEgEBzOl0BmSBnxDoxyf+QXUoVlMNyuk4nU469nuIb8YMx52ZgenZR2bKDEzzFmYP93DjgAZUvST2jPNL3nwzodWrEz9wIJ6Dh3Dv2cOe7vdSYeRbRLZpc8pzFecaLOcsxwcdP6Dfwn5sPLKRwxmH6be4H5PaT6JOyTpWxysWinsNigXK14fe3+NZ/Cr2lSMx7UHYL+mCXXUoFtHroFgtUGvwfI5JF7kSERERCUA1GzflnleGE1HqeCPVm0JW8iy86Vv5+r31/Lpo19kvfnXppVT/9FNC6vuulO1NS2P3gwM5/P4HuvjV/xMTEsPk9pOpX8r3s0rMTOS++ffx1+G/LE4mIgXGZsPb+lnWVHsIz/WjoHITqxOJiIiF1GAVERERCVBlqtWg27C3KVvzxFf7XbhSv8ST8Rs/f7qZ+R/9jcfjPeN8Z7lyVJ32MZEdO/oGTJMDI0aw97nns5cPEJ/o4GgmtZ9Ew9INAUjOSqbv/L6sO7jO4mQiUpASYppiXnpXzkGvB7YssiaQiIhYQg1WERERkQAWEVOSu15+ndrNWhwfMXGnL8OdvoR/Vu8lLensjVJbaCgVR71N7EMPZY+lLFqEa9++AkztnyKDIpnQbgKNyjQCIMWVwgs/voDH67E4mYgUqiWvwrTb4Pv/gcdtdRoRESkEarCKiIiIBDhncAg3PPoMzbv8d5aVJ3MDDTuEEVky5JzzDcOg9EMDqTh6FLawMCq+/TZBVaoUZGS/Fe4MZ9x142hWrhmlQkoxpu0Y7Da71bFEpLAk/A4/vu27vWocTLsV0o5Ym0lERAqcGqwiIiIixYBhs9HirnvpNPBxbA4HNW+8l7Y3XZVjn3OtrRrVsSM1Fy8iouXVBRnV74U5w3jv2vf4qNNH1IiuYXUcESlMFS6HG8eA7fiFUbb/ABNbw36tySwiEsjUYBUREREpRi5u1Zb7Ro3nlu535BjPyHIz9vVVrPth91nnO2JiThlLnr9Aa7L+PyGOEKpE5TzL1+118+ehPy1KJCKFpnEv6PUNhJf2bR/dCZPbwd9fWRpLREQKjhqsIiIiIsVMdJlyObZN02TUM+/g2fwrP87YxKIZG/F6z3426/GJHBo1mj2DBpHwwguY3jNfMKu483g9PLfiOe79/l6W7FpidRwRKWhVmkO/ZVD+Mt+26xjMvheWvg56rRQRCThqsIqIiIgUcz/MX0RQwhJcad/jSl/JxuV7+HT0b2RlnP3iLM6DB0maPh2A5K++5sCItwojrl/6bPNnfL/je9xeN08se4L5O+ZbHUlEClp0JbhvHjQ46RsDy9/wNVozU63LJSIi+U4NVhEREZFizrt/R/ZtT8YqXMe+5eC/h/jo1VUkH04/4zxXmTKUHT4cbL63lEemTOHw+x8UdFy/1OWiLtxY40YA3Kabp394mm+3fWtxKhEpcM5Q6DIJ2r0CGL6xozvBMCyNJSIi+UsNVhEREZFirnWPPrTu0Tf7F36vaxNZKZ+SceAIHw9dRcKWo2ecG9G2DeWGDM7ePjBiBElfflnAif2Pw+ZgaIuh3FrrVgC8ppdnVzzLl1v0sxIJeIYBLQZBtzlQsibcPQOCwq1OJSIi+UgNVhEREZFizjAMGne+mVueehFHcAgApmcfmSkz8B7bz+cjf+Pvn/eecX7MHXdQetAj2dsJz79A6ooVBZ7b39htdgZfNZg7a98JgInJiytfZM6mORYnE5FCcdF1MHA1lMh5ATwyksDMxbrXIiJSZKnBKiIiIiIA1GzclHteGU5kqVjfgDeFrORZeLO2sfTDf/jjxz1nnFtqwABiunb1bbjd7H5kEOnr1hVCav9iM2y80PwFutXrBviarEN+HsLMjTMtTiYihcLuyLmdmQofdIIvB4Irw5pMIiJywdRgFREREZFsZarVoOuwtylX86LjIy5cqV+S5v2Hiy4rfcZ5hmFQ9vnniOzQAQAzPZ34/gPI3La9EFL7F8MweKbJM/Su3zt77LVVr/HNtm8sTCUihc40Ye4DcOAv+GM6TO0MyWf+toCIiBRdarCKiIiISA4RMSW58+XXqd2sBQAeezA3PNiO8Iigs84z7HYqjBhOWLNmvu2QEPB6CjyvPzIMg8caP0a/S/sBUK9kPVpWbGlxKhEpVIYB9W8FR6hve89amNgadq+1NJaIiJw/x7l3EREREZHixhkcwg2PPsNPn06nSoPLqHxx7Rz3JySkkpV06mf1tqAgKr33LvteHkyZp57EWb58YUX2O4Zh8PDlD1M2rCztqrYjOjja6kgiUtgu6QKlasGsrpAUD6n7YEonuHEMXNbV6nQiIpJLOoNVRERERE7LsNlocde9VL64QY7x+L1H+Xj4MhJ+CePgrpRT5tkjI6n49kg1V3Ppzjp3EhMSk2Ms3Z2OqYveiBQP5S+Ffsugqu9bA3iyfEsHzHsWPG5Lo4mISO6owSoiIiIiueb2eJn64quEHp6BLWsvn49ZT+K+Y+ecZ7pcHJk+HdOjJQPOJSUrhd7zejNy7Ug1WUWKi/BY6PElNOnz39gvY2FaF0g7Yl0uERHJFTVYRURERCTX1i/4hrCUjWCmk5XyGWbabmYOX0vyofQzzvGmpRE/cCD7h77KviGvqGl4Fh6vhwcXPchfh//iw78/5I3Vb+jnJVJc2J3QeSTcMBpsx1fz274c/phhaSwRETk3NVhFREREJNcubtmWKpc0PL7lIiv1CzzJe5kxfC3HkjJPOyfjn3849tPPABydPZtD78UVUlr/Y7fZuaXWLRgYAMzYOIOhvwzFa3otTiYiheaK3tDzawiLhbo3QPMHrU4kIiLnoAariIiIiORaSEQEtz7zMpXrX+obMDPJSv0MV+I+Zg5fS8Yx1ylzwho3psLrr2dvH4qLI3HWrMKK7Hduq30br179KjbD91b9002f8vJPL+PxankFkWKj6lXQfzncOh5s+rVdRKSo0yu1iIiIiJwXR1AQNzz2LCGxZX0DZjpZKXNIP7CfWW/9SlbGqRdlib7xBso++7/s7X1DXiF5/oLCiux3bqp5E69f/Tp2ww7A3C1zeX7l87i9uuCNSLERXQmCI3OO7fwZPu0NmanWZBIRkdNSg1VEREREzpszJITyrTtSuloN34B5jKzUOaTu2c/sUb/hzjr1bMuSPXtSqs/9x/c3SXjySY6tXl2Iqf3L9TWuZ8Q1I3AYvrUYv932Lc/88Awu76lnCYtIMZC0G2bfC399Du+3gyPbrU4kIiLHqcEqIiIiInliDwrilmdepmTFyr4BbzJZKXPYn5LI8SVET1H6iSeIvuUWAEyXi90PDiRj48bCCeyH2lVtx9ut38ZpcwKwYOcCnlz2JC6PmqwixU7iDnAfX+v6wN8wqQ1sW25pJBER8VGDVURERETyLDQyijteHEZUmXIAeJ127n+0EQ6n/bT7G4ZB+aGvEH5NK9/+qans6tuXrN17Ci2zv2lTpQ3vtH2HIFsQ4LsQlmGcoYMtIoGr2tXQdwmUquXbTk+Ej2+FVRPANK3NJiJSzKnBKiIiIiIXJCKmJHe99BrVr2jOg++NoUSZ0mfd33A6qTRqFCENfRfKCqpUGXtEeGFE9VtXV7yauOvi6FS9E2+2ehOHzWF1JBGxQuxF0GcxXNTet2164Pun4auH/ju7VURECp0arCIiIiJywaJKl6HLUy8QFhWdY/zvTYdZ9d12zP93dpUtLIzK48cT0707VT54H3uJEoWY1j81L9+c4a2GZy8XICLFVGgJuGcWXP3Yf2O/T4OpnSFln2WxRESKMzVYRURERKRA/LR6B9++NpY1X25m1benXozFERNDuReexxYaakG6wLAreRdPLHuC1CxdUVykWLHZ4brBcNv74Ajxje1eA1M6gdZoFhEpdGqwioiIiEi+S05KZtF7w7Clr8J17DvWfr2NPxbHn3OeOzGRQ+PHY3q9hZDSv+1O2c39C+5nwc4F9F/Un+SsZKsjiUhha3A73DcPoir6tls/B3ad5S4iUtjUYBURERGRfHfs4F4ivYcB8Lq24Eqbz4+zN/HPTwlnnOPas4edXbtxcPQYDrw1srCi+q1UVyrp7nQA1h9cT98FfUnKTLI4lYgUugqXQ79lcMNouPQOq9OIiBRLarCKiIiISL4rX6sOtz79IobddzEmb9Y/uNMWs+jDf9j6+4HTzsnYtImsnTsBOPLBBxz+YEqh5fVHdUvW5f3271MypCQAfx/+m/vn38+RjCMWJxORQhdRBq7ofer4bx9BemLh5xERKWbUYBURERGRAlHt0su56bH/geF7y+nJWo8n/Qe+n7iBXX8fPmX/yDZtKDf45eztA8OHk/TVV4WW1x/VKVmHDzp8QGxoLAD/Jv7L/fPv51D6IYuTiYjl/pgJXz0Mk9rCgY1WpxERCWhqsBYRv/32G48//jgNGzYkKiqK0qVL06pVK+bOnWt1NBEREZE8q9WkOdc//ARgAODJ/BV32s98HbeevVuOnrJ/zJ13EvvIw9nbCc89T+qKFYWU1j/VLFGTKR2mUCasDABbjm7hvvn3cSDt9GcKi0gx4MqAJa/6bh/ZBpOvhY3fWZtJRCSAqcFaRAwfPpwPP/yQpk2bMmLECJ5//nkyMzO59dZbeemll6yOJyIiIpJn9VpcQ/v+/zVNPRm/4D62hrnv/EHKkYxT9o994AFiut7j23C72f3IINLXry+suH6pWnQ1pnaYSvnw8gBsT9pO73m92Xdsn8XJRMQSzhC473so18C3nZUKs+6B5SPANK3NJiISgNRgLSIefvhh9uzZw6RJk+jfvz+PPvooP/30E82bN+f111/nyBGtpSUiIiL+q0Hb9rTu0Td7253+A+5Se4iICT5lX8MwKPv880R26ACAmZ5OfP8BZG7bXmh5/VHlqMpM7TiVihG+q4nvStnFop2LLE4lIpYpUQXuWwD1u/w3tvRV+LQXZB2zLJaISCBSg7WIaNGiBSEhITnG7HY7Xbp0we12s2nTJouSiYiIiOSPxp1vpsVd3QEIKncxDzx7B4ZhnHZfw26nwvA3CWvaFABPYiLxffrgPnzq2q3ynwoRFZjacSpVo6rSt0FfutXrZnUkEbFSUBjc/gFc+xInlmrh77nwfntI3GllMhGRgOKwOoCcXUJCAgClS5e2OImIiIjIhWt2612UKFeBi5pehd2R862oaZo5Gq624GAqxb3Hznt7kLlxI+EtrsIeHV3Ykf1OufByzOw8kwhnxBkb2CJSjBgGtHwCytSHz/pAVgrs/xMmtoaus6FyE6sTioj4vYA8gzUtLY3vv/+eV199lS5dulC1alUMw8AwDAYPHpyrx0hJSWHw4ME0aNCAiIgIoqOjadKkCSNHjiQrK6tgD+C4PXv2MGXKFJo1a0bNmjUL5TlFRERECpJhGNS9qtUpzdVvF25l1htryDjmyjFuj4yk8sQJlHn6acq98gqGQ+cH5EZkUOQpzdXVe1ezOXGzRYlExHJ1OkLfxVDy+O+WdidEV7I2k4hIgAjId6irV6/m+uuvz/P8nTt30rp1a3bs2AFAWFgYmZmZrF27lrVr1zJ9+nQWL15MTEzMKXNN0yQzMzNXz2Oz2QgKCjrtfWlpadx6661kZmYyceLEPB+LiIiISFE3febP7PtqPEFhbfliNNz2RCOCQv57m+osU4ZS9/W2LmAAWLNvDQMXDyTUEcqk9pOoU7KO1ZFExAql60DfJTD3Qbj6UYgqb3UiEZGAEJBnsALExMRw7bXX8tRTTzFz5kzKlSuXq3lut5sbb7yRHTt2UL58eRYuXMixY8dIS0tj1qxZREZG8vvvv9O9e/fTzt+5cyehoaG5+tOoUaPTPkZWVhZdunTht99+Y/r06Vx66aV5/jmIiIiIFGWH9u5j57djwHuYrNS5HNq+ma/i1uF2ec46L+PfTRwYPRpTV8M+J9M0mbBuAhmeDBIzE7lv/n38degvq2OJiFVCS8A9M6By05zjGUmQst+SSCIi/i4gz2Bt2bIlR44cyTH2v//9L1dzP/zwQzZs2ADAZ599xpVXXgn4zja966678Hq9dO3ale+++47Fixdz7bXX5pgfGxvLlClTcvVcpzsD1uVyceedd7JgwQKmTJlCly5dTjNTREREJDDElC5F9bq1SdjwG+AiK/Vz9v5zJ99PcHD9Aw2w2089HyBtzRriHxyINyUFw+Gk9EMDCz+4HzEMg1FtRvHgogf54+AfJGcl02dBH8a3G0/D0g2tjiciRYHXA5/1hX0b4O7pUPH0JwOJiMjpBWSD1W6353nuhx9+CECbNm2ym6snu/vuu3n++efZvn07H3300SkN1oiICHr16pWn5/Z4PHTt2pUvv/yScePG0bNnzzw9joiIiIi/sDuc3P7088wY+hKHNv0FZiZZqXPYse4uFk6x0/6++thsOdcSde3bjzclBYBD772HI7YUMXffbUV8vxEZFMn4duMZuHggv+7/lVRXKv0W9GPcdeNoVFaNFJFib+UY2Dzfd3tKJ7jxHWh4l7WZRET8SMAuEZAXaWlprFy5EoBOnTqddh/DMOjYsSMACxYsyLfn9nq99OzZkzlz5jBq1CgGDBiQb48tIiIiUpQ5g4K55/nBRFe9yDdgppOV8imbV21i+cx/T1kGIPrGGyjzv2eyt/e9MpTkfHxfFqjCneGMvXYszco3AyDNncaARQNYvXe1xclExHKXd4fKzX233RnwRT9Y8ILvzFYRETmngDyDNa/++ecfvF4vAJdccskZ9ztx3759+zhy5AglS5a84Od+6qmnmD59OldeeSWxsbFMmzYtx/1XXXUVNWrUOO3czMzMHBfWSk5OBnzLDbhcrtPO8WcnjikQj038h+pQrKYaFKvldw0adgd3P/cyH7/8LGn74sE8hiv1M/5abscRZNDs5uoYxn9nskZ160bW/gMcnTIFvF4SnnwKxkcS2uSKfMkTqJw4GdVyFE+ueJKf9v5EujudBxc/yNut3ubK8qd+e6so0+ugWC2gajA4Brp+hn3+M9j+OP676E/v4t33F55bJvrWbZUiJ6BqUPxSoNfg+RyXYRaTKwNUq1aNnTt38vLLLzN48ODT7vP1119z0003AbBu3bozXlzqyy+/5JZbbgFgw4YNZ23G5lbr1q1Zvnz5Ge+fMmXKGZceGDx4MEOGDDllfMaMGYSFhV1wNhEREZHC4slIZ+v8b7AdOwqAYYvBGXkn5VpCUJQ3586mSdnZnxL922++ucHBxD8wgKzyuir2ubhNN7OOzWKjeyMAQQTxRNQThNvCLU4mIpYyTaodWkyD3dOx4Tt7NTW4LKtqPEpqSEWLw4mIFK60tDS6du1KUlISUVFRZ91XZ7CeJOX4Wl7AWRuTJ9938pwLsWzZsjzPffbZZ3n88cezt5OTk6lcuTLt27c/ZwH4I5fLxcKFC2nXrh1Op9PqOFJMqQ7FaqpBsVpB1mBq69Z8+OL/8CQdwvQmElFjD7fcff9p9zXbt2fvI4NI+/FH7JmZ1Jw+g0off4SzohoB59LJ04nnfnqOpbuXMvjKwXSs1tHqSOdFr4NitcCtwc54d3bB+Px+jLTDRGTup+3WYXhumYB5UQerw8lJArcGxV8Eeg2e+IZ4bqjBGgCCg4MJDg4+ZdzpdAZkgZ8Q6Mcn/kF1KFZTDYrVCqIGY8qWo/erb/LBs09QruHV3P1w/xzLA/y/AFR+Zww7e/cmY916PAcPsnfAA1T/4nNsoaH5mivQOJ1O3mr9Fr8f+J0m5ZpYHSfP9DooVgvIGqzVBvouhVldYf+fGFmpOP7+HC6+wepkchoBWYPiVwK1Bs/nmHSRq5NERkZm305LSzvjfiffd/IcEREREckf0WXKMmDMOO55ZMApzdXUxMwc27awMCqPH09Q9epgGMTc213N1Vxy2Bynba7uTN5pQRoRKVJiqsL9C+Dim6FcA7jpXasTiYgUWUW2wXriYlOFqUKFCtm39+zZc8b9Tr7v5DkiIiIikn9CI079IHvs6Pl8+PwKdv19OMe4IyaGKpMnUfGdMZTs1q2wIgakTzd9yk1zb+KLzV9YHUVErBYUDnd8CD2/9t0+mddjTSYRkSKoyDVYPR4PH3zwAXXr1i30565Xrx42m+9H8ueff55xvxP3lStXjpIlSxZKNhEREZHizDRNRoz8gvSfx5OV/C3fxK1j75ajOfZxVqxIVLt21gQMEOsOrmPoz0Pxml5e+uklZv872+pIImI1w4DQmJxjiTtgbHPY8aMlkUREipoiswary+Xigw8+4M0332TnTmu+khQWFkaLFi1YsWIF8+bN46mnnjplH9M0mT9/PgDt27cv7IjnxeVy4XK5rI6R704cUyAem/gP1aFYTTUoVivsGjyWegzvbzOw4cLr2kxm8vd8+a6Nmwc1JLZyxBnnpS5aRObff1PqkUcKJae/qxddj3vq3MOMf2cAMPSXoWS4Mrinzj0WJzuVXgfFasW2BrNSccy8B+PQJsyPbsbb/jW8jXr7GrFSqIptDUqREeg1eD7HZZimaRZglnPKzMxk4sSJDB8+nISEBMDXxDQMA48n/75yUK1aNXbu3MnLL7/M4MGDz7jf+++/T58+fTAMg59//plmzZrluH/27NncddddACxatIhrr7023zJeqLi4OOLi4vB4PGzatIkZM2YQFhZmdSwRERGRfHF0dzwHVizAZvqWkrIHN8QZ1ZqyV2bgjDh1eanoVaso88VcDNPkYOfrSWzVqrAj+yXTNFmQsYAVmSuyxzqGdOTqkKstTCUiRYXDfYwrdoylbMqG7LEdpdqwvtK9mLYicw6XiMgFS0tLo2vXriQlJREVFXXWffOlwTp//nyWLFnC9u3bSUpKIjIykksuuYQuXbpw6aWXnnaO2+1m3LhxvP766+zfvz+7qXoiTuPGjVmzZk2eMyUmJuZo0DZq1Ij4+Hieeuopnn766ezxkJAQIiL+O+vB7XbTqFEjNmzYQMWKFfnwww+59tpr8Xq9fPbZZ/Tp04fk5GQ6derEd999l+d8BSk5OZno6GgOHTp0zgLwRy6Xi4ULF9KuXbuAvEqd+AfVoVhNNShWs6oG169cyZJxI7Hhe89oD25MZLnruPWJy4gsGZJj36RP53DwlVeyt8u+9hqRN+oK2LlhmibjN4xn0p+TsscevPRB+lzSx8JUOel1UKxWrGvQ68G2dCj2X977b6hSMzy3TYGIMhYGK16KdQ1KkRDoNZicnExsbGyuGqwX9PHSr7/+Su/evfnrr79Oue+LL75g6NChdO3alYkTJxJ60pVc58+fz4MPPsiOHTuyG6vgeyPXrFkzXnzxRa6//voLicbll19+2qUGRowYwYgRI7K3e/bsydSpU7O3HQ4HX331FW3atGHHjh1cd911hIWF4fV6ycjIyH7s6dOnX1C+wuB0OgOywE8I9OMT/6A6FKupBsVqhV2DjVu3Jisri5Xvv4MBeDJ/JXV/EN+8Z+e2JxsTHh2cvW9s13swjyZy6B3fla/3v/QSQaVLE9FSZ2LmxiONHyHYEcx7f/gaKGPXj8VreHmw4YPZ79+LAr0OitWKZw06oeMwKH8pfPUweDKx7V6FbUo7uHs6VLjc6oDFSvGsQSlKArUGz+eY8nyRq7Vr19KmTRv++usvznQSrGmazJgxg1tvvTV77Omnn+b6669nx44dOfZr1aoVCxYs4Oeff77g5uqFqlatGuvXr+ell17ikksuwTAMnE4njRs35q233uKXX34hJibm3A8kIiIiIvnuyvbtaXRP3+xtd8bPJMav5MvRf5BxLOdaWbEPPEBM1+Prh7rd7B40iPT16wszrl/r37A/jzd+PHt7/LrxTFg/wcJEIlKkNLwL7psHkRV828l74IOOsGGOtblERApZns5g9Xq99OjRg9TU1Bxnn4aHh1OiRAkSExNJS0vLHl+4cCHTp0/nn3/+4a233sqxFMB1113Hiy++SMuWLfPpkHxObuDmRWRkJEOGDGHIkCH5E0hERERE8k3bW24mPS2djV9OA8CdvpwDu4JIPlSPkPD/zjYwDIOyzz+P+9BhUhYswExLI77/AKrOmE5w9epWxfcrvS/pjdPm5M01bxLhjKBlxfx93y4ifq5iI+i3DD7pDrtXgzsDPrsfytaHMvWsTiciUijydAbr999/z8aNG7MbpTfeeCO//fYbKSkpxMfHk5qayqpVq+jYsWP2nKFDh2Z/Nd80TS699FKWLl3KggUL8r25KiIiIiKBr3PXu6nargsAJgYXta5Emaqnro9l2O1UGDGcsCZNAPAkJhJ/fx9c+w8Ual5/1v3i7rx05UuMu24c9WPrWx1HRIqayLLQ6xu4/F7f9jXPqLkqIsVKns5gnTt3bvbt22+/ndmzZ5+yT5MmTfjuu+/o0qULc+fOZfPmzdnrrfbp04e4uDgcDl1hUERERETy7rb7ezMrK4tqF13Ele2uPeN+tuBgKo2NY+e9PcjcuBFXQgK7H3qIap/MwrDledWsYuWO2necMuY1vQDYDP0MRYo9RzDc9C7U7QwXdbA6jYhIocpTh/PXX3/Nvj18+PCz7vvWW29lN2QNw+Dqq69mwgSt21QYXC4XLpfr3Dv6mRPHFIjHJv5DdShWUw2K1YpSDd7e934gZxav18u23w5Ro1FpbLbjF2QKCaH82Dh233sv3qRkSj46CLfHAx6PFbH9nmmavLn2TdLd6bzU7CXsNnuhPn9RqkEpnlSDZ1DjOt/r6kmvrca6mRBWCvOi9hYGCzyqQbFaoNfg+RyXYZ7pClVnUaZMGQ4fPkytWrX4999/z7l/nTp12Lx5M4ZhMHfuXG688cbzfUrJhbi4OOLi4vB4PGzatIkZM2YQFhZmdSwRERGRQuX2wIZFOyiVWYLIGmUpUT+Tky967zx0CFtWFpkVKlgXMgDMS5/Hj5k/AnCp81JuC7sNu1G4TVYRKfpKpv5Liy1vYJhe/il/O5vL3kCOF2URkSIqLS2Nrl27kpSURFTUqctQnSxPZ7AmJSUBUKlSpVztX6lSJTZv3gzAZZddlpenlFwYOHAgAwcOJDk5mejoaNq3b3/OAvBHLpeLhQsX0q5dO5xO57kniBQA1aFYTTUoViuqNZjl9jL0lUmUPryQLJykbL+dWnWa0PSmatkXZ5X8EbwrmF9W/oLbdLPetZ7SUaV5rcVrOG2FUw9FtQal+FAN5o7tu8XYTN/ZrBfv/ZS6MS48N7wDTp0MdKFUg2K1QK/B5OTkXO+bpwary+XCMAyCg4NztX9QUFD27YoVK+blKSUPnE5nQBb4CYF+fOIfVIdiNdWgWK2o1aDd5iEmY8fxLRdZqZ/z+zwHIeFBXNGp2mnnmKbJoXffA8Og9MMPFVZUv9exZkdCgkJ4fNnjuLwuFscv5pmVzzDympEE2YPO/QD5pKjVoBQ/qsFzuHE0lKgES4cBYPt7LrYj2+DuGVCisrXZAoRqUKwWqDV4PsdU6KvR23QRAREREREpIDa7ncdef5WM0tV9A2YmWamf8fPna1m/dPcp+5umyb4hQzg0diyH4uJInDWrkBP7t9aVW/NO23cItvtOvFgWv4xBSweR4c6wNpiIFB02G1zztK+hGhThG9u3Hia2hh0rLY0mIpJf1O0UERERkYASFBLCo2++QVqJ48tZmWlkpcxh+Yw1bPxlb459DcMgqFq17O19Q14hecGCQkzr/66ueDXvXfseIfYQAH7c8yMPL3mYdHe6xclEpEip2xn6LIKY4x+ApR2Cj26CNe9bm0tEJB+owSoiIiIiASc8PJyH33yTYxFlfQNmKq7UOSyasoZtvx/MsW+pXr0oef99x/czSXjyKY6tXl3Iif1b8/LNGXfdOEIdoQD8svcXBi4eSJorzeJkIlKklKkHfZdAjda+ba8bvn0clgyzNJaIyIXK0xqsJ6xevZq2bduec7/169dn387N/uA7m2Dx4sV5ziYiIiIixVuJEtH0f/1NJjz9OOHpRzC9SWQlz2HeRDs3DbqSSnVLZu9b5okn8Bw6TNKXX2JmZbF74ENUnfYxIXXqWHgE/uWKclcwsd1EHlj0AKmuVPYf288x1zHCdCEbETlZWEno9hksfAl+iQNHCNTpZHUqEZELckEN1sTERJYvX56rfU9ctTU3+5umqau8ioiIiMgFK1smlp6vvMGHzz9FeFYSpvcI6alfEVU654f+hs1G+VeH4k48wrEfVuBNSSG+T1+qzpxJUCVdpDW3LitzGZPaT+KVn1/hnbbvUDqstNWRRKQosjug42tQrgHYnVCxkdWJREQuSJ4brKZp5mcOKQAulwuXy2V1jHx34pgC8djEf6gOxWqqQbGaP9VghfKl6fLsEL4Y9gIhXhftH+hLaJTjtNnLjhjBnr59yVy/AffBg+zqcz+VPvoIe0yMBcn9U53oOkzrMA3DMAq0PvypBiUwqQbzQf3bff89+WfodWPs/AmzeitrMvkR1aBYLdBr8HyOyzDz0Cnt3bv3+U7JkylTphTK8wSKuLg44uLi8Hg8bNq0iRkzZhAWpq9kiYiIiADsO3CUCJubiNjYs+5nO3aMKuPGE3TQt1Zr6sX1SOjZszAiBiyX6WJe+jzahLQhwhZhdRwRKcLq755BrYPz2FT2Rv4pfxsYunSMiFgjLS2Nrl27kpSURFRU1Fn3zVODVYq25ORkoqOjOXTo0DkLwB+5XC4WLlxIu3btcDqdVseRYkp1KFZTDYrVAqUGk1Iy+O2L7VxxQ3UiS4Zkj7sSEth9bw9sISFUmDAeZ6VKFqb0b1meLB7/4XF+2vsTNaJrMK7tOEqHXvjSAYFSg+K/VIP5z4hfheOjztnb3lrt8Nw8AUIC7/fa/KAaFKsFeg0mJycTGxubqwbrBa3BKkWb0+kMyAI/IdCPT/yD6lCsphoUq/lzDSYcSOWjZ98iODOF/dtvo8tTVxAeHQyAs2pVqk75AHuJEjhKlbI4qX87mHmQrUlbAdiWtI3+i/szuf1kyoaXzZfH9+calMCgGsxH1VtAp+Ew71kwPdi2LMQ2tQPcMwtia1mdrshSDYrVArUGz+eYdK69iIiIiBQ7WW4vw4cOx5n6K17XJg7v/IqvxvxOxrH/1toKrlnzlOaq6fUWdlS/VyGiAlM6TqFCeAUAdiTvoNe8XiSkJlicTESKHMOAZv3h3s8h9Pja14c3w6S2sHmhtdlERM5CDVYRERERKXaCHDYaXn0lnuNvhz1Zf7F/yzd8/e4fZGW4TzvHm5HB7kce4fAHuk7A+aocWZkpHadQKcK31MLu1N30mteL+JR4i5OJSJFUozX0XQplLvZtZybB9Dvgx9GgVQ5FpAhSg1VEREREiqXed3UitH1PvBgAeDL/YM/f3/Ht2PW4XZ4c+5ouF7v69CF10WIODB9O0ldfWRHZr1WIqMDUjlOpFlUNgL3H9tJrXi92Ju+0NpiIFE0lq8P9C6HuDccHTFj0MnzWB1zplkYTEfn/8tRgfeWVV3jllVeYMWNGfufhoYceolGjRjRu3DjfH1tERERE5GQP338b3qvvyt72ZK5h17r5zJ/0Fx7Pf8sBGE4n4Vddlb2d8NzzpK5YUahZA0HZ8LJM6TiFmtE1ATiQdoBe83qx7eg2i5OJSJEUHAF3fgytn/tv7Mg2OP7BmIhIUZGnBuvgwYMZMmQI06ZNO+e+drsdu91O586dz7kvwNatW/njjz/4448/8hJNREREROS8PP1QN5Ib3Zy97c5YyZbVC1g89R9M739fRY194AFiut5zfCc3uwc9Svr69YUd1+/FhsbyQccPqB1TG4BD6YcYv268xalEpMiy2aD1M3DXNChZE+6eDs4Qq1OJiORQ4EsEmMfXRzG1ToqIiIiIFEGGYfDik/dzsF777DF3+jL++XER65bE59iv7PPPE9net5+ZlkZ8/wFkbtte6Jn9XcmQkrzf/n3qlaxH47KNGdJiiNWRRKSoq3cjDFwNURVyjmckW5NHROQkWoNVRERERIo9h93GkBcGsrdGy+wxt2sNdZrH5tjPsNupMGI4YU2aAOBJTCS+Tx9c+w8Uat5AUCKkBJM7TCbu2jhCHaFWxxERf2B35NzOSILJ18K3T4DHZU0mERHAce5dxF+5XC5crsD7n8yJYwrEYxP/oToUq6kGxWqBWIM24LnnHuaVlzOokLiNnq8MwxHsPPUYbTbKjhnNnt73kfXvv7gSEtjVty8Vp3yAPSrKkuz+KtTwNVZP/hkfTDvIvrR9NIhtcNa5gViD4l9UgxYzTexz+mA7tAkObcK7/y88XaZAeOy55wYI1aBYLdBr8HyOyzDz8N19m82GYRh06NCB7777Lt/2BejUqRPz58/HMAw8Hs8595f/xMXFERcXh8fjYdOmTcyYMYOwsDCrY4mIiIj4lQy3icOThSM4OMe46QHD/t+2PTmZKmPH4UxMBCDxqis5ePPNSN6lelN5P/V9krxJ3BtxL9Ud1a2OJCJFWOXDK2gYPwW76QYgzVmKVTUeJTmsqsXJRCQQpKWl0bVrV5KSkog6x4foOoM1gAwcOJCBAweSnJxMdHQ07du3P2cB+COXy8XChQtp164dTqfT6jhSTKkOxWqqQbFacavBjVsOsnbqGpre2Jjazcpmj2c1b87uHj0JubgeNd5+G5s+3L4gb6x5g4PJBwGYnj6dUdeMolm5Zqfdt7jVoBQ9qsGi4HrMPbdhzumJkbqfMNdhWm97Hc+N72LWC/wPvFSDYrVAr8Hk5Nyv8awGawBzOp0BWeAnBPrxiX9QHYrVVINiteJQg7/+lcCS11/D5kpg6YfHCA1vR43LSwPgvOgiqs2YTlDFihhBQRYn9X9PN3uahLQEftzzIxmeDB5d/iij24zm6opXn3FOcahBKdpUgxardiX0Ww6fdIM9v2K40nB8fj+0/AfaPA+2wL/0jGpQrBaoNXg+xxT4rzQiIiIiInmU5fYyfsJkbK4dQBaZKZ/z/YSlxP99JHuf4OrVT2muerOyCjdogAi2BzOmzRjaVG4DQKYnk0eWPMKy+GWW5hKRIi6qPPT6Dhp2/W9sxVswqytk5P4MNBGRvFKDVURERETkDIIcNno92IeEkAq+ATODjKNz+Oa9ZezdcvS0c1z797PjtttJnDWr8IIGkCB7ECNbj6Rd1XYAuLwuHlv6GIt2LrI4mYgUac4QuGUsdHgdjOOtjk3fw4bZ1uYSkWJBDVYRERERkbNoUbcC7R59jv3BZXwDZhppRz7lqzErOLgrJce+nqNH2XHPPWRu3sy+V4aSvGCBBYn9n9PmZHir4XSq3gkAt+nmyeVPMm/7PIuTiUiRZhhw5YPQ/TMIKQH1b4Ur7rc6lYgUA2qwioiIiIicQ8fLq3FFv2c45CzlGzBTOHZoFl+O/pHEfcey97OXKEFUJ19TEK+XhCef4tjq1RYk9n8Om4PXr36dm2reBIDH9PDMimdYf3C9xclEpMir2Rb6LYOb43xNVxGRAqYGq4iIiIhILtx5dR1q9HicREcJAExvEsl7ZzH37Z9IPpyevV+ZJ54g+mZfU9DMymL3wIfI+PdfKyL7PbvNztAWQ7ntotsA6HJRFxrENrA4lYj4hZLVISg859j2H2Dug+BKP/0cEZE8clzI5OXLl1OjRo183Xf//v0XEklEREREpMD0bd+QkcceJvnT0UR5UjC9h0ncPZM/l1fgqi71ATBsNsq/+iruxESO/bACb0oK8X36UnXmTIIqVbT4CPyPzbDx0pUvcUW5K7i++vUYOhtNRPIicQfM7gnpR+DA33DXdIjWa7KI5I8LarBmZGSwY8eOs+5z4g1QbvY9eX8RERERkaLo8VuaMjh1AMe+H0u45xiEhdK4Y7Uc+xhOJ5VGj2Znr95krF+P++BB4vv0oeqM6ThKlrQmuB+zGTZuqHHDKeNu021BGhHxS4e3gjvTdzvhd5jYGu6aBlWaWRpLRAJDnpcIME0T0zRzvV9u9j2xv4iIiIhIUWUYBi93a0Vy6/sIrn0FD733FsFh4afsZwsLo/KE8QRVrw5A1o4dxA94AO+xY6fsK+fv38R/GZMyhhV7VlgdRUT8Qa1roc9CKFHVt33sAEztDL99ZG0uEQkIeTqD9eWXX87vHFIAXC4XLpfL6hj57sQxBeKxif9QHYrVVINiNdUgvNarDYbRFsj5c0jcl0ZkqRAcThtERFB+/Dh239sDz4EDZKxfz6GZs4jp2cOq2AFhy9Et9FrQi0xvJs//9DzTIqdRJaqK1bGkmNHroB8qWRt6L8T+xf3YdqwArwu+ehhPwjq81w0Fu9PqhOdFNShWC/QaPJ/jMkydMhow4uLiiIuLw+PxsGnTJmbMmEFYWJjVsURERESKjS1bM7Fv2EhEpabENsrEOP59saC9+6g8fjxJTZtyqFNHsOlasxfCa3qZlTaLv11/A1DGVob+kf0JNoItTiYi/sAw3dTfM4uaBxdkjx2MqMfaagPJckZZmExEipK0tDS6du1KUlISUVFnf21QgzUAJScnEx0dzaFDh85ZAP7I5XKxcOFC2rVrh9PpX58wSuBQHYrVVINiNdXgqb5a8TfbJ72F4U3EHlSfuq260rZHPQyb7xoD7n37cJQrZ3HKwHE07Sh3f3U3B7wHAGhTqQ0jWo7AZqh5LYVDr4P+z/hjOvZ5T2F4sgAwS9fF3Wc52OwWJ8sd1aBYLdBrMDk5mdjY2Fw1WC/oIldStDmdzoAs8BMC/fjEP6gOxWqqQbGaatAny+1l1tLfaepNwgA8WX+xccWnBId1p3XXOhiGgbNy5VPmeZKTsQfgB+KFoURYCbqGd2VyxmRSXaks3b2Uqf9MpX/D/lZHk2JGr4N+rEkvKHcxfNIdUvdjtHkOZ3CI1anOm2pQrBaoNXg+x1TkPt4NCwvDbrfjcKj3KyIiIiL+IchhY+Sjd/JTlU6Y+M5Y9WT+wboFs/n5i62nvZBr2u+/s7V9B5K++qqw4waMWHssr7d4HeP4zzzujzh+2P2DxalExK9Ubgr9lsGN78DFN1udRkT8VJFrsJqmmf1HRERERMRfVCkVxutPdGNl+WuzxzwZq1nz5af8Nn9njn0zt29nV+/78Bw9SsJzz5O64sfCjhswWlRowcOXPwyAickzPzzDjqQd1oYSEf8SVQEa9zx1/NcPISO58POIiN8pcg1WERERERF/VbdcFC881oMfy7TKHnNnrOTHWXPYsGx39lhQtWpE33rL8R3c7B40iPQNGwo5beDo06AP7aq2AyDVlcqgpYPIcGdYnEpE/NqvU+HrR+D9dnB4q9VpRKSIU4NVRERERCQfNa5akkEP9eLnUldmj7nTl7Hkw8/495e9ABiGQbkXXiCyfXsAzLQ04vv1J3P7dksy+zvDMHi1xavUKlELh+Hgnrr3EGwPtjqWiPirrGOwZJjv9sGNMKkNbFlsbSYRKdLUYBURERERyWet65ShZ//erC7ROHvMnbaQAzs3ZW8bdjsVRgwnrEkTADyJicTf3wfX/gOFnjcQhDnDGNNmDJM7TObuundjGIbVkUTEXwWFw33zILaObzsjCabfDj+9B1rOUEROQw1WEREREZECcGPDCnTu1ZvfoxoCEFHzSq6+o2WOfWzBwVQaG0dw3boAuBISiO/XD09KSqHnDQRVoqrQuGzjc+8oInIupWpCn0VQu5Nv2/TCgudh7gPg0hIkIpKTGqwiIiIiIgWkx5XVaNm9N3W6PkS/Yc9h2E59+22PjKTyxAk4K1YEIPPff9n94EC8mZmFHTcgLYtfxvYkLb0gInkQEgV3z4BWT/03tm4mTOkEyQnW5RKRIkcNVhERERGRAtT/mlrccHPHU76yvnntXg7t9p2p6ixThsqTJ2GPiQEgbc0ajq38qdCzBhKv6WXcH+N4eMnDDFo6iNSsVKsjiYg/stmg7Qtwx1RwhvnGEn6Dia1h7zork4lIEaIGq4iIiIhIIZvw7vd8PfJJPh8xj6P70wAIrl6dyhMnYIuKosKI4US2bWNxSv+W6clkwc4FAGxP2s5zPz6H1/RanEpE/Fb9W+H+BRBdxbdtc0BkeWsziUiRoQariIiIiEghMU2TkTOXkLxyEqY3iZT9n/DZ8AWkHPGt5xfaoAG1Fi0k+sYbLU7q/0IdobzT5h0igyIBWBq/lAnrJ1icSkT8WrkG0G8p1GoHd0+HiDJWJxKRIkINVhERERGRQuLymKxNdLA/qJRvwMzg6J6ZfDZ8MWnJWQDYo6JOnbdvX2HGDBiVoyozvNVwDHzLM4z9YyzL4pdZmklE/Fx4LHSfAxUuzzmenghpR6zJJCKWc1gdQAqOy+XC5XJZHSPfnTimQDw28R+qQ7GaalCsphrMGwOIu7cJvTIycfw+k9JZB8A8xuGd0/hsuJNbn7qa4DBnjjnJX8zlwNChlBsxnIhrr7UmeBGU2xpsVqYZDzV8iHfXvQvAsyue5cP2H1I9unqBZ5TAptdByeZ1Y5/dCyNxO+47PoYyFxfK06oGxWqBXoPnc1yGaZrm+T7Bfffdd75Tcu2jjz7C6/ViGAYej6fAnicQxcXFERcXh8fjYdOmTcyYMYOwsDCrY4mIiIjI/5PigvHrXFyz/StKunxnPBm2EoRXvJ0yVxrYjp8GEbp1K5UnTgLA63Cw5/77SK9Rw6rYfss0TT5J+4Q/XX8CEGuLZUDkAEKMEIuTiUggqLP3c+rumwuA2xbMb1X7sbdEE2tDicgFS0tLo2vXriQlJRF1mm8YnSxPDVabzXbKVVDzk2maarBegOTkZKKjozl06NA5C8AfuVwuFi5cSLt27XA6neeeIFIAVIdiNdWgWE01eOESjqbTY+xSrtn0KdHuowAYtlJUuawPnR9uisNpw/R6OfDCC6R8/Q0AtshIKk6dQnDt2hYmLxrOtwbTXGn0WtiLLUe3ANCqYivebvU2NkOrpkne6HVQsiXvwf5pD2z71mUPeVo+hbflU1CArzGqQbFaoNdgcnIysbGxuWqwXtASAXnozUohcjqdAVngJwT68Yl/UB2K1VSDYjXVYN5VLe3k/Qfa0vNdNx22ziHck4LpPcyudVNZ+mEo1z9wBQAVX3uN+KNJHFuxAm9KCnsHPEDVmTMJqlTR4iMoGnJbg9HOaN5p8w53f3s3yVnJ/H7wd/am76VadLWCDykBTa+DQqlqcP98+OoR2DAbAPuKEdgP/gO3jofgyAJ9etWgWC1Qa/B8jilPDdZWrVoV6BmsIiIiIiLFQa0ykYzrfy393nNxw87PCfWmYXr2U6L0gex9DKeTSqNHsbP3fWSsX4/74EHi+/Sh6swZOGJiLEzvf05c9OrtX99mVOtRVImqYnUkEQkUzlDoMhHKNYBFL4PphY3fwOR2cM8MKKnlXUQCWZ4arMuWLcvnGCIiIiIixdOllUrwdp/reHSCh1v3zOXia7tw1e3X59jHFh5O5Qnj2dm1G1nbt5O1Ywfx/QdQdeoUbFpz/7y0qNiC5uWbY7fZrY4iIoHGMKDFI76LXM25DzKT4OA/MLEN3DEVaraxOqGIFBAtOCQiIiIiYrGrasXyZp923DliHDfff89p93HExFBl8iQcpUsDkLF+PbsHPYoZoFfuLUj/v7lqmiaZnkyL0ohIwLnoOui7BGKPr5edcRQ2zLE0kogULDVYRURERESKgDZ1ylCzYmyOMdM0Wfj+UtZ+tw0AZ8WKVJ48CVukbz2/tF9/JXPLlkLPGkjS3ek8++OzPLHsCbym1+o4IhIoYmtBn8VQuyNUaASdR1qdSEQK0AVd5EpERERERAqGy+1h5LNTce76EnvQxQSFDuTSNpUJqVOHymPj2PP0M1R6Zwwh9epZHdWvDVoyiJ/3/gzA2D/G8tDlD1mcSEQCRkgU3D3TdwarMyTnfV4v2HTOm0igsORfc1paGj/88AOffPIJX331FevWrbMihoiIiIhIkZTl9vLIB8ux7/oK8OLJ+pMlUyay8ecEAMKaNKHm/HmENmhgbdAA0OuSXtgM369FE9ZPYPHOxRYnEpGAYrNBWMmcY4e2wLgrYfdaazKJSL7LlwZrYmIiCxcuZNasWXzzzTfs2LHjtPsdPXqUAQMGULp0adq0aUPXrl259dZbadSoEZUqVWLs2LGYppkfkURERERE/JbTbhBTujTzS7fFxADAk/k78yd8wLbfDwJgCwrKMcc0TTI3by70rP7uqgpX8Vijx7K3n/vxObYe3WphIhEJaBnJMOseOLgRpnSCP2ZYnUhE8sEFNVgTEhK48847KVu2LB07dqRbt27cfPPN1KxZk6uvvjrHmam7du3iiiuuYNKkSaSnp2OaZo4/CQkJPPzww9x99914PJ4LPjAREREREX9lGAZDb76EOle2ZHGp1tnj7vRf+ObdD4j/+0iO/U2vl/2vvc62W7uQuuLHQk7r/3rW70mn6p0ASHOnMWjpIJKzki1OJSIByeOC8NLHb2fB3Adg3rPgcVubS0QuSJ4brDt27ODKK6/ks88+w+12Z595eqJh+tNPP9GyZUs2bNiAaZrccccdbNu27YyPZxgGpmkyZ84cXnvttbzGEhEREREJCHabwdt3NSS28dUsL3l19rjr2Armvv0he7cmZY8lfTGXxI8/Breb3YMGkb5hgxWR/ZZhGAy5agh1YuoAsDN5J8+ueFYXvRKR/BdeCnp8CU36/Df2y1iY1gXSjpx5nogUaXlusPbu3Zv4+PgcY///6/2pqak89thjfPHFF6xZswbDMIiIiOCRRx5h9uzZLFiwgE8++YSBAwcSFhaW3WR97bXXOHToUF6jiYiIiIgEhGCHnfHdG2Nv0JKfSjTLHs9KWcznw6dxMD4FgOhbbiayXTsAzLQ04vv1J3P7dksy+6tQRyij24wmOjgagB92/0DcH3EWpxKRgGR3QueRcMNosB2/9vj25TCpDez/29JoIpI3eWqwLl++nOXLl2c3RFu3bs3cuXPZuHEj69evZ/z48VSvXh2ApUuX8u677wJQqVIl1q1bx+jRo7n99tu57rrruOOOO3j33Xf5/fffqVChAgBZWVnMmKF1SEREREREwoMdTOnVhJS6rVgb3Sh7POPoPL599zNM08Sw26nw1gjCmjQBwJOYSHyfvrgOHLAqtl+qFFmJEa1GZF/0auL6iSyPX25xKhEJWFf0hp5fQ1isbztxB0y+Dv752tJYInL+8tRg/eSTT7Jv33LLLSxevJibbrqJ2rVrc8kll9CvXz9WrVpF5cqVAfjhhx8wDIO3336batWqnfYxa9WqxciRI7O3ly1blpdoIiIiIiIBp0RYEB/f34z4GtewPrLB8VE7TTrXxjB8F8GyBQdTKe49guv4vubu2rOH+L798KSkWJTaP11Z4Uoeb/w4AC0qtOCyMpdZG0hEAlvVq6DfMijf0LftOgaze8BhXWxPxJ/kqcG6Zs2a7NsjRozIflN3stjYWJ555pnsNVnDwsK45ZZbzvq4Xbp0ISwsDID169fnJZqIiIiISEAqGxXCtD7N+atqW/aWb8y1Dz9H/WuuyrGPPSqKyhMn4qxYEYDMf/9l94MD8WZmWhHZb/W4uAfDWw0n7tq47CUDREQKTInK0HseXHK7b7vN81CqprWZROS85KnBunPnTgCqVatGzZpn/kffvn17wLdofN26dbHb7Wd9XIfDwcUXX4xpmlqDVURERETk/6kWG86s/lcy5M0Xuezqpjnu83i8ZKa7cZYtQ+XJk7DHxACQtmYNCU89jenxWBHZLxmGQafqnbDbzv77i4hIvgkKg9smw13ToOUTVqcRkfOUpwZrUlIShmFkLwFwJiffX7JkyVw9dokSJQDfBbJERERERCSnWmUiCA925BhLTXMx44XxzHnjO7Iy3ARXr07lCeMxjn87LOPvv3EfPmxF3IBxMO0gn2/+3OoYIhLIDAPq3ej778l++wi2aT1okaLMce5dTuVyuTAMI/vr/GcSHBycfftcZ6/+//1M08xLNBERERGRYuVIaibjHh9BSNIvYIQw9+0gujzVjtBLL6XSmDEcHDOGSmPjcJYpY3VUv7Xu4DoeX/o4B9IPEBkUSbuq7ayOJCLFxbbl8PWjvtsdX4em/U5twIqI5fLUYBX/4HK5cLlcVsfIdyeOKRCPTfyH6lCsphoUq6kGi4ZMt5d7xv/I5Rk7CAEwM4hfP4WvxgTR+aEWBDdvRsWm08BmC7i/q8Kswd/2/caB9AMAPP/j81QKq0StErUK/HmlaNProBQG++/TsJnHl3j5/mm8CevwdBwOjmDVoFgu0GvwfI7LMPNwqqjNZsMwDDp06MB3332Xb/sCdOrUifnz52MYBh6tE3Ve4uLiiIuLw+PxsGnTJmbMmHHOs4xFRERExL/9sNfg621u7kj4mpKug75BWxTRtboQ29h5yolOhttN8J4EMqpWKfywfso0TeakzWGdax0AJW0leSDiAUJtoRYnE5GAZ3qpt/czau//OnvoSFhNVtcYRKazhHW5RIqBtLQ0unbtSlJSElFRUWfdV2ewBpCBAwcycOBAkpOTiY6Opn379ucsAH/kcrlYuHAh7dq1w+l0Wh1HiinVoVhNNShWUw0WHdcDFZdsZeIik7sSviTKfQS8ySRvmUuVqg/TusflGMe7rN7UVPY++hgZv/9OhQnjCb3iCmvDX4DCrsG27rbcv/B+NiZu5Ij3CEvDlzLmmjG6EFYxptdBKTw34P7rc+zfDMJwp1MybSsddrxG5q0fMP/PQ6pBsUygvw4mJyfnel81WAOY0+kMyAI/IdCPT/yD6lCsphoUq6kGi4bH2tchOdPD7B883L33S8LdSZjeo/y5eCwhEU/Q6u5LATg4bTrpq1YBsPeRQVSdNo2QOrWtjH7BCqsGnU4nY9qO4e5v7iYxM5Gf9v7EhL8mMKjRoAJ/bina9DooheKyu6BMHZjVDZJ3Y6TuI3jGrVSq1Aun83rVoFgqUF8Hz+eYLqjBunr1atq2bZuv+65fv/5CIomIiIiIFDuGYfDSDReTlO5i9pobuTvhS0I9KZjew6z9agzBYU/S7KZ6xA7oT/r69RxbsQJvSgrxfftSbeYMnBUrWn0IfqFCRAXeuuYt+i3sh8f0MHnDZOqVrEf7au2tjiYixUGFy6DfMpjdA3b9hOHJpPHOCbjX1YcrelqdTqRYu6AGa2JiIsuXLz/rPie+jpSbfUVEREREJG9sNoPht19K/3QXn5o3cFfClwR70zA9+/nt2w+44vo3sTudVBo9ip29epOxYQPuAwfY1acvVWdMxxETY/Uh+IWm5ZvyxBVPMHzNcABeWPkC1aOrc1HMRRYnE5FiIaI09PgSvn8Kfp2K2xYEJapanUqk2LPldaJpmgX2R0REREREzp/TbiOuayNqX1SdOeVvwGWEgD2CW58ZiN3he+tvCw+n8oTxBFWrBkDW9u3E9x+ANy3NwuT+pXu97txQ4wYA0t3pLItfZmkeESlmHEFww2g8Vz3GzzWfwqzawupEIsVens5g7dlTp56LiIiIiBRFoUF2Jvdswt0TPRytcx9PdG5I6Uo5lwBwlCxJ5cmT2XnPPbgPHiRj/Xp2P/oolePiMAJwDbX8ZhgGL1/5MrtTdnNb7du4pdYtVkcSkeLGMPC2eZ4j331ndRIRIY8N1ilTpuR3DhERERERySfRoU4+6d+ciCAHNpuR47605AwS96dT8aKKVJ48iZ3d78WbksKxH1aw94UXKP/66xi2PH/RrdgIcYTwYacPsRn6WYlIEWGasOx1KF0XLulidRqRYkXvBkREREREAlBUiPOU5urm7YeZ8sRLzBk2gr1bjxJSpw6V4t7DCAoCwLV3H2ZmphVx/dLpmqvp7nQLkohIsWeasHgILH8TPusDf39pdSKRYkUNVhERERGRYuDffcnMHPIqGcl/405fz5xhozkYn0x406ZUeGsEUddfT+XJk7CFhlod1W/N2zGPjp915N8j/1odRUSKG9OEY4eO3/bAnPtg47fWZhIpRtRgFREREREJcJluD/dNXcvK8KqcuKRs1rG1zH7lPY7uTyOqfXsqvj0S2/EzWeX8LdixgKeWP8WRjCMMWjqIpMwkqyOJSHFis8GN78Bl3XzbXjfM7gn/zrM2l0gxoQariIiIiEiAC3bYeeO2BmwrUZvlpVpnj2ck/8TMweNIOZJxyhz3oUOkrV1biCn9W6tKrbi41MUA7Endw9M/PI3H67E4lYgUKzYb3PQuXHqXb9vrgtn3wuaF1uYSKQbUYBURERERKQZaXlSa0Xddzp/R9fgppkX2eNqRpcx8eRJpyVnZY1k7d7Ljnq7s6tef9A0brIjrd0IcIYxpM4aSISUB+CnhJ8b8PsbiVCJS7NjscMs4uOR237YnC2Z1gy2Lrc0lEuDUYBURERERKSY6X1qe125twK8lLmVtiabZ4ykH5jNz8IdkHHMBcHjy+7ji4zHT0ojv15/M7dutiuxXyoWX461r3sJhOACY8ucU5m3X13NFpJDZ7HDrBLj4Ft+2JxNmdYVty6xMJRLQ1GAVERERESlG7mlahac71uHnmMasj2qUPX50z1d8MnQGrkwPZV94nrArrgDAk5hIfJ++uA4csCqyX2lSrglPNnkye/uln17SRa9EpPDZHXDbZKh3o2/bnQHfPgEet7W5RAKUGqwiIiIiIsXMA9fUpF+rGiwv2ZSNEQ2Oj5qkJf6CzWZiCw6m0tg4gmvXBsC1Zw/x/frjSUmxLrQf6Vq3KzfVvAmAdHc6g5YO4mjGUWtDiUjxY3fCbR9Anc4QXRm6zfE1XkUk36nBKiIiIiJSzBiGwbOd6nJnk8osjG3BtvC6GBGV6PXWcOxO3y/f9qgoKk+ahLNCBQAyN25k98CH8GZmWhndLxiGwUtXvkT9UvUB30Wv/vfj/zBN0+JkIlLsOILgjqlw33woWd3qNCIBSw1WEREREZFiyDAMXru1AZ0alOfyXg/wcNxoQiMjc+zjLFuGyu9Pxh4TA0Da6tUkPPU0psdjRWS/EmwPZnSb0ZQMKUlMcAz3X3I/hmFYHUtEiiNHEERXzDnmccGBjdbkEQlAarCKiIiIiBRTDruNsd0a0adVTZwhITnu27vlAEs+/JGgatWoPGE8RmgoACkLFrB/2DCdjZkL5cLL8W7bd/nkhk9oUq6J1XFERHzcWfBpL3i/Hez51eo0IgFBi2+IiIiIiBRjpzurcuHif/hzylt43cmYpsG1vVpQ6Z0xxD/wILjdgAGmCToj85wuLX2p1RFERHL6cRRs/MZ3++NboceXUOFyazOJ+DmdwSoiIiIiItkW/r2f+dOn4HXtBzOdP+a9w4+friWiZUsqvDaM0oMeoeyLL2DY9KtEXpimyfR/puuiVyJinasehmotfbczkuCjW2Dveksjifg7vSsSEREREREAMt0ehn7zN1/FNifFWdo3aB5j9edvs+abDUTfdBOxDzygtUTz6JjrGI8ve5w3Vr/Bkz88idvrtjqSiBRHQWHQ9ROocpVvO+MofHQz7PvT0lgi/kwNVhERERERASDYYWdq7yZERkYyq3xn0hwlATC9yayYPpz1SzedMif9z79IW7u2sKP6pZSsFH478BsAq/auYvSvo60NJCLFV1A4dJsNlZv7ttOPwEc3wf6/rc0l4qfUYBURERERkWw1Skfw4X1NcYRFMrP8DWTaowEwvYksmvQ6G3/enr1v6sqV7OrRg/gHHiTj31Obr5JTufByvN36bRyG71IYH/79Id9u+9biVCJSbAVHQrdPodLxi/ClHYYPb4QDG63NJeKH1GAVEREREZEcLqkYzeSeV+AOiWRm+Rtx2SIAMD0H+f69YWxbtweAxOkz8Kal4U1JIb5vX1x79lgZ2y80LtuYZ5o+k709+KfBbDyiZoaIWCQkCrp/BhUb+7bTDvmarIe3WptLxM+owSoiIiIiIqdoXqMUcV0bkRYcxazyN+K2hQHgde/jyxFDSdyfTMURwwm59FIA3AcOsKtPX9yJiVbG9gt31bmLW2vdCkCGJ4NBSwaRmKGfm4hYJCQaun8O5S/zbZeoAuGxlkYS8TcOqwNIwXG5XLhcLqtj5LsTxxSIxyb+Q3UoVlMNitVUg8VD64tK8vot9Xn68z/5tNwN3Ln3K+xmBrGVShMa5cDjcFL+vXfZ3aMHrh07ydq+nV39+lFx8mRsYWEFms3fa/Dpxk+zOXEzfx7+k4RjCTyx7Ani2sThsOlXNH/h7zUo/i9fa9ARDvd8im3pULzXDgF7GKi25RwC/XXwfI7LME3TLMAsUoji4uKIi4vD4/GwadMmZsyYQVgBv7EVERERkcC3bK/BFzvsVMjczy2eTdRu0wzD9t+X4RxHjlBl7DgcKSkAHKtThz09e4DdblVkv5DsTWZsylhSzVQAWgS3oFNoJ4tTiYiICEBaWhpdu3YlKSmJqKios+6rBmsASk5OJjo6mkOHDp2zAPyRy+Vi4cKFtGvXDqfTaXUcKaZUh2I11aBYTTVY/Ixdto1LK0Vzda1Sp9zn9Zq4Nm9mT69eeFN9zcLIm26kzKuvYhhGgeQJlBr8/cDv9F/cH7fppnpUdT7u8DFhTp0k4Q8CpQbFfxVKDaYdwf7to3javepbOkDkJIH+OpicnExsbGyuGqz6/kkAczqdAVngJwT68Yl/UB2K1VSDYjXVYPExqF2d046vX7KBXz7/jm7DHqXS2Dji+/TFzMoi5auvCSpThjJPPlmgufy9BptWbMr/mv6Pn/f+zLCrhxHuDLc6kpwnf69B8X8FVoPHDsOM22D/BmwH/oJe30KJyvn/POL3AvV18HyOSRe5EhERERGRPHl/0hIWThxKysEVTHvuLWwXX0aFt0aAYYDNRlC1alZH9At31rmTUa1HqbkqIkWL1w3uDN/tozvhwxshaY+1mUSKKDVYRURERETkvJimybuLNzP3jw2YZhoAqYd+ZvoL7xDSqi3lhgym0nvvUuL22y1O6h8MwzhlKYVMTyZur9uiRCIiQGRZ6Pk1lKzp207c7muyJu+1NpdIEaQGq4iIiIiInBeXx2TZpoNsiKzFqpKts8eT9i5jxkvjibz1NiLbtrUuoJ/bd2wfPb/vyci1I62OIiLFXVR56PUNxFT3bR/Z6muypuy3NpdIEaMGq4iIiIiInJcgh40PejahbrlI1kTX47cSLbLvO7xzHp8M+QCvx5tjTtK335K+YUNhR/U7Ge4Mun/Xnb8O/8W0f6bx9davrY4kIsVdVAVfk7VEVd/24c2+JmvqQWtziRQharCKiIiIiMh5iw5z8tF9TalSMoyVMZfyV1Sz7Pv2bf6SOa9Px/SaABz58EMSnniS+H79ydy+3arIfiHEEUK/S/tlbw/5eQh/H/7bwkQiIkB0JV+TNbqKb/vQv/DRTXDskLW5RIoINVhFRERERCRPykSFMO3+ZpSJDGZJycvZHNko+774DbP5ctQcvC4XKYsWA+BJTCS+T19cBw5YFdkv3FnnTm676DbAtxbro0sf5UjGEYtTiUixV6IK9PwKoir5tg/8Db+MtTaTSBGhBquIiIiIiORZlVJhfHR/U6JCncwr1ZSd4Q2O32OydfXHbP9zG5Xi3iO4Th0AXHv2EN+vP56UFOtC+4Hnmj1Hw9INAdh7bC9PLn8Sl9dlcSoRKfZKVvc1WSMrQIM7ofVzVicSKRLUYBURERERkQtSt1wUU3o3ISTIzlelW5AQVheAS9reSo3LamOPiqLyxIk4K1QAIHPjRnYPfAhvZqaVsYu0IHsQb7d+m9jQWADW7FvD22vftjiViAhQqib0XQy3jge7w+o0IkWCGqwiIiIiInLBGlctyfjujXE6bPxQoz0Nuz9Gh/69MQwDAGfZMlR+fzL2mBgA0lavJuGppzE9HitjF2llwsowqvUoHDZfA2PaP9P4autXFqcSEcF34SubPefY4a2QkWRNHhGLqcEqIiIiIiL5onWdMsR1bcSnD7TguhuvPeX+1KBSVJ4wHiMsDICUBQvYP2wYpmkWdlS/cVmZy3iu2X9fwR3y0xC2Ht1qYSIRkdM4+C9M6QTTboOMZKvTiBQ6NVhFRERERCTftK9fjmqx4TnGTNNkwaTv+fDJB9h0ECqNGQMO31mZiTNmcnjCRCui+o07at/B7bVvB6BbvW5UjapqcSIRkZN4vTC7J6Tuh91rYPodkJlqdSqRQqUGq4iIiIiIFBiP12TYG3PYsGg8pjeJxZNfI95RmgqvDQPAFh1NWNOmFqcs+p5r+hxjrx3L41c8nr1kgIhIkWCzwW2TINS3BAzxv8CMOyHrmLW5RAqRGqwiIiIiIlIgstxeHprxGx8dtpPpKAmA6U3l+3df4XDlBpQbMoRq06cR1uhyi5MWfU67k5aVWlodQ0Tk9Mo1gB5fQki0b3vnSphxF2SlWZtLpJCowSoiIiIiIgXCYTMIC3KQ5QhmWvnOuOzHm6yeJL5862WONbya4Fq1LE7pv/469BfzdsyzOoaIiE/5hnDvXAg+3mTdsQJm3g2udEtjiRQGNVhFRERERKRA2GwGb97WgOvqlSXNEcqM8p3x2H2/eHvdR5jz6gvs334ge3/T6+XgO++S8e8mqyL7jS+3fEmP73vw/Irn+fPQn1bHERHxqdgI7v0CgqN829uXw6yu4MqwNpdIAVODVURERERECozDbuO9rpfTrHpJkp0RzCp3A15bBAAe1wFmvfwChxMSMV0u9j77HIfGjiW+b19ce/ZYnLxoW3dwHVneLLK8WTy69FEOpR+yOpKIiE+lxtD9MwjyvdazdQl80h08bmtziRQgNVhFRERERKRAhTjtTO55BZdUjOJIUBSflrsB0xYGgDszgRnPv0DSvkQyt23zjR04wK4+fXEnJloZu0h7tumzXF7Gt3bt/rT9PLn8SVxel8WpRP6PvfuOjqJswzj8my0pmwYhQOhdehdUEAURBGyI2LD3zwoi2AsoVkBBjb2AChZsWECNCFgQEURBBAJCgAQIBEI2yaZs+/5YCSAQEkgy2c19ncM5O7szs/fg44Z98s77ivyrUU+49COwRwW2G58AVi3QJ6FLDVYREREREalwMRF2pl3dk+YJUewIr8mndc/Cb4QDUOTaTNqGjTR65WXCmjYNPLdxI1v+9z98Li2Qcih2q51n+j5Dncg6ACzLWMbE3yaanEpEZD9NToJLZ8HACXDKWLPTiFQoNVhFRERERKRSJESH8851J1AvLoL0iFp8VfcssERy+vVj6HDqCdji42n0+uvYatcGoODPFaSNGoXfrZGZh5IQmcCz/Z7FbrED8N6a9/hs/WfmhhIR2V/T3tDrNrNTiFQ4NVhFRERERKTSNKgRyTvX9qSmw050y1aMeO5VOp9+avHrYQ0b0Oj117BEB+buy/vhR7Y98CB+v9+syFVap9qdeODEB4q3H/3lUS16JSJV2/p58OlNmpNVQooarCIiIiIiUqla1onhvRtOZOb1J1Cvds0DXvN5fSz5fRv1nn8eIywMgOzZs9k5ebIZUYPCsFbDuKj1RQAU+YoYOX+kFr0Skapp3Xfw3iXw50z49Ebwec1OJFIu1GAVEREREZFK1yYxlpgI+wHP5ed7mPnwq/z22SQ+m/0jiU8/DZbAV5Zdr79B1vsfmBE1KNzd42661ekGQFZBlkaxikgV5f/3D/DXR/DZzWqySkhQg1VEREREREyXne9m9OQvyFj3JQA7Ny5k7i/rqPvggwCEtWxBdN9TSzpFtWa32pncdzIdanXgrUFv0bdRX7MjiYgcrNUAuPAd+HfuaFa8D5/fBj6fublEjpEarCIiIiIiYqpCj5dLXl3M3Nww/qrRt/j5rWu+Zv6mXOo99hhN330Xe2KieSGDQEJkAjPPnEnn2p3NjiIicnitB8GF08FiC2z/MQO+uF1NVglqarCKiIiIiIipwm1WhnatD8D8mm1ZF9u7+LXU5Z/xU1o+1ho1TEoXXAzDOOi5nKIcE5KIiJSgzZkw/C0wrIHt5e/AV3eoySpBSw1WEREREREx3Q2ntOB/p7YA4Otandgcc0Lxa2sXvce3r31cvO1zuci49z7smVrIqSRen5epv09l6OyhWvRKRKqedufA8Df2NVmXTYO5Y8HvNzWWyNFQg1VERERERKqEuwe15pKejQCYXasrGdHdil9b+d00Frw7B09WFpuuupqcL7+kwZtv4c12mhW3ykv6I4nXV77ODtcORi8YjdvrNjuSiMiB2p8Hw14F49/21LJpsH2FqZFEjoYarCIiIiIiUiUYhsGEoR0Z0jERDIMPE3qSFdXx31f9LPviFZYmL8FfWAhA2K5dZNx/H37dUnpII9qOoI6jDgDLdyznqd+eMjmRiMghdBwOQ18GazhcMA3qaR5pCT5qsIqIiIiISJVhtRg8e1EX+rRKAMPg3YTe5DraAmCxhlGvXSMavfQiln/nZHUt/IFdr7xiYuKqKyEygan9phJmCQPgg7Uf8Mm6T0xOJSJyCJ0vgpF/QNuzzU4iclTUYBURERERkSol3Gbl5cu606VRDbAYTKt9Ct463bl4/JM06dAOe/36JD71FP5/F3Ta+dzz5P70s7mhq6gOCR148KQHi7cnLJ7Anzv/NDGRiMhhxNY/+Lm0pZqTVYKCGqwiIiIiIlLlRIXbeOuqHrSqE8053Rpyx5SHqdeqRfHrjl4nsWvggMCG38/WO++kKC3dpLRV29CWQ7mkzSUAuH1uRs8frUWvRKTqW/wyvN4fFjxhdhKRI1KDtYrYvHkzl112GW3atCE2Npbo6Gg6dOjAI488Qk5OjtnxREREREQqXc2oMD688SSevbALduuBX10K8or4294U6yl9APBmZ5M+ciS+f+dnlQON7TGW7nW7A7Ajfwd3zL9Di16JSNW1/S/4+u7A44VPwQLNIS1VmxqsVURGRgZpaWmcd955PPnkk0yePJmTTjqJCRMm0K9fP9xu/eNHRERERKqfmlFhWCzGAc8tXZjC9DEPkbtlHqua1cfWuDEABatWkTHhMTNiVnl2i53Jp04mMSoRgD92/sHU36eanEpE5DASO8Cg/ZqqCx6HHyaZl0fkCGxmB5CAHj16sGDBggOeu/HGGznuuOO46667SE5OZsiQIeaEExERERGpIv7ZmcuXs1YTk7sJgA1/LKXZNZcT8eRk/AUFuLdtw1dUhCUszOSkVU+tyFpM6TuFK+ZeQfMazbmk7SVmRxIRObwT/wd+L3xzX2D7+0fBYoOTR5kaS+RQ1GCt4po1awZAVlaWyUlERERERMxV6PFyxRtLOMXvIN5xOu68LwH48evZnDd2NOG79pBwy80YVqvJSauu9gnteXnAy3RI6ECkLdLsOCIiJTvpFvB5Ifnfxfq+ezjQZO11q7m5RP5DUwRUMQUFBWRmZrJlyxa++uor7rvvPiIiIjjllFPMjiYiIiIiYqpwm5VHh7YHw8AadhzWsPYAFOXn8+PaldS6Vc3V0uiR2EPNVREJHr1vh/4P79v+9n5Y/JJ5eUQOISQbrC6Xi7lz5zJhwgSGDRtGkyZNMAwDwzAYN25cqc6Rk5PDuHHj6NixI9HR0cTFxdGjRw8mT55MUVFRhWV//fXXqV27No0bN+ass87CYrEwe/ZsGjVqVGHvKSIiIiISLE5rU5cmtRwA2Bx9yQ2LBWDr2r/5bfbHB+zrycrC7/VWesZg43K7SPojiSJvxX3PERE5Jn1GQ78H9m1/fQ8sfdO8PCL/EZJTBCxZsuSY5ivdtGkTffv2JTU1FQCHw0FhYSFLly5l6dKlzJgxg3nz5lGzZs2DjvX7/RSWcuVSi8VC2H/mhho6dCht2rQhOzubRYsWsXDhQpxO51Ffi4iIiIhIqKkTHc62jAIMI5xv4k9jWMZsDL+fRbNm0KRTVxJbtMK1fDnpI0dRY/hwat9+m9mRq6wtzi2MXDCSdVnryMzP5OGTHj7yQSIiZjh1LPg8sPBJiKwJ9buZnUikWEiOYAWoWbMm/fv3Z+zYsbz33nskJiaW6jiPx8PZZ59Namoq9erVIzk5mby8PFwuF++//z4xMTEsX76cyy677JDHb9q0icjIyFL96dbt4A+Dhg0bcvrpp3P++eczefJk7r77bi644AK+++67Y/r7EBEREREJGca+h9sj6vFbXODf1T6vlznPTyI/LY3NV12NZ8cOMl98kZz/LCYr+zjdTjY7NwPwUcpHzEqZZXIiEZES9L0H+j8EV34B9buYnUakWEiOYO3Tpw+7d+8+4Ll77rmnVMdOnz6dlStXAvDxxx9z0kknAYHRphdddBE+n48RI0YwZ84c5s2bR//+/Q84PiEhgbfeeqtU73WoEbD/NWzYMCIiInjrrbc4/fTTS3VeEREREZHq5Lca3WlVlEZNVwZZ29L5bWEy7W+7lR2TJgOw9a67afbxR4Rp2q2DtK/VnodPepj7fgqs0v34r4/TqkYrutTpYm4wEZFDMQzoc6fZKUQOEpINVusxTGw/ffp0APr161fcXN3fxRdfzP3338/GjRt5++23D2qwRkdHc9VVVx31+/+Xx+PB7XaTlZVVbucUEREREQkVPZrU4NfNe/gy/jQuK/yI1j1OoMe5w4mIiib/zz/JSf4On9NJ2u0jafreTCwREWZHrnLObnE2f+/6m3dXv4vH5+GOBXfwwVkfUMdRx+xoIiJH5vPB3LHQsAd0vtjsNFJNhewUAUfD5XLx888/AzB48OBD7mMYBoMGDQLg22+/Lbf3zsjIOOTzr7zyCl6vlxNOOKHc3ktEREREJFQ8ek47IuwW8iJqYh8+ljNH3kVkdAyGYVDv8ccJa9oUgMLVq9k+/hH8fr+5gauo0cePpkdiDwAy8zO5Y8EdWvRKRKo+nw++HAm/vQ6f3QQrNM2JmCMkR7AerdWrV+Pz+QDo0KHDYffb+9r27dvZvXs38fHxx/zed999N3///TcDBgygSZMm5ObmsnDhQr744gtat27NyJEjj/k9RERERERCQa9hLclzFvDb0t9oWjuKicM706puNG0SYw/YzxoTQ8Pnn2PjhRfhz88n+9NPiezShZoXXWhS8qrLbrEz6dRJXPzlxWzL28aKnSt4/NfHGddrnNnRREQOzzDAGh547PfBpzeAxQIdzjc3l1Q7arDuZ+vWrcWPGzRocNj99n9t69at5dJgHTZsGLt27WL69Ons3LkTm81Gy5YteeCBBxgzZgyxsbGHPbawsJDCwsLibafTCYDb7cbtdh9ztqpm7zWF4rVJ8FAditlUg2I21aCYqVYjB7FuO5GbvHi8Hga1qw0cXI95e7LYuWcXdcaPI+OuuwHYPmECtuNaEVHCgIrqKsYaw6Q+k7gm+RoKvYV8vO5jWtdozfBWw82OViXpc1DMphr814DHsXjdWH+fBn4f/o+vx+s38Lc52+xkIS/Ua7As16UG635ycnKKHzscjsPut/9r+x9zLM455xzOOeecozr2iSeeYPz48Qc9/+2335Z4HcEuOTnZ7AgiqkMxnWpQzKYaFLMdrgadm1PJ/O1H/B43jQYPo0Hv3tT8+Wdwu9n4v5tIvXM0/vDwSk4bHM4OP5uPXB8BMPuP2USmRGIYhsmpqi59DorZVIOAvy+da22k6a6FGH4vlo+v47dmt7K9Rnezk1ULoVqDLper1PuqwRoC7r33XkaPHl287XQ6adSoEQMHDixx5GuwcrvdJCcnM2DAAOx2u9lxpJpSHYrZVINiNtWgmO1wNVjk8fHSwg2krfuNRoUFABSsWk7XZ59h+43/o3DVKhrceSdtzxtqUvKqbwhDiPg9AofNwQ0db8BiaOmOQ9HnoJhNNfgf/iH4vhyFZcVMLHjpuelFvMe/hf+4QWYnC1mhXoN77xAvDTVY9xMTE1P8uKQu9f6v7X+MWcLDwwk/xG/f7XZ7SBb4XqF+fRIcVIdiNtWgmE01KGbYsclJnrOAgkwrVqvtgBq8+5M/+GR5OraI4/lfbDqGcyc7Nq7n97mz6TllCp7MnUS2b29i+uBwV8+7NGq1lPQ5KGZTDe5n6Atg+OHP9zB8bmyfXAMXzYDjBpqdLKSFag2W5Zr0q8j91K9fv/hxenr6Yffb/7X9jxERERERkYq36OP1zEn6i8zfHPi8/gNeu+HU5titBh6LnY9iTsWwWAH49dNZ7MjKVHO1lA7VXM0uzMbv9x9ibxGRKsJihXOToOMFgW1vEcwbDz6vubkk5KnBup+2bdtisQT+Sv7666/D7rf3tcTExHJZ4EpERERERMpHm8RYbu3XCoDtYbVZ17AXAH6/j7lJkyn8z51qzq+/oSg1tbJjBp3fM37nnM/O4cO1H5odRUSkZBYrDH0Z2g+DhOPgso8Dz4lUIDVY9+NwOOjduzcAX3/99SH38fv9fPPNNwAMHKgh5iIiIiIiVc1NfVvQJjEwldfXlnYYdZsBkL0jg/nTXgXA73aT8eRTpI8aRdrtI/GVYSGL6maLcwvXfnstuwt28+SSJ/k943ezI4mIlMxqg2GvwdVfQ0yi2WmkGtAcrP9x5ZVX8uOPPzJ//nx+/fVXTjjhhANenzVrFhs2bADgiiuuMCNiqbndbtxut9kxyt3eawrFa5PgoToUs6kGxWyqQTGTb7/b1N1Fbmz2A8eNGMCT57Xn/Fd+xeuz8G5Eb64I34a3sIBVC7+jceeutOjQhZyFCwEoTElh60MPU+fxxzTv6CEkRiZyUauLmLF2Bh6/h9ELRvPuoHep66hrdjRT6XNQzKYaLIWwWNj/78ftgswUqNfFtEihJNRrsCzXZfhDdBKdrKwsvN59c2x069aNLVu2MHbsWO66667i5yMiIoiOji7e9ng8dOvWjZUrV9KgQQOmT59O//798fl8fPzxx1x33XU4nU4GDx7MnDlzKvWajiQpKYmkpCS8Xi8pKSnMnDkTh8NhdiwRERERkXK189dICncHxoo0GJiDcZg7P7/cbCE5PdB87VW0hu7p8wGwhIXT5JyLiMzKpvELL2ApKgIg49xzye51UsVfQBDy+r1Mz5vOBk9gsElDa0Oujb4WuxF6i5qISGiyegs5YcMz1HRt4JcWY9gd3drsSFLFuVwuRowYQXZ2NrGxsSXuG7IN1qZNm7Jp06Yj7nfllVcybdq0A55LTU2lX79+pP47F5PD4cDn81FQUABA165dmTdvHjVr1izv2OXC6XQSFxdHZmbmEQsgGLndbpKTkxkwYEBIrlInwUF1KGZTDYrZVINipi+fW8HWddkAXP5kTyKjwg+5X6Hby7kvLeafnXng9zPS8gv2jH847Zr/0eqEwNRgud9+y/Y7xwQOsNlo8NZbRHbpXCnXEWyyCrK47JvL2Ja3DYBzm5/LQyc8VG1H/epzUMymGiwby4LHsf78DAD+sCi8l8zC37CnyamCW6jXoNPpJCEhoVQNVk0RcAhNmzZlxYoVTJo0iU8++YSNGzdit9tp3749l1xyCbfddhthYWFmxzwiu90ekgW+V6hfnwQH1aGYTTUoZlMNihkMy76Gnt1uO2wN2u12Jl7QmfNfWoQfgx/r9OXN0aOoUbt28T41zzyTor9Wsfutt8DjIePOO2n2ycfYEhIq/DqCTR17HZ477Tkun3M5Bd4CZm+YTYfaHbi4zcVmRzOVPgfFbKrBUup7N2z/E/6Zh1GUh+29i+CKz6Dh8WYnC3qhWoNluaaQXeQqNTUVv99/xD//Hb26V0xMDOPHj2flypXk5ubidDpZunQpd955Z1A0V0VEREREBLo1rsmNp7Tg9tNa8t5tpx3QXN2rzp2jcfToAYBnxw7SR9+J3+Op7KhBoU18G8b3Gl+8/dSSp1iWsczERCIipWSPgItnQPO+ge2iHHjnPEjXZ5gcu5BtsIqIiIiIiADcM7gNowe2Jtx28GSt65f+itfvp8Ezk7H923x1LVnCzilTKjll8BjSfAhXtrsSAI/fw50L7iS3KNfkVCIipWCPhIvfg2anBLYLnYEm69Y/TI0lwU8NVhERERERqXYK8nKZ8/wkZk98lJ8/eAdb7do0mDoFbIFZ1PZ88imerCxzQ1Zho7qP4oR6JxBuDWdMjzFEh0Uf+SARkaogzAGXvA9NTg5sF2TD2+fCthXm5pKgpjlYRUREREQkqJw7situt5s5c+ditZd9zMja7Tl88M2vxC7+CYClX35Ksy7dadytG3XvuovsL7+k4dQp2KroorZVgc1iY9Ipk9iWt422tdqaHUdEpGzComDEBzBjOGz+BQr2BJqs134LCa3MTidBSA3WEOZ2u3G73WbHKHd7rykUr02Ch+pQzKYaFLOpBsVsHq8HwwCPx1OmVexnLNnCY3PW4Pb6ufvEs3D99Bn4/cxNeoYRT0wh+uKLiB5+Ptjtqu8jiLJG0TK2ZbX9e9LnoJhNNXiMLOFw4Uys712IJf03fPU643XUBf19llqo12BZrsvw+/3+CswilSgpKYmkpCS8Xi8pKSnMnDkTh8NhdiwRERERkSpj7R6DF1cH5mINN3zcnPMFnp1bAYhu3Jy6vU87uGG79ytTGRq51dUfRX8QZ4mjma2Z2VFERErF5s3nuO2zWVNvGD6LFjWXfVwuFyNGjCA7O5vY2NgS91WDNQQ5nU7i4uLIzMw8YgEEI7fbTXJyMgMGDMBut5sdR6op1aGYTTUoZlMNitmOpQbv/2wVHy5LB6BfQzvdl71BoSsPgIE3jaJN71OL9/Xl5pLx4INEdu9OjcsuK78LCDEen4cpy6cwc+1MaobXZMagGSRGJZodq0Lpc1DMphoUs4V6DTqdThISEkrVYNUUASHMbreHZIHvFerXJ8FBdShmUw2K2VSDYoa/fkgna3suezaGYxlgLXMNPnB2e35Yt4vtzgLmp7k5tf8lFH7xOgALpr1Kk/adiK1dB19+PpsvvYyiDRvIW7CQqE6dcHTvXhGXFPQsPgsbnRsByCrMYsxPY5g+aDoRtgiTk1U8fQ6K2VSDFSA7HT69Ec6eCrVamJ2mygvVGizLNZV9RngRERERERET/fP7DlZ8n07uxjB8vrLfkBcbYefxYR2Ktyevj6TZiacAUJTvYm7SM/h8XiyRkcT0Py2wk8dD2qhRuHfsKJdrCDVWi5WJp06kQXQDAP7e9TePLn4U3TApIkEnOw2mnQmpP8L0s2H3RrMTSRBQg1VERERERKqd09rUZVjXQDMwp8DD3OiTiE2oA0Da6r/4fc7nANQeORLHiScC4N2ZSfro0fhDdDGPYxUXHsfUflOJtEUC8Pk/nzNzzUyTU4mIlJEtAuyBzzGc6YEma9YmczNJlacGq4iIiIiIVEsPnd2OhOhwAJL/cRIz6AowDJp360Hbk/sCYNhsNJg8CVvdugDkL13GjsnPmBW5ymsd35pHej9SvD3xt4n8tv03ExOJiJRRVAJc8TnUbhPYzt4C08+CPVvMzSVVmhqsIiIiIiJSLdVwhDFh6L6pAib+4eb8h55i6F0PEVWjZvHztlq1aDh1Cvw7F9vuadNwzp1b2XGDxqCmg7i6w9UAeP1e7lxwJ9tyt5mcSkSkDKJrB5qstVoFtvdsDjRZs9PNzSVVlhqsIiIiIiIStI51js9BHRI5q1M9WtSO4tUrjqdpu3YYhnHQfpFdulD33nuKt7fe/wCF69cf03uHspFdR9Krfi8gsOjVyPkjKfAUmJxKRKQMYurClV9A/L+LXGWlBpqsTv3CSA5mMzuAVBy32407BOeH2ntNoXhtEjxUh2I21aCYTTUoZvKzr6nqcXuOuQ7Hn9WGcJuFcLv1oHPl5zhx7sigbotWRA8fjmv5cnK++BK/y8WWW2+j0XszsURHH9P7h6rHTnqMy7+5nLTcNHa4drBpzyaaxzU3O1a50eegmE01WAkiE+DSz7C9ew5G1kbYvQH/tDPxXDYbYhLNTme6UK/BslyX4deyjiEjKSmJpKQkvF4vKSkpzJw5E4fDYXYsEREREZFytXNJJIW7AmNF6g/IwVJBw0Zc29LIWLwQ/H4aDzkfa0QkRlERjV98kfBt28lp356MCy/AFxFRMQFCwHbvdubkz2G4Yzixlliz44iIHJWIol2cvO5xoop2AvB3veGsSzzH5FRS0VwuFyNGjCA7O5vY2JJ/hqnBGoKcTidxcXFkZmYesQCCkdvtJjk5mQEDBmD/dx4skcqmOhSzqQbFbKpBMdNXSStJX7MHgMse74EjpnwbnF6fnx/XZ+L8/BVS/1gGQPPuPTlz1D0YhkHR5s3kzV9AjSsuP+R0AlI96HNQzKYarGTZW7C9cw6+4wbjG/AY6PM/5GvQ6XSSkJBQqgarpggIYXa7PSQLfK9Qvz4JDqpDMZtqUMymGhQzWPb7Umuz28q1BtfvyGHsRytYvnkPLw27lMh/1pGf42TDsiWs+XE+nfqfgb1FC6JatCi396xufH4fzkInNSJqmB2lXOhzUMymGqwkCc3hhoVYHfFY1Vw9QKjWYFmuSYtciYiIiIhIUKndOIYGrWsQXsuDxVK+X3L/2JLN8s17ABj33WZOvvrm4tfmT3+VrG2HXkHavX077owd5ZolFOUU5XD797dzQ/INWvRKRIJPVK2DR65uWwGu3ebkkSpDDVYREREREQkqJ53XkjNv7UjtnvnYwqzleu7zuzWgb+vaAGQ4C3l3Wwwd+58BgKewkDkvTMbr8RxwTN7ixWwcdj7po0bhLyoq1zyh5u4f7mZh2kJW717N+F/GoxnrRCSopS2DaWfBO0MhP8vsNGIiNVhFRERERET+ZRgGj5/XkejwwGxqHy5Nw37SUGok1gNg+/oUfv30g+L9/UVFbHvgQby7d5O/fDkZT080JXewuKP7HUTaIgH4csOXvLv6XZMTiYgcJZ8XPr0RCrNh25/wznmQv8fsVGISNVhFRERERET2U79GJPcNaVu8/cBX6+h74x0YlsDXp8WffMDWlDUAGGFhNHj2GYx/52nLevddsr/4svJDB4lWNVsxofeE4u3JSyezZNsSExOJiBwlixUungFRgbse2Loc3j0fCpzm5hJTqMEqIiIiIiLyH5f0bETvlrUASN+Tz1spXk46/xIA/D4fc1+YjLswMIdoZMeO1H3wgeJjtz30EAVrUyo/dJAY2HQg13W8DgCv38uYhWPYmrvV5FQiIkehdmu48gtwBH5ekL4UZgyHwhxzc0mlU4NVRERERESCyoKZa5n1+DK2/+igKN9z5AOOgmEYPDmsE5H2wByv7y7ejK/TadRr1Rp7RCQnnn8xtrDw4v1rXHABcecPA8Cfn0/a7bfhzdEX7MO5tcutnNzgZACyCrMYNX8U+Z58k1OJiByFOm3his8hMj6wveVXmHEBFOaam0sqlc3sAFJx3G43brfb7Bjlbu81heK1SfBQHYrZVINiNtWgmMm500XWNhdgpcjtJsxdMV9rEmPsjBnYike/CkwHcM+nq3jvutuIsFuJq5OI5z+LXdW65x4K/v6bwtVrcG/aTPpdd5M45dniqQXkQBNOmsBlX19GWm4aq3ev5uGfH2bCSRMw/rtCdxWlz0Exm2qwCqnVGkZ8jG3GeRgFe2DzL/hmXID3ovcgLMrsdBUm1GuwLNdl+LVsY8hISkoiKSkJr9dLSkoKM2fOxOFwmB1LRERERKRc7fwtksLMQFO1/uk5WOwV914+Pzy/ysqGHINTEn2c1dhHuPXw+9t276bJc89jzQ+Mxtw5aBBZ/fpWXMAgl+HN4JWcVyiiCIDzIs+je3h3k1OJiBydOFcqvdY/SZjXBcDO6HYsankXGPpFWzByuVyMGDGC7OxsYmNjS9xXDdYQ5HQ6iYuLIzMz84gFEIzcbjfJyckMGDAAu70C/zUtUgLVoZhNNShmUw2Kmea++BdbVmcBMOKx44mOjazQ90vdlcfOnCJ6NK15yNe3pqymXqs2xSMv8378kW233Ap+P1gsNP58NmFNmlRoxmA2b/M8xv40loGNB/LwiQ8TaavY/57lRZ+DYjbVYNVkbF2Odeb5GIVOvIMm4ut+tdmRKkyo16DT6SQhIaFUDVZNERDC7HZ7SBb4XqF+fRIcVIdiNtWgmE01KGYwLPtuIbfbKr4GWyXWoFXiwc8XFeSz4O3XWTnvG067+ka6DjobgBqnnYb7llvY9eab1H9sAlEtW1ZovmA3qMUgakfXpludbkEzPcD+9DkoZlMNVjFNesLln8LONVi7XkYJNz2EjFCtwbJckxqsIiIiIiISxMy5IS+nwM3udWtZOe8bAH549y0ad+hMrYaNAUi4+Sbizj2HsEaNTMkXbLrX1bQAIhJCGh4f+PNffj8E4S+S5Mg0CYSIiIiIiAQZ876c+nx+pv28kV5PfM+eGk2LR6163EV89fwkvJ7AghiGxaLm6jFIy0njheUvoBntRCRk/PUJfHgFeIrMTiIVQA1WERERERGRUvro9zTGffE3OYUe7vpoBSdcdDnxDQKN1J2pG/j5wxmHPXbPJ5+S/cUXlRU1aC3etpiLv7qYV1a8wtt/v212HBGRY7fyI/j4Wlj9OXx0NXhLvzq9BAc1WEVERERERErpvK4NaF8/sNDF2owcXvk5jSG3jcFiDcy+9tvnH7Pl75UHHOP3etn20MNsu+8+tj3wIAWrV1d67mCSV5RHdmE2AM8se4ZFWxeZnEhE5BhFJYA1LPB4zZeBZqvXY24mKVdqsIqIiIiIiJSS3Wrh6eGdsP270NaL89ezK7w2vS+6LLCD38/cpGcoyMstPsaw7lvixF9YSNrtI/FmZ1dq7mDSv0l/buh0AwA+v4+7friLtJw0k1OJiByD5n3h4plgDQ9s/z0bPr1BTdYQogariIiIiIgElXa963HC0GbEtS7Aaq/89Znb14/j5r4tAPD4/Iz96E86DzmXhu06AJCTuZPv33z5gGPq3n8fER0Cr7u3bGHr3ffg9/kqN3gQuaXLLZzS8BQAsguzGTl/JC63y+RUIiLHoGX/f5us/45k/etj+Owm8HnNzSXlQg1WEREREREJKi261aFz/4bENHdjs5vzleaW01pyXN1oAFZtdfL6T5sYfMtowh1RAKz+aQFrfl5YvL8lPJyGU6dgrVEDgNwFC9j1yiuVnjtYWAwLT/R5gqaxTQFIyUrh4UUPa9ErEQlurU6Hi94Fiz2wvfJDmH2LmqwhQA1WERERERGRMgq3WZk4vDP/zhTA1O/WkeGNpP+1NwHQuEMn6rdud8Ax9gYNqD9pEhiBg3Y+9zy5P/5UqbmDSWxYLFP7TSXKHmhaf536NdNWTTM3lIjIsTruDLjwbbAE5u7mz/fgi9tBdzUENTVYRUREREREjkLnRjW4/pTmABR5fYz9aAXH9TqVoXc9xPD7JxCbUPugY6JP7k3tkbcHNvx+to4ZQ1FaemXGDirNazTn8ZMfL96e8vsULXolIsGvzRC4YBoY/05zs/JjyFxraiQ5NjazA0jFcbvduN1us2OUu73XFIrXJsFDdShmUw2K2VSDYqaCPDeF+UV4CwwKC4tMzXLrqc349q/tbNzlYoezgE2ZThp36orH6wXvoW/5jL36avL++APXgoV4s7NJu/12Grw9HUt4eCWnDw596vXh+g7X89pfr+Hz+/hrx1/0qN3D7Fj6HBTTqQaDXMtBGOe9ivWrUXiHv42/ZksIsv+WoV6DZbkuw69JbEJGUlISSUlJeL1eUlJSmDlzJg6Hw+xYIiIiIiLlKnNpJAU7A2NF6vXPxRpm7leajTnw204L5zTxEXGINbe8RUV4C/MJi4krfs6Sn0/j518gbNcuPLGxbLn+Otx16lRi6uDi8/v4yPUR7e3taR/W3uw4IiLlxu7JxW2LNjuGHILL5WLEiBFkZ2cTGxtb4r5qsIYgp9NJXFwcmZmZRyyAYOR2u0lOTmbAgAHY7Xaz40g1pToUs6kGxWyqQTHT1y+vYvOq3QBc8kh3YmpW3UEF6Wv+5tuXp2CPiODiRyZiC9s3SrVwbQq7pk6lziOPYEuoZWJKORr6HBSzqQZDl7FhPv5mfYvn7K6qQr0GnU4nCQkJpWqwaoqAEGa320OywPcK9euT4KA6FLOpBsVsqkExg2HZ94WzKteg3+fjp5lvkZO5E4DFs2bS76obil+3d2hP9GuvmhUvJGTmZ5IQmWBqhqpcg1I9qAZDiN8P8x+HH56GXrfBgEerfJMVQrcGy3JNWuRKRERERESkHKVlubjh7aWk7Slg8C2jsdnDAPh97uek/vl7icf6vV68uXmVETPozUqZxaCPB7EoXYteiUiI2PZHoLkKsOh5mDc+0HSVKk8NVhERERERCSpVeSzP4g27OOPZH/j27wzu+WQF8Q0a0efSq4tf//qlKeTnOA95rCcriy03/o/0O+7Af5gFsiRgwZYFPPLLIxR6Cxn7w1i2OLeYHUlE5NjV7wpnT923/dOz8P0ENVmDgBqsIiIiIiIStKrakhLt68cSFxm4pfDn9bv44LctdD3jTJp27gZAXtZukl994aDcfr+fLTf+j7yffiLvxx/JfPGlSs8eTE5peAp9G/UFwFnkZOSCkbjcLnNDiYiUh+5XwZnP7Nv+cRIsfMq0OFI6arCKiIiIiIiUk5gIO0+c36l4+7GvVrM9p5Az/jeSiOgYANYtWcSqhfMOOM4wDOqMvgMsga9omUlJ5C5cWHnBg4zFsPDEyU/QNLYpAOuy1vHQooeqXMNdROSo9LgWBk/ct73gCVg48fD7i+nUYBURERERkeBSxRf8OPW42lzQvSEAOYUe7vtkJVE14xl4w23F+3z/1ivsydh+wHFRJ54YaLL+K33sXRRt0a3vhxMdFs3U06YSZY8C4JvUb3jzrzdNTiUiUk5OuAHOeGLf9vwJ8OMzh99fTKUGq4iIiIiISDl74Mx21IkJB2D+2p18ujydVif0on3f0wFwF+Qz94XJ+P4z12r8tdcSMyCwj8/pJO32kfgKCio3fBBpHtecJ07e14CY+vtUfk7/2cREIiLl6KSbYeCEfdvzxsOS18zLI4elBquIiIiIiEg5i3PYeey8jsXb47/4mx3OAk676gbi6iZis4fR9uS+GJYDv5IZhkG9xx8nrGlTAApXr2b7+Ed063sJ+jXux82dbwbAj1+LXolIaOl1G5w+LvA4pj60OM3UOHJoarCKiIiIiEhQ6XNhK4bf2426J+cR7rCbHeewBrSry7ld6gOQne/mgc/+wh4Rydmj7uGyJ6fS5YwzMQ4x3YE1JoaGzz+HERkZOPbTT9nz4axKzR5sbux8I/0a9QMgpyiHu364S01pEQkdJ98Bg5+Gq76EWi3MTiOHoAariIiIiIgEldiESOLrR2GP8WGxVu35WB8+uz0J0WEAfPt3Bsu37KFu85bUatioxOPCW7Wi3qOPFm9nTJhA/qpVFZo1mFkMC4+f/DjN4ppR11GXB0584JDNaxGRoHXCjQc3V/WLpCpDDVYREREREZEKEh8VxiPndiAxNoI3rzqebo1rHnK/XWkH39Ied9aZ1Lz8cgBihwwmvHnzCs0a7KLDokk6LYkPzvqA9gntzY4jIlKxvG746BpY+pbZSQSwmR1AREREREQklA3pWI++rWvjCDv465e7qJCfZk7n97mfc+7YB2l5/AkHvF537Bgc3bsRc8YZGpFZCo1iSx4ZLCISEryeQHN19eew6hOw2KDb5WanqtbUYA1hbrcbt9ttdoxyt/eaQvHaJHioDsVsqkExm2pQzJS2JovsTBd5W2zk5xVClNmJjsxuHPr/l5TFP/P73M8B+OblqSQ8MYWoGvuNcjUMIvv3x+PxVFbUkOLxeXh15auc1ewsGsc2Ltdz63NQzKYarMb8fixxjbDu3fz8Nrx+8He6uFJjhHoNluW6DL9m/g4ZSUlJJCUl4fV6SUlJYebMmTgcDrNjiYiIiIiUq8xlERTsCCxuVe+0XKzhwfWVxu+HZZkGbWv4cdj8bP8xmby0TQA46jei3qklj1a179qFLSuL/JYtKytyUMr35fO+633+8fxDHUsdboy5kXAj3OxYIiLlw++nQ/pMWuz8JrCJwe9NbiAtvrfJwUKHy+VixIgRZGdnExsbW+K+arCGIKfTSVxcHJmZmUcsgGDkdrtJTk5mwIAB2O1Vd9VYCW2qQzGbalDMphoUM33z6t9sWrkLgIvHdyM2PgiGsP4rw1nA/bP/ZmFKJud1rc/TwzrgcmYz895RuLL3AND3qhvpdPqgQx6ft2ABGffdD34/Dd9/j7AmTSoxfXDJc+dxxTdXsNG5EYDTGp3GxJMnlttUC/ocFLOpBgW/H8u392Fd+lpg07DgPfdl/O2HVcrbh3oNOp1OEhISStVg1RQBIcxut4dkge8V6tcnwUF1KGZTDYrZVINiBotlX4PMZguuGrTZvPy+eQ8Any7fyjldGtCvdR0G3TSKT54cB8BPM6fRtFMXajU4eD7R3Dlz8OXkAJAx+k6avv8eFt21dkg17DV47rTnGPHVCHLcOXy/5XumrZnGDZ1uKNf30eegmE01WM2dORHwwdI3MPw+bLNvAnsYtD+v0iKEag2W5ZosFZhDRERERERE9pMYF8GDZ7Yr3r7vk5U4C9w063o8Xc44EwBPUSFzX5iM13Pw3G/1Hp1AWPPmABSmpLDt4XHopsTDaxrXlCdPeRKDQFP+heUv8EPaDyanEhEpR4YBQyZB96sC234vfHQt/P25qbGqGzVYRUREREREKtEFxzekT6sEALZlF/DEnDUAnHLp1cTXbwhAxob1/PLR+wcda42OouHzzxWPWnV+8QVZM2dWUvLgdErDU7i1660A+PFzzw/3sMm5yeRUIiLlyGKBM5+FrpcFtv1eWPQ8+Hzm5qpG1GAVERERERGpRIZh8MSwjkSFBdZ/fm/JZn5en4k9PIIht43BYg08v+SzWaStWXXQ8eEtWlDv8ceKtzOeeBLX78srJ3yQur7j9Zze+HQActw5jPx+JHnuPJNTiYiUI4sFzn4eOo+ABt3h0lmB56RS6G9aRERERESCV5DeHd+wpoN7Brcp3r774xXkFXqo27wlvS4MjEBKbNmK6Brxhzw+dtAg4q++OrDh8ZA+ahSezMwKzx2sDMNgwskTaBHXAoB/sv/h/p/ux+fX6C4RCSEWC5z7AlwxGyJrmJ2mWlGDVUREREREgkp5rQJvtktPaMIJzQIN1LSsfCZ+sxaAHucMY+CNt3Px+KepkVjvsMfXuXM0juOPB8CzYwfpo+/E7/FUfPAgFWWPYuppU4mxxwCwPW87OUU5JqcSESlnFiuExxz4XH4WbPrFnDzVhBqsIiIiIiIiJrBYDJ46vxMR9sDXsmmLUlmycTcWi5WOpw0snirgcAybjQbPPoOtdm0AXEuWkLf41wrPHcyaxDbhqVOeYmjLoUwfPJ248DizI4mIVCzXbnj7XHhnKGxYaHaakKUGq4iIiIiIBJXwKBuO2DAs4T4I8sGsTROiGDOwNQDndK5Pi9pRh9zPXVCAK3vPQc/batemwdQpWOPjafjyS0Sf3Lsi44aEPg378GjvRwm3hpsdRUSk4v08Fbb9CZ4CmHkRpP5kdqKQZDM7gIiIiIiISFmcdnlb3G43c+bMwREbZnacY3Z172a0qxdLr5YJh3x9+/oU5rwwiZhatRl+/6MY/1m0xNGtGy2/S8bicFRG3JCUW5SLYRhE2Q/d4BYRCVr97ofMFFg7Bzz5MONCuOwjaNLL7GQhRSNYRURERERETGS1GIdtrno9Hr6c+hRZ27ay+a8/+X3u54fc71DNVb8/SFcAq2QbszcyYs4I7vvxPi16JSKhxxYGF0yDVmcEtt15MOMC2LzY1FihRg1WERERERGRKiYzt5ACtxerzcbAG28vfv7HmdPYuTm1xGP9fj+7353BtnvuUZP1CNw+Nzd9dxMbszfy/ZbveXXFq2ZHEhEpf7ZwuOgdaDkgsF2UC+8Ohy2/mZsrhKjBKiIiIiIiUkX4/X5m/5HOgGcW8vz36wBo3KEz3c86DwiMaJ3z/CQ8RUWHPcf2Rx4hY8IEsmd/TtY771RK7mBlt9h54MQHMP6dzPfFP15k4RYtAiMiIcgWDhe9C837BbaLcuDdYZC+zNxcIUINVhERERERCSp/fLeZ795cza7lERTkuc2OU67SsvIZM+tPslxuXl64gb/SswE4+eIrSGjcFIDMzan89P7bhz1HVK998+plPD0R1zJ9eS7JyQ1O5vZugVHCfvzc8+M9bMzeaHIqEZEKYI+AS96DZqcEtgud8M55kLnO3FwhQA1WEREREREJKtv/yWbD8kzyt9vxukNrzsxG8Q5u6dcSAK/Pz5hZf1Lk8WGz2xly2xisdjsAy776jE0r/zjkOWIHDKDW9dcFNjwe0kaNwr1jR2XED1rXdriWAU0Ct87munMZOX8kuUW5JqcSEakA9ki45ANo2iew3eI0qNnU1EihQA1WERERERGRKuTmvi1pkxgDwJrtOby88B8AajduSp9Lrire7+sXnyU/N+eQ56g9ciSOE04AwLszk/TRo/G7Q2u0b3kyDIMJvSfQskagub0xeyP3/aRFr0QkRIU5YMQHcNqDMOx1sNrNThT01GAVEREREZGgFYprOIXZLEwc3hmrJTAv6PPfr2Pt9kAjtdvgs2ncsQsAubt38d1rSYdcyMqw2WjwzGRsdesCkL90GTsmP1M5FxCkHHYHz/V7jtiwWADmb5nPK3++YnIqEZEKEhYFp4wBq+3A50PxB2slsB15FwlWbrcbdwj+lnrvNYXitUnwUB2K2VSDYjbVoJjJt9+XP48nNP/N26aug+tPbsrLP2zE7fUzZtYffHh9T2xWC6dffysz7h2Fu6CAOs1a4Ha7MQzj4JPExpI4eRJpV10NHg+7p03D3r49MYPOqPwLChKJkYk83utxbl94Oz6/jxf/fJET6p5Ax4SOB+2rz0Exm2pQyt2u9dg+uwHPuS9DwnFH3D3Ua7As12X4D/XrTglKSUlJJCUl4fV6SUlJYebMmTgcDrNjiYiIiIiUq13LI8jfHridMbFvLrbI0PxK4/bBxBVWMvIDzdNzGnvp3yBwrXlbt2CLdBBes9YRzxP3yy/U/Ww2AL6wMDbfeitFdetUXPAQ8EPBDyQXJNM/oj+nhp966Aa2iEgIiSrYzsnrHifCs4cCWxw/t7qP3Ih6ZscylcvlYsSIEWRnZxMbG1vivmqwhiCn00lcXByZmZlHLIBg5Ha7SU5OZsCAAdjtmidEzKE6FLOpBsVsqkExU/Ibq9n4RyYAFz7UlRq1o01OVHGWb9nDRa8twe8PTB3wxc0n0bx2VJnO4ff72XH//eR88SW2BvWpN3Uq4a1bV1Di0OD3+1mdtZp28e0Ou48+B8VsqkEpVwXZWGcMw7L9TwD80XXxXP45xLc47CGhXoNOp5OEhIRSNVg1RUAIs9vtIVnge4X69UlwUB2K2VSDYjbVoJjBYtk3mtBmC+0a7Nm8Ntf2bsbrP20kKsxKRq6b1vUPfb25WbuJrhl/yNfqP/IIO2rWpPbNN2OtUaMCE4eOznU7l2o/fQ6K2VSDUi7sCXDFZ/D2ObB9JUZuBvZ3z4Orv4L45iUfGqI1WJZr0iJXIiIiIiISZKrX7dp3DmzNVb2a8u0dp3LKcbUPet3rcfPT+2/z+m3Xsv2fdYc8hyUyksT77lNz9Rgs2baE6aummx1DRKTiOOLhis+hbofAds5WmHY2ZKWaGisYqMEqIiIiIiJShUWGWRl3Tntqx4Qf8vUV333Nr59+iNftZs7zk3AXFJTqvH6Ph8KNG8szasiasXoGNyTfwKSlk/h+8/dmxxERqTiOeLhiNtRuG9h2pgWarHs2m5urilODVUREREREgkrD1jU47sS6OBq4sYVV3680e5fT6HT6YOo2bwVA1rZ0Fr775hGP9ezcyearrmbTZZfjzsio0JyhoNBbiNfvBeC+n+5jQ/YGkxOJiFSgqAS48nNI+He+7uzNMO0s2LPF3FxVWPX914iIiIiIiASlDqc2pO+lxxHfqYCIqNCb8+1IducVcft7y5nxa2A0kdVmY8htd2ILC4xw/TN5Dht+/63Ec+yYNAnX0qV4d+0ifdQd+IuKKjx3MLu6/dUMajoIgDx3HiO/H0lOUY7JqUREKlB0HbjyC6gV+AUeezbBmi/NzVSFqcEqIiIiIiISJLLyihj47EI+/3MrT8xZTfqefADi6zek7xXXFu/3zctTcWXvOex56txzD7b69QDIX76cjKcnVmjuYGcYBuN7jee4mscBkOpM5YFFD+Dz+0xOJiJSgWLqBpqs8S2g3/1w4k1mJ6qy1GAVEREREREJEjWjwji9bV0A8oq83PvJygOmCmjerQcAruw9fPPKc8Wv/ZetZk0aTn0O498VkrPefZfsLzQyqSQOu4Mp/aYQFx4HwI9bf2RB4QJzQ4mIVLTYenDjQjj1LrOTVGlqsIqIiIiIiASR+85sS2JsBAA/pOxk1rI0IDDK8oz/jcQRVwOADcuWsHLeN4c9T2THDtR98IHi7W0PPURBSkrFBQ8BjWIaMfGUiViMwFfpnwp+wu11m5xKRKSChccc/NymXyBvZ+VnqaLUYBURERERkaAy7+3VTLtrEenJ0eTuLjA7TqWLjbDz+LAOxduPfvk3Gc7A34MjrgYDb7y9+LX5b7/G7q3phz1XjQsuIO78YQD48/NJv+12vDmaW7QkJ9U/iR6JgZHCRRRR6Cs0OZGISCX753t4Zyi2GcMIczvNTlMlqMEqIiIiIiJBxVPkpSjfi99jcOgb4EPfaW3qMqxrAwByCjzc/+m+qQJadO9J5wGDAYiv1xBK+FsyDIPEBx8kol07AIo2bWLrvfcedmoBCbAaVrMjiIiYw+uBr8aApwBj52oa7/7R7ERVghqsIiIiIiIiQeihs9uREB0OwHerd/D5n1uLXzv1smvpM+IqRjw2ifj6DUs8jyUiggbPTcUSF5hbNPe7eWR/8mnFBQ8BbeLb0LNuT5rbmqvZKiLVi9UGl86CmPp4e97I+jpDzE5UJajBKiIiIiIiwasaD7Ss4QhjwtB9UwU8/PkqduYEble3R0TQ89zhWG32Up0rrGFDGkx8GgyDmiMuIfbssyokc6i4o/sdvNz/Za6JvoZIW6TZcUREKletFnDjD/hOnwCGYXaaKkENVhERERERCSr6KrfPoA6JnNmpHgB7XG5mLdty2H09RUW4Cw4/Z230KafQ7LNPSXzoISxhYeWeVUREQkh0bTVX96MGq4iIiIiISBB75Jz2NEuIYvIFnbnp1BaH3Gfn5lRm3D+aeW+9XOK5Ilq3roiIIiIiIc1mdgAREREREZGjpbWYoFZ0ON+NPhWr5dAjidyFBcx65D7yc5xkbk6lebceHHdC71Kdu3DdOnLmzSPhf/8rz8giIiIhRSNYRUREREQkuOiWxIMcrrkKYA+P4JTLrineTn71BXJ37zriOZ1z57LxwovYOWUqez79rDxihowJiydw4ZwLed75PLnuXLPjiIiIydRgFRERERERCTHz1+zgh5SdxdvtT+1fPGq1IDeHr1+agt/nK/EcPlc+/vx8ALaPG0fB6tUVFzjIpOWmsX7PejJ8Gfg1jFpEpNpTg1VERERERCREFLi9jP7wD66e9htjP/qT7Hw3AIZhcPr1txBdMx6ATSuWs/ybL0s8V43zh1HjwgsB8BcWknb7SLzZ2RV7ASIiIkFIDVYREREREQkqnfo1pP9VbYjvkk9kjN3sOFVKuM3C7rwiADKchTz+1b5Rp5ExsZxx8x3F2z/MeIvMLZtKPF/dB+4nomNHANxbtrD1rruPOPK1uvGjEawiItWdGqwiIiIiIhJUEpvH0aJ7bRz1PNjDrWbHqVIMw+Dx8zoSHR5Yz/iDpVsOmCqgaaeudBtyLgBet5s5z0/C43Yf9nyWsDAaTp2CtUYNAHIXLmTXK69U3AWIiIgEITVYRUREREREQkj9GpHcN6Rt8fa9n6wkt9BTvN3nkiup1bAxADs3beTnD94p8Xz2+vWpP3lS8eJiO597ntwff6qA5CIiIsFJDVYREREREZEQc0nPRvRqUQuA9D35PDV3TfFrtrAwhtw2BqvNhmGxEBYZecTzRffuTe2RIwMbfj9bx4yhKC29QrIHAwPD7AgiIlKFqMEqIiIiIiJBJXunix2pTgr3WPC4NR/ooRiGwVPndyLSHphC4Z3Fm/jln13Fr9dp2pzTr7+Vi8c/zUnnX1Kqc9a64Xqi+/UDwJuTQ/7vy8o/eBDy+zUHq4hIdacGq4iIiIiIBJUlX2zks8l/svOXKFx7Cs2OU2U1indw96DWxdt3f7wCV9G+qQI69D2d+se1KfX5DIuF+k89SWTXrjR+803izjmnXPOKiIgEK5vZAaTiuN1u3CVMWB+s9l5TKF6bBA/VoZhNNShmUw2KmXz7rWLv9nhUhyW45PgGfPHnVpZt3sPm3S6e+XYtd59x3GH3L8rPL3nKgMhI6k+fhmEY1frv3e/bN2rV7QnN711S9elnsZgt1GuwLNdl+HU/Q8hISkoiKSkJr9dLSkoKM2fOxOFwmB1LRERERKRc7f4zAtdWOwCJp+Rii9JXmpLsyIen/7TSItbPRS18xIcfvI/f5yNr9Z/sWfMXjQYNxR4VU6b3MNxu/HZ7OSWu+v4q+ossXxYAJ4afiN2oPtcuIlJduFwuRowYQXZ2NrGxsSXuqwZrCHI6ncTFxZGZmXnEAghGbreb5ORkBgwYgL0a/SNOqhbVoZhNNShmUw2Kmb6fvob1S3cCcP69XahVv2zNwOooJSOHVnWiMYxDL8605LNZLP5oJgAN2rTnvPvGY7FYj3hev9/PnjfexPnJJzScOQNrjRrlGbtK0+egmE01KGYL9Rp0Op0kJCSUqsGqKQJCmN1uD8kC3yvUr0+Cg+pQzKYaFLOpBsUMFuu+pSRsdptqsBTaN4wv8fXjzzyXVQuSycncSfqaVfz59Zf0PHf4Ec+7Y+pUdr30cuDxvffR6JWXMaxHbsyGEn0OitlUg2K2UK3BslyTFrkSERERERGpZnILPaRm5hVvhzuiGHLLnfDvCNefP3iXjI3/HPE8NS+6CGt8oHmb99NPZCa9WDGBRUREqjA1WEVEREREJHhpwrMyW5iykzOe/YEb31lGkWffgmEN23Wg5znnA+Dzepjz/CTcRYUlnsuemEiDZyaDJfDVMvPFF8lZsKDCslcV2YXZ7MzfSY4vB5/fd+QDREQkpKnBKiIiIiIiQcXg0POIypH5fH6e/noN6XvyWZuRwwvz1x/weq8LL6VO0xYA7E7fwo8zph3xnFEnnkjtO0YVb2+9626Ktmwpz9hVzv0/3c8Zn57BU86nyC7MNjuOiIiYTA1WERERERGRasJiMXh6eCdslkCT+sX56/l7q7P4davNzpDbxmCzhwGw/Osv2PjHsiOet9Z11xEz4HQAfE4nabePxFdQUAFXICIiUvWowSoiIiIiIlKNtK8fx819A6NUPT4/Yz/6E7d3323utRo24pTLryne/ualKbicJY/SNAyDeo8/TliTJgAUrl7N9nHj8fs1h4OIiIQ+NVhFRERERCSonDqiNVc+dRL1T88hJiHC7DhB6ZbTWnJc3WgAVm118uoPGw54vcvAM2napTsA4VHRFOTmHPGc1pgYGjz/HEZkJADZn33Gng9nlXNyERGRqkcNVhERERERCSr2cCvhDhsWe+CWdym7cJuVicM7s/evb+p361iXsa+JahgGg24aRY9zzueyJ6cQX79hqc4bcdxx1Hv0UQDCmjfHcXz3cs8uIiJS1ajBKiIiIiIiUg11blSD609pDkCR18fYj1bg9e27pT+qRk1OufRq7GHhZTpv3FlnUu+JJ2j64YeEt2hRrplFRESqIjVYRUREREREqqk7Tj+O5glRAPyxZQ9v/rSxxP29Hg8+r/eI561x3lCs0VHlkrEqMtg3ctqP5pkVEanu1GAVEREREZGgsuGPnSybuwnnujAKct1mxwlqEXYrTw/vhGFApN1KZJj1sPvu3prO+w+N5ddPPyzz+/iKisiZN+9YooqIiFRZNrMDiIiIiIiIlMXGP3ayZvF2IJyCPDcxNc1OFNyObxrPhKEd6NOyNo1rOQ65T35uDjPuu4OifBcZG/+haedu1GvVulTnd2/dStrIURSsXEnDpBeI6d+/POOLiIiYTiNYRUREREREqrlLT2hy2OYqQGR0DN3PHAqA3+djzguTKCrIL9W5c+Z9T8HKlQBsvfseilJTjzWuiIhIlaIGq4iIiIiIBC9Nf1lhPF7fAdsnDruIei0Do1b3bN/GgrdfL9V5al52KTGDBgHgy80l7faR+Fyu8g1bye7ueTfvD36fW2NuJSYsxuw4IiJiMjVYRUREREQkuBhH3kWOnqvIw7jPV3H1tN/w+/d1sC1WK4NvuxN7eAQAK+d9w/rfFh/xfIZhUG/CBMKaNwegMCWFbePGHXDuYNMwpiHH1TyORGsidovd7DgiImIyNVhFRERERESk2DXTfmPaolR+XJfJB79tOeC1mon16XfVDcXb377yHHl7so54Tmt0FA2ffw6LIzANgfPzL8iaObN8g4uIiJhEDVYREREREREpdlPflsWPH/tqNduyD5xrtUO/AbTscSIA+TlOvnlpSqlGo4a3aEG9xx8v3s548ilcy5eXU2oRERHzqMEqIiIiIiJBK3hvMq+6Tj2uNhd0bwhATqGH+z5ZeUAD1TAMBtxwG1E1agKw8Y9l/PntnFKdO3bQGcRffXVgw+0mfeQoPJmZ5XsBleDXbb/y+YbP+b3wdwo8BWbHERERk6nBKiIiIiIiwcXQJKwV7YEz21EnJhyA+Wt38snv6Qe87oiNY9BNowIbhkFe9p5Sn7vOnaNx9OgBgGfHDna99VZ5RK5UM1fPZNzicXyS/wm57lyz44iIiMnUYBUREREREZEDxDnsPHZex+Lt8V+sYofzwJGaTbt0p/eFl3HBA4/R+8JLS31uw2ajwTOTsdWpQ/y111DnjjvKLbeIiIgZ1GAVEREREZGgEpcQQe0mMdjjvFht+kpTUQa0q8u5XeoD4Czw8MBnfx001+qJ519M4w6dynxuW+3aNP/yC+qOHYths5VLXhEREbPoXyMiIiIiIhJUjh/SjPPGdKFuLxexCRFmxwlpD5/dnoToMAC+/TuDL1dsO+IxXo+nVOe2xsYeUzYREZGqQg1WEREREREROaT4qDAeObdD8fbsP9IPu6/f52PZV7OZPvZWCl15ZX6v/L9WsfnGG/Hllf1YM/m11JqISLWnBquIiIiIiIgc1pCO9RjWtQEPnNmWVy4//rD7/fT+2yx4+zWytqYx782Xy/Qezm++ZdOIEeQt/IFtDz540FQEVY2hhdZERGQ/arCKiIiIiIhIiZ65qAvX9WmO1XL4xmLnAUMIi3QAsPrH+az5eWGpzx9+XCsMux0A55y5ZL3zzrEFFhERqURqsIqIiIiISFBZ9nUqs5/5kx2/OHBm5psdR/4VW7sOp197U/H2d2+8iDNzZ6mODW/WjHpPPlG8nfH0RFzLlpV7RhERkYqgBquIiIiIiASV7J35ZGx0UrTHisftMztOtfT75iyeSU456Pk2J/elda9TACjMy+PrF5/F7yvdf6PYAQOodf11gQ2Ph7RRo3Dv2FFumUVERCqKGqwiIiIiIiJSas8mp3D+S4t4bt465q89sAFqGAanX3szMbVqA7Bl1QqWffVZqc9de+RIHCeeCIB3Zybpo0fjd7vLLXt5iQ2LJSEigWgjGgPNxyoiUt2pwSoiIiIiIsGraq+FFJLq14hg7xpU932yEmfBgQ3QiOhoBt18B/y7ENRP77/NjtQNpTq3YbPRYPIkbHXrApC/dBk7Jk0uv/Dl5JHej/DtsG+5J+4eEiITzI4jIiImU4NVRERERESCisYLmuvC4xvRp1Wgqbgtu4An5qw5aJ/GHTpx/FnnAeD1eJjz/CQ8RUWlOr+tVi0aTp0C/y56tXv6dJxz55ZPeBERkQqgBquIiIiIiIiUmmEYPDGsI1FhVgDeW7KZn9dnHrRf74sup3aTZgB43EXk7CrdglcAkV26kHjfvYENux1vbu6xBxcREakgarCKiIiIiIhImTSs6eCewW2Kt+/5ZAV5hZ4D9rHZ7Qy5bQyd+g/iiqeeo2a9BmV6jxoXX0z8tdfQ9J23qXnBBeWSW0REpCKowSoiIiIiIiJldukJTTihWTwAW3bnM/GbtQftk9CoCQNuuJWwSEeZz28YBnXHjiWyS5djjVru3vn7He79+V4+yPuAPYV7zI4jIiImU4O1ilqzZg3h4eEYhsHXX39tdhwRERERkarD0CysVYHFYvDU+Z2IsAe+Vk5blMqSjbuPeJzf58PvP/rVyYrS0o/62PKyfMdyvtn0DSvdKyn0FpodR0RETKYGaxV10003Yf93UncREREREZGqqGlCFGMGti7efuCzlfh8h2+eZu/I4IPx9/L3D9+X+b38Xi87pkzhn0GDyPt1yVHlFRERqQhqsFZB77zzDr/++itjxowxO4qIiIiISJXTpEMtug1qREyLQiKiNSjBbFf3bka3xjXo1DCO5y7pisVy6BHGObszefuu20hfs4p5b77MnoztZXqf7M9ms+vlV8DjIX30aNwZGeURX0RE5JipwVrF7NmzhzFjxnDPPffQtGlTs+OIiIiIiFQ5zbvU5vgzmxJ3XBGO2DCz41R7VovBq1cczyc39aJNYuxh94uJT6BVz14AuAvymZv0DD6vt9TvEzf0XKJ69wbAu2sX6SNH4S8qOrbwIiIi5UAN1irmnnvuITo6mrvuusvsKCIiIiIiIqWSEB2OzXrkr5enXX0DcXUTAdi69m+WzP6o1O9hWK3UnzQRW/16AOT/8QcZT088usDl6FjmkxURkdAQkg1Wl8vF3LlzmTBhAsOGDaNJkyYYhoFhGIwbN65U58jJyWHcuHF07NiR6Oho4uLi6NGjB5MnT6aogn5L+uuvv/Laa68xdepUIiIiKuQ9REREREREKlqhx8vP6zMPej4s0sHgW+7EMAJfRX/5aCbb16eU+ry2mjVpOPU5jH/Xq8h6912yv/iifEKLiIgcJZvZASrCkiVLGDJkyFEfv2nTJvr27UtqaioADoeDwsJCli5dytKlS5kxYwbz5s2jZs2aBx3r9/spLCzdKpIWi4WwsMAtTV6vl5tuuokhQ4Zw1llnHXV2EREREZFQ53F7cRd68XkocUElMcefW/Yw9qM/+WdnHrNv6U2HBnEHvN6gdVtOGHYhiz9+H5/Xy5wXJnP5k1Oxl3KQSWTHDtR98AG2P/QwANseepjw41oT0fq4cr8WERGR0gjJEawANWvWpH///owdO5b33nuPxMTEUh3n8Xg4++yzSU1NpV69eiQnJ5OXl4fL5eL9998nJiaG5cuXc9lllx3y+E2bNhEZGVmqP926dSs+7oUXXmD16tVMnTq1XK5fRERERCRU/fThOt4as4ityTFkbc0zO478x/y1O0jJyMXr8zP2oxW4vb6D9jlx2MUktmgFQNa2dBa++0aZ3qPGBRcQd/4wAPz5+aTdfhvenJxjD19KBodeyEtERKqnkBzB2qdPH3bv3n3Ac/fcc0+pjp0+fTorV64E4OOPP+akk04CAqNNL7roInw+HyNGjGDOnDnMmzeP/v37H3B8QkICb731Vqnea+8I2OzsbB588EGuuOIKLBZL8cjZzMzALTUZGRmkpqbSqFEjrFZrqc4tIiIiIiJihpv7tuTrv7azZnsOq7c5eWnBP9zev9UB+1htNgbfOoZ37rkdT2EhfybPpVnXHrTo3rNU72EYBokPPkjB6tUU/r0a96bNZEx4jPpPPVkRlyQiIlKikGywHksTcvr06QD069evuLm6v4svvpj777+fjRs38vbbbx/UYI2Ojuaqq64q03tmZWWRk5PDq6++yquvvnrQ63vPt2XLFho2bFimc4uIiIiIiFSmMJuFicM7M/TFn/H6/Dz//TrOaJ9I68SYA/aLr9+AfldcT/JrLwCwff3aUjdYASwRETR87jk2nj8ce/36JNx6S7leR0mOTzyecEs4aWlpRNi0foaISHUXkg3Wo+Vyufj5558BGDx48CH3MQyDQYMG8dJLL/Htt9+Wy/vWqVOHTz/99KDnv//+e55//nkeeughunbtSkJCQrm8n4iIiIhIqNAMrFVTx4Zx3HhKc15c8A9ur5+7PvqTj2/qhc164Cx1HfufwfZ/UmjZ8ySad+1R5vcJa9iQJm+9SVjz5lgqcaHgS9pcwvAWw5kzZw41wmtU2vuKiEjVpAbrflavXo3PF5gfqEOHDofdb+9r27dvZ/fu3cTHxx/T+zocDoYOHXrQ83v27AHgpJNOYtCgQYc9vrCw8ICFtZxOJwButxu3231M2aqivdcUitcmwUN1KGZTDYrZVINiJp9/35yeHo9HdVhF3XxKU75ZtZ1/dubxZ1o2ryxczw19mh20X79rbgKO/vPE2qoVXsBbyXWgz0Exm2pQzBbqNViW61KDdT9bt24tftygQYPD7rf/a1u3bj3mBuuxeuKJJxg/fvxBz3/77bc4HA4TElWO5ORksyOIqA7FdKpBMZtqUMyQtSkcCANg8eLFhP198CJKUjWcUxem7LTix+DZ5BTsO1ZTN/LIx/n9fgzj6BaSMoqKiF+4kN39+uG3VfxXXn0OitlUg2K2UK1Bl8tV6n3VYN1Pzn6rTpbUmNz/tZwKXKnyqquuKtV8rvfeey+jR48u3nY6nTRq1IiBAwcSGxtbYfnM4na7SU5OZsCAAdjtdrPjSDWlOhSzqQbFbKpBMdOPOetYvWU7ACeecCKJzWqYG0hKlB27ljcXbcLjN/h6dwIzr+2B1XLo5qnf72fV/GTWLvqBofeMw1rGBmnRpk1sH3UHRevX0yw+njoPPlgel3BI+hwUs6kGxWyhXoN77xAvDTVYQ0B4eDjh4eEHPW+320OywPcK9euT4KA6FLOpBsVsqkExg9Wyb1Fbm82mGqzixg5qy/drd5KWlc/JLROw2mzY/zMX617zp7/G73NmA7B09kecfPHlZXovb1ER7i1bAHB+OIuort2ocd7QY8p/KA8vepjvNn2H2+2ma1FXGjsal/t7iJSWfhaL2UK1BstyTYf+qVZNxcTsW9WypGHA+7+2/zEiIiIiIiJyoMgwK1Mu7srsW3szemDrwzZXAdqe3BeLNdBAX/LZLNLWrCrTe0W0a0fiQw8Vb28fN46C1auPLngJXG4XziIn+f58/H4ttSYiUt2pwbqf+vXrFz9OT08/7H77v7b/MSIiIiIiInKwLo1q0L5+3BH3S2zRil4XXAqA3+9j7gvPUFiGOfAAapw/jBoXXhg4R2Ehabfdjjc7u+yhRURESkkN1v20bdsWiyXwV/LXX38ddr+9ryUmJpq+wJWIiIiISHXTZUAjzrmjE7VPdBFXpxQrJkmVlFvoOeTzPc49n/qt2wHg3JnB/GmvlPncde+/j4gOHQBwp6Wx9a678fu0GJqIiFQMzcG6H4fDQe/evfnxxx/5+uuvGTt27EH7+P1+vvnmGwAGDhxY2RHLxO1243a7zY5R7vZeUyhemwQP1aGYTTUoZlMNipkcNezYoxyEr/GCxac6DDJur4+Xf9jI279sZvbNJ1K/xsFN8gE33s7M++7AXZDPqoXzaNy5G6169ir9m1gs1J08iS0XXYxvzx5yFy5kR9KLxP/vxnK5hv2nBXB7QvN7l1R9+lksZgv1GizLdRn+ajJhTNOmTdm0aRMPP/ww48aNO+x+b7zxBtdddx2GYfDLL79wwgknHPD6hx9+yEUXXQTAd999R//+/SsydpkkJSWRlJSE1+slJSWFmTNn4nA4zI4lIiIiIiJSbO4Wg6/TAvOstonz8b+2Pgzj4P2cG1LYsXghAJawcBoPOR+bI6pM7+VIWUeDN9/E8PvxGwbpV1+Fq3XrY76GD/I+YKV7JQCjY0YTb9WdjSIiocblcjFixAiys7OJjY0tcd+QHcGalZWF1+st3vb9ezuIy+UiMzOz+PmIiAiio6OLt6+88kqmTp3KypUrOf/885k+fTr9+/fH5/Px8ccfc/311wMwePDgKtVcBbjlllu45ZZbcDqdxMXFMXDgwCMWQDByu90kJyczYMCAkFylToKD6lDMphoUs6kGxWyqweDVp8DN8ucXkeEsZE22hfx6HRnercFB+/n9g5nrc7N+ySJ8RYV416/i7LsewrCUYaa7IbDbEcnu51/A8Ptpu2Mnde+445iv4ceff2TlpkCDtU+fPjSp0eSYzylSVvocFLOFeg06nc5S7xuyDdauXbuyadOmg56fOHEiEydOLN6+8sormTZtWvG2zWbj888/p1+/fqSmpnL66afjcDjw+XwUFBQUn3vGjBkVfg3Hym63h2SB7xXq1yfBQXUoZlMNitlUg2KGnVty2L09F9d2Gz432B2qwWASb7fzxLCOXDNtKQCPz13LaW0TqRsbcdC+A2+8je3r1pCbtZusrWkU5GQTm1CnTO9X56abKPp7NRHt2pJw001la9AehmW/c9hsNn0Oiqn0s1jMFqo1WJZr0iJXh9C0aVNWrFjBQw89RIcOHTAMA7vdTvfu3Zk0aRKLFy+mZs2aZscUEREREamWVv+0le/eWM3u5ZE4MwvMjiNH4bQ2dRnWNTBqNafAw/2fruRQs9dFRscw6ObRtO51CldOTCpzcxXAsFho+Pxz1L7llnJproqIiPxXyI5gTU1NPabjY2JiGD9+POPHjy+fQCIiIiIiIlLsobPb8cO6TDJzC/lu9Q4+/3Mr53Y5eKqAJp260KRTl2N6r0M1Vv1uN0YIjrgSEZHKp1/fiYiIiIiISKWr4QhjwtAOxdsPf76KnTmFpTr2WNdqdi1bxj+Dh5C/atVRHT+izQge7/U4FzguoGa47m4UEanu1GAVERERERERUwzqkMiZneoBsMfl5uHP/zriMbm7d/HJk+PYtPKPo3rPvMW/sunKq3CnpZF++0g8WVllPkeXOl0Y1HQQncM647A7jiqHiIiEjpCdIkACq7m53W6zY5S7vdcUitcmwUN1KGZTDYrZVINiJq/PV/zY4/GoDoPcg4OPY9H6TLJcbtZsyyHT6SIu8tC37u/ZvpUPx91NQW4uOzelcumTU4iIii7T+9k7dSS8XTsKV6zAnZ5O+pix1Et6AcNqLdN59DkoZlMNitlCvQbLcl2G/1jvrZAqIykpiaSkJLxeLykpKcycOROHQ79NFREREZHQkvV3OHmbwgCo0yuPsDjfEY6Qqu73TIOtLoMzGvqwl3Cfpd/vZ+v3c8jP2ApAdOPm1O19GoZhlOn9bHuyafzcc9jy8gDYdXp/dg0YcNT5RUQk9LhcLkaMGEF2djaxsbEl7qsGawhyOp3ExcWRmZl5xAIIRm63m+TkZAYMGIBdk9KLSVSHYjbVoJhNNShm+nnWelb9sA2As0Z1oH4LzYFZneTsymTmfXdQmJcLwMD/jaTNyX3LfB7Xr7+y9YYb4d8R0fWSXiDqlFNKdWxabhq78nbx66+/MmLgCKIjyjaKVqQ86GexmC3Ua9DpdJKQkFCqBqumCAhhdrs9JAt8r1C/PgkOqkMxm2pQzKYaFDNYLPtu5bbZbKrBaiY+sR4Drr+VL6c8CcCC6a/RuH0n4urULdN54k4+Gfcdo9g5+RkAMu69j2Yff0RYo0ZHPPaVv17hqw1fATDYPZiaMWryi3n0s1jMFqo1WJZr0iJXIiIiIiIiUqWsy8jhhreXkp1/6PnvWp90Mu1OOQ2AonwXc5Oewefzlvl9al13HTEDTgfA53SSdvtIfAUFRx9cRESqJTVYRUREREQkqFhtBrYwC4bVTxmn3pQg8NWKbZz53E98+3cGj33192H3O+3q/xFbOzBqNX3NKn77/JMyv5dhGNR7/HHCmjYFoHD1araPf+SocouISPWlBquIiIiIiASV3sNbcc3k3jQYmEvtxjFmx5Fy1qVxDcJsga+qHy5N44eUnYfcL9zhYPAtd2AYgX0XffguGRvWl/n9rDExNHz+OYzISIyICKJOPKFMx/vRsiYiItWdGqwiIiIiIiJSZTSoEcm9Q9oUb9/7yUpyCz2H3Ldh2w70HDocAJ/Xyz/Lfj2q9wxv1YoGkyfR9P33iDv33CPub6Ch0yIiso8arCIiIiIiIlKljOjZmF4tagGQviefp+auOey+Jw2/hCadunL2HffQ64JLj/o9Y047jYg2bY68o4iIyH/YzA4gFcftduN2H3pS+GC295pC8dokeKgOxWyqQTGbalDMphoMfY+e05azXlhEvtvHO4s3cUa72pzQLP6Q+54z9kEMwyj3enD9spjInj0wrNYDnvf5fMWPQ/V7l1R9+hwUs4V6DZblugy/368JY0JEUlISSUlJeL1eUlJSmDlzJg6Hw+xYIiIiIiLlyrXNRmFmoOEV06IIm0NfaULVwm0Gn6QG/lvXCvdzd2cv4dYjHFQevF5qfzWHmj//zK5+/dg16IwDXv4o7yP+cP8BwKiYUSRYEyohlIiIVCaXy8WIESPIzs4mNja2xH3VYA1BTqeTuLg4MjMzj1gAwcjtdpOcnMyAAQOw2+1mx5FqSnUoZlMNitlUg2KmRR//w18LtgJw1sj21G956FGNEvx8Pj8j3viNZZv3AHB1rybcN7h1icf4/X7WLvqBzSv/YMCNt2MYZZ8vtWDVKtIuvQy8XgASp04l+rR+xa8/uOhBvkr9CoBZg2bRIr5Fmd9D5FjpZ7GYLdRr0Ol0kpCQUKoGq6YICGF2uz0kC3yvUL8+CQ6qQzGbalDMphoUM1gs+5aSsNlsqsEQN/GCzgye+iOFHh9vL97MtX2a07Dm4e/Um/fmy/zxzZcANGrXkU79zzjsvodj79KFOmPHsOPJpwDYcf/9RH00i7CmTYEDa9Bu0+egmEs/i8VsoVqDZbkmLXIlIiIiIiJBS/fjhb7mtaMZPeA4miVE8f4NJ5bYXAVo0qlr8eP5018la1v6Ub1v/JVXEjN4EAC+3FzSbh+Jz+UC4IETH2D++fO5L/Y+GkQ3OKrzi4hI6FCDVURERERERKq06/o0Z+7IPvRoeuTpIFoefwKd+gcao57CQua8MBmvx1Pm9zQMg3qPTiCsReD2/8KUFLaNG4ff78dhdxAXHofD4sBqqYxJYUVEpCpTg1VERERERESqNKvFIMJe+kZm3yuuo2a9+gBsX5/Cr59+cHTvGx1Fw+efw/Lv4sHOz78ga+bMozqXiIiELjVYRUREREQkqBiUfdEiCS0+n59ZS7dQ5PEd8nV7RASDb70T49+5Uhd/8gFbU1Yf1XuFN29OvccfL97OePIpXMuXH9W5REQkNKnBKiIiIiIiIkFjY2YeF77yC2M/WkHS/PWH3a9ey9acNPwSAPw+H3NfeIaifNdRvWfsoDOIv+aawIbbzfoH7+b1la8xv2A+ewr3HNU5RUQkdKjBKiIiIiIiIkHDVeThjy17AEiav57V25yH3feEoRdS/7i2AOzJ2Mb86a8f9fvWGX0Hjh49iOzShc9v7MCLK19iXsE8NVhFREQNVhEREREREQke7evHcXPfwMJTHp+fsR/9idt76KkCLFYrg2+9E3tEJAAbfl+Cy5l9VO9r2Gw0fP45mrw9nfyakUcXXkREQpLN7ABScdxuN2632+wY5W7vNYXitUnwUB2K2VSDYjbVoJipRr0ImnSMZ8eODKxhqsPq6IY+TZn71zbW7cjjr3QnL89fx/9ObX7IfaPia3HqFdfxz2+L6X/dzdgjHUdfM1FR+AC/z1/8VKh+75KqTz+LxWyhXoNluS7D7/f7j7ybBIOkpCSSkpLwer2kpKQwc+ZMHP+udikiIiIiIhJKNuXCsyut+DGwGn7u6uQl8TBff/Z+7TWM8lkg7WPXxywvCix0NTJmJLWttcvlvCIiUnW4XC5GjBhBdnY2sbGxJe6rBmsIcjqdxMXFkZmZecQCCEZut5vk5GQGDBiA3W43O45UU6pDMZtqUMymGhSzqQYF4OlvUnjtp1QAOjeM44Pre2K1lE8TtSSf3HsB7tUpAHRLmkGrBh0q/D1F/kufg2K2UK9Bp9NJQkJCqRqsmiIghNnt9pAs8L1C/fokOKgOxWyqQTGbalDMphqs3u48ow3z1uxkQ2Yef6Zl8+6SNK7rc+ipAvbncmbz/Zsvc+L5F5PQqEmZ3zdhYxb1/wmMVbIYhmpQTKXPQTFbqNZgWa5Ji1yJiIiIiIhIUIqwW3l6eCf23vk/8Zu1bMzMK/GYnZtTeXvsraz95UfmvDAZzzHOHehHN4WKiFR3arCKiIiIiEhQWTpnIzMeWsK2+VHs2JRjdhwx2fFN47mqV1MALji+IbVjwkvcv0ZiPSKiYwDYmbqBRR++W+b3NNhvGgLNuiciUu2pwSoiIiIiIkGl0OUhL6sQb4EFn9dndhypAsae0ZoPbjiRCUM7Eh1e8kx49rBwhtw2Bos1sN9vX3zCllUryvR+/oqf5lVERIKIGqwiIiIiIiIS1BxhNk5oXqvU+9dp2pyTL748sOH3MzfpWQryckt9fIQ1ovhxuLXkEbMiIhL61GAVERERERGRkJPhLMBfwu37x591Ho3adwIgZ9dO5r3xUqnP3SimUfHjelH1jj6kiIiEBDVYRUREREREJGT4fH7e+SWV0yYt4IPfthx2P8NiYdDNdxDuiAJgzc8LWf3TgrK/oeZgFRGp9tRgFRERERGR4KXelvzH8i17eHD2KvKKvDz21Wq2Zecfdt/YhNqcft3Nxdvz3ngJ584dR34TQ5OwiojIPmqwioiIiIhIcFFzS0rQvUlNLujeEICcQg/3fbKyxKkC2vQ+lbYn9wWg0JXH3z/Or4yYIiISQkpeXlGCmtvtxu12mx2j3O29plC8NgkeqkMxm2pQzKYaFDP5fN7ixx6PR3UoB7n7jFYsTNnJjpxC5q/dyUdLNzO0S/3D7n/K5dexI3UD3c46jza9Tz1iTf3Zyk6WrzaFhYXYXNtoGhtb3pcgckT6WSxmC/UaLMt1Gf6SfpUnQSUpKYmkpCS8Xi8pKSnMnDkTh8NhdiwRERERkXK1Z004uRvDAKh9govweO8RjpDqaOVug9fXWgFwWP3c28VLbNjh9/f7fBiW0t3k+anrU5YVLQPgtpjbqGute8x5RUSkanG5XIwYMYLs7Gxij/CLNDVYQ5DT6SQuLo7MzMwjFkAwcrvdJCcnM2DAAOx2u9lxpJpSHYrZVINiNtWgmGnxZxtYMS8dgCG3tqNh61omJ5KqavSsFXyxYjsAA9rWIemSzhjlMMXEI78+wmf/fAbAewPfo3VC62M+p0hZ6WexmC3Ua9DpdJKQkFCqBqumCAhhdrs9JAt8r1C/PgkOqkMxm2pQzKYaFDO0Oj6RuNqRrPzrL+LrxagG5bDGn9uRRf/sZldeEcmrd/DtmkzO6nT4qQL2t27JInakbqT3hZce9Jplv5GuVptVNSim0s9iMVuo1mBZrkmLXImIiIiISFBJbBZH2971iG7kxlHSPd9S7cVHhfHIuR2Ktx+evYpduYVHPG7emy/z+eTHWfzxe6T+seyg1w200JqIiOyjBquIiIiIiIiErCEdExnUPhGAMJuF9D35RzymZr0GxY+/fmkKLmf2Aa+f/NyPzHzKw8ynPODMKd/AIiISdNRgFRERERERkZBlGAaPDG3PVb2a8s0dp9CpYY0jHtN10Fk07dIdgLw9WXz3WhL7L19i+PzYfGDzVVRqEREJJmqwioiIiIhIUHE5i8ja7sKda8Fd6DU7jgSBOjERjDunPbERpZtPzzAMzvjfSCJiAouarFuyiL8WJB9yXz9aN1pEpLpTg1VERERERILKn/M2M+uxZWT8GMXOzbo9W47O/iNSDyW6ZjwDb7i1eHv+W6+yZ/u2Q5yovJOJiEiwUYNVREREREREqpWte/K5ZtpvzF+7o8T9WvXsRYd+AwFwFxYw54VJ+LwaNS0iIgdSg1VERERERESqjQ07cxn47A/MX7uT+z5ZibPAXeL+/a66nhp16wGwbd1afv30Q+Ij44tfjwuLq9C8IiJS9anBKiIiIiIiItVGs4QoujSqAcC27AKemLOmxP3DIiIZfOudGJbA1+fl33xJXPi+BmutiPjDHSoiItWEGqwiIiIiIiJSbRiGwRPDOuIIswLw3pLN/Lw+s8Rj6h/XhhOHXUTjjl24/Kmp2C3WyogqIiJBQg1WEREREREJMobZASTINYp3cO/gNsXbd3+8grxCT4nHnDjsYobf9wgx8QkVHU9ERIKMGqwiIiIiIiJS7Vx6QhN6Ngvc3p+Wlc/Eb9aWuL/Fai2eJsDn9xU/7/Vp0SsRkerOZnYAqThutxu3u+QJ24PR3msKxWuT4KE6FLOpBsVsqkExk2+/hpbH41EdylF7/Nx2nJW0iAK3j2mLUhnYtjY9mtY84nGzTw5jebyNurtrcJFrCx3cGtUqlU8/i8VsoV6DZbkuw+/3+yswi1SipKQkkpKS8Hq9pKSkMHPmTBwOh9mxRERERETKVfbaMHI2hAOQ0NNFRC2NIJSjN3+rwWebAnOq1o7wc1cnL2FHmGJ1wdoPqLFqN9EFNhzDzqB+RONKSCoiIpXJ5XIxYsQIsrOziY2NLXFfNVhDkNPpJC4ujszMzCMWQDByu90kJyczYMAA7Ha72XGkmlIditlUg2I21aCYacnnG/kjOQ2AQTe3pXFbjR6Uo+f1+bnk9SUs35INwKThHTm3c70Sj3nmviuxbXYCcOoTD9C5UfcKzynyX/pZLGYL9Rp0Op0kJCSUqsGqKQJCmN1uD8kC3yvUr0+Cg+pQzKYaFLOpBsUM3Qc1o93J9fh+/nzqt4xXDcoxsQMTL+jC1dOW8NBZ7RnQrm6ZjrdZbapBMZV+FovZQrUGy3JNarCKiIiIiEhQiYiyYw0DW6Qfm13r9sqxa1knmvl39sVmLV092Qs87L0V1B+icw+KiEjp6V8jIiIiIiIiUu2VtrkKEL0jZ99GnqsC0oiISDBRg1VERERERERkP36/n9l/pPNXenap9hURkepNUwSIiIiIiEhQ2fZPNtv+ySJno53c3QXUrBt6876JebJdbu6c9Qffrd5B23qxfH5rb+wHjW419j1Uf1VEpNrTCFYREREREQkqm1Zm8ssnG8heE0H2znyz40iIiQyzkpYVqKvV25y8tOCfkg/QCFYRkWpPDVYRERERERGRf4XZLEwc3hmrJTBK9fnv17F2e84RjhIRkepMDVYRERERERGR/XRsGMeNpzQHwO31M/ajP/F4fcWvR9giih/Xi65X6flERKRqUYNVRERERESCi3HkXUSO1e39W9GyTjQAK9Kyef2njcWvdYtO4MT16Zy4Pp1YRw2TEoqISFWhBquIiIiIiAQtTX8pFSXCbuXp4Z0w/m3oP5Ocwj87cwGIs4cRn1dAfF4BFqvVxJQiIlIVqMEqIiIiIiIicgjdGtfk2t7NACjy+LjroxV4ferqi4jIgdRgFRERERERETmMOwe2pmktBwDLNmXx7uJN5BblFr+eU6QFsEREqjs1WEVEREREJKgYhiZhlcoTGWblqfM7AXBmx3qc2akenw2sw11XxnDXlTGkW50mJxQREbPZzA4gIiIiIiIiUpWd0LwWc27vQ7v6sQBYl26l78Y6AHgvcpsZTUREqgCNYBURERERkaASEW2nRt1IbFFebGFaYEgqx97mqoiIyH9pBKuIiIiIiASVzqc1ol2fRObMmUNiczW9xGRa80pEpNpTgzWEud1u3O7Qu11l7zWF4rVJ8FAditlUg2I21aCYTTUoZvH7/Vh37lvkyuPMUR2KKfQ5KGYL9Rosy3UZfr9fv28LEUlJSSQlJeH1eklJSWHmzJk4HA6zY4mIiIiIiISMQi9sf/dF3HY7AI6+J1G/fgeTU4mISHlzuVyMGDGC7OxsYmNLvmNGDdYQ5HQ6iYuLIzMz84gFEIzcbjfJyckMGDAA+7//qBGpbKpDMZtqUMymGhSzqQbFTK9ccjaF1sANod1uuZaTTzrL5ERSHelzUMwW6jXodDpJSEgoVYNVUwSEMLvdHpIFvleoX58EB9WhmE01KGZTDYoZ1v66nbWLt7FzZyR7OhRQv4Xu2hLzWCxWfQ6KqfSzWMwWqjVYlmtSg1VERERERILKnh0utqzOAmwU5nnMjiMiIiLVnMXsACIiIiIiIiLByjDMTiAiImbTCFYREREREQlaWlBCzGAlEqu3EIBmsc1NTiMiImZTg1VERERERIKKBgyK2RKLommTsgaAiMgok9OIiIjZNEWAiIiIiIiISBn4NS+AiIjsRw1WERERERERERERkaOkKQJERERERCR4+TULq1Q+47goNrpr4fG4sfiyqE0zsyOJiIiJ1GAVEREREZHgotuzxWTrWoWx3m8FrCTaC8yOIyIiJlODVURERERERKQMjNQsWm+JAcDn9picRkREzKY5WEVEREREJKjUbRZLh771iW5SRHR8hNlxpJrzo2kqRESqO41gFRERERGRoNKkfS3qHxfLnjlrqZnoMDuOVENen794tJLPZ2oUERGpAjSCVURERERERKQMwjbtLn5ctGN3CXuKiEh1oAariIiIiIiISBmEub37Nrzew+8oIiLVghqsIiIiIiIiIkfJrylYRUSqPc3BKiIiIiIiQWXpnI38+sVG8EezucluWnSpa3Ykqcb86rCKiFR7GsEqIiIiIiJBxe+HwMLthslJpNpS6YmIyH7UYBUREREREREpA8NvLX4cZXeYmERERKoCTREgIiIiIiLBS3dniwki/VHE7dkBQL2YRianERERs6nBKiIiIiIiQcXQ7dlisho+B203ZQAQFhFpchoRETGbpggQERERERERKRN1+UVEZB81WEVEREREJGj5NUeAiIiImExTBIiIiIiISJDR6EExV94ZtfisQSwej4czbTuoRyuzI4mIiInUYBUREREREREpg4xd2RTl1AQg31dkchoRETGbGqwiIiIiIiIiZZHvJrog8HXa79M0FSIi1Z0arCHM7XbjdrvNjlHu9l5TKF6bBA/VoZhNNShmUw2KmZp2iSe2rp3fly+nZv1I1aFUPv++pqrHE5rfu6Tq089iMVuo12BZrsvw+/36dVuISEpKIikpCa/XS0pKCjNnzsThcJgdS0REREREJKQs/fZtamQWBjbOOJmWtdqaG0hERMqdy+VixIgRZGdnExsbW+K+arCGIKfTSVxcHJmZmUcsgGDkdrtJTk5mwIAB2O12s+NINaU6FLOpBsVsqkExm2pQzPTKiHMotFgBaHflRZw+4GKTE0l1pM9BMVuo16DT6SQhIaFUDVZNERDC7HZ7SBb4XqF+fRIcVIdiNtWgmE01KGZTDYrZDItFNSim0uegmC1Ua7As12SpwBwiIiIiIiLlzpmZT9qaLAoyreTnaAV3MZnfZ3YCERExmUawioiIiIhIUFm3NIPFn20AHGR0zSE2PsrsSCIiIlKNaQSriIiIiIiIiIiIyFHSCNYQtHfdMqfTaXKSiuF2u3G5XDidzpCc40OCg+pQzKYaFLOpBsVMuXk55BflBR7n5OB0OkxOJNVNodugwOIGIMLrCNnvXlK16WexmC3Ua3DvZ/vePltJDH9p9pKgkpaWRqNGjcyOISIiIiIiIiIiEtS2bNlCw4YNS9xHDdYQ5PP52Lp1KzExMRiGYXaccud0OmnUqBFbtmwhNjbW7DhSTakOxWyqQTGbalDMphoUs6kGxWyqQTFbqNeg3+8nJyeH+vXrY7GUPMuqpggIQRaL5Yid9VAQGxsbkv8DS3BRHYrZVINiNtWgmE01KGZTDYrZVINitlCuwbi4uFLtp0WuRERERERERERERI6SGqwiIiIiIiIiIiIiR0kNVgk64eHhPPzww4SHh5sdRaox1aGYTTUoZlMNitlUg2I21aCYTTUoZlMN7qNFrkRERERERERERESOkkawioiIiIiIiIiIiBwlNVhFREREREREREREjpIarCIiIiIiIiIiIiJHSQ1WERERERERERERkaOkBquIiIiIiIiIiIjIUVKDVYJGTk4O48aNo2PHjkRHRxMXF0ePHj2YPHkyRUVFZseTEOZyuZg7dy4TJkxg2LBhNGnSBMMwMAyDcePGmR1PqoFdu3bx1ltvcdlll9GuXTuioqIIDw+nYcOGDB06lE8//dTsiFIN/P7774wfP55zzjmHNm3aUKtWLex2O7Vq1aJ379489thj7N692+yYUs08+eSTxT+TDcMwO46EuGnTph1Qb4f7891335kdVUKc0+nkqaeeolevXtSuXbv434X9+vVj3Lhx7Nmzx+yIEoJK8/m390+/fv3MjlvpbGYHECmNTZs20bdvX1JTUwFwOBwUFhaydOlSli5dyowZM5g3bx41a9Y0N6iEpCVLljBkyBCzY0g1lpiYiMfjKd6OiIjAbreTnp5Oeno6s2fPZvDgwXz00Uc4HA4Tk0ooe/PNN0lKSirejoiIIDIykt27d7No0SIWLVrElClT+PzzzznppJNMTCrVxdq1axk/frzZMaQaslgs1K5d+7Cvh4eHV2IaqW7mz5/PJZdcQkZGBgBhYWE4HI7ifxcuWLCAoUOH0qVLF3ODSsipW7duia+73e7iX7b36NGjMiJVKRrBKlWex+Ph7LPPJjU1lXr16pGcnExeXh4ul4v333+fmJgYli9fzmWXXWZ2VAlhNWvWpH///owdO5b33nuPxMREsyNJNeLxeOjZsycvvvgi//zzD/n5+eTm5rJx40auvfZaAObOncuNN95oclIJZT179mTixIn88ssvZGVlkZ+fj9PpJCcnh+nTp1O7dm0yMzMZOnQo2dnZZseVEOfz+bjmmmsoKChQQ18qXaNGjdi+ffth//Tp08fsiBKifv75Z84880wyMjIYNmwYv/32GwUFBWRlZZGXl8eSJUu4//77iYuLMzuqhKCSPve2b9/OfffdV7zv3u8o1Ynh9/v9ZocQKckbb7zBdXDTjjUAAB2zSURBVNddB8CiRYsO+kf0e++9x4gRIwD47rv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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ebno_db = np.linspace(0, 7, 8) # sim SNR range \n", + "\n", + "k = 80\n", + "n = 128\n", + "f, _ = generate_5g_ranking(k, n)\n", + "# init components\n", + "enc = PolarEncoder(f, n)\n", + "dec = OSDecoder(encoder=enc, t=4)\n", + "model = System_Model(enc, dec, cw_estimate=True)\n", + "\n", + "dec_ref = PolarSCLDecoder(f, n, list_size=32)\n", + "model_ref = System_Model(enc, dec_ref, cw_estimate=False)\n", + "\n", + "\n", + "# and run simulation\n", + "ber_plot = PlotBER(f\"Polar n={n},k={k}\")\n", + "\n", + "# reference with Polar SCL\n", + "ber_plot.simulate(model_ref, \n", + " ebno_dbs=ebno_db, \n", + " legend=f\"SCL-{dec_ref.list_size}\",\n", + " max_mc_iter=100, \n", + " num_target_block_errors=2000, \n", + " batch_size=1000, \n", + " soft_estimates=False, \n", + " early_stop=True,\n", + " show_fig=False, \n", + " add_bler=True,\n", + " forward_keyboard_interrupt=True); \n", + "\n", + "# sweep over t\n", + "for t in range(5):\n", + " dec = OSDecoder(encoder=enc, t=t)\n", + " model = System_Model(enc, dec, cw_estimate=True)\n", + " ber_plot.simulate(model, \n", + " ebno_dbs=ebno_db, \n", + " legend=f\"OSD-{dec.t}\",\n", + " max_mc_iter=100, \n", + " num_target_block_errors=2000, \n", + " batch_size=1000, \n", + " soft_estimates=False, \n", + " early_stop=True,\n", + " show_fig=False, \n", + " add_bler=True,\n", + " forward_keyboard_interrupt=True); \n", + "\n", + "\n", + "# ber is not comparable (u_hat vs. c_hat)\n", + "ber_plot(show_ber=False)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Remark**: SCL-32 is not necessarily optimal for longer codes." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", + "---------------------------------------------------------------------------------------------------------------------------------------\n", + " 0.0 | 1.3296e-01 | 3.1857e-01 | 14891 | 112000 | 2230 | 7000 | 8.6 |reached target block errors\n", + " 1.0 | 6.4558e-02 | 1.5746e-01 | 13428 | 208000 | 2047 | 13000 | 0.3 |reached target block errors\n", + " 2.0 | 2.2788e-02 | 5.6389e-02 | 13126 | 576000 | 2030 | 36000 | 0.7 |reached target block errors\n", + " 3.0 | 5.3969e-03 | 1.3350e-02 | 8635 | 1600000 | 1335 | 100000 | 2.0 |reached max iter \n", + " 4.0 | 7.5875e-04 | 1.9200e-03 | 1214 | 1600000 | 192 | 100000 | 2.0 |reached max iter \n", + " 5.0 | 3.6875e-05 | 1.2000e-04 | 59 | 1600000 | 12 | 100000 | 2.1 |reached max iter \n", + " 6.0 | 6.2500e-06 | 1.0000e-05 | 10 | 1600000 | 1 | 100000 | 2.1 |reached max iter \n", + " 7.0 | 0.0000e+00 | 0.0000e+00 | 0 | 1600000 | 0 | 100000 | 2.1 |reached max iter \n", + "\n", + "Simulation stopped as no error occurred @ EbNo = 7.0 dB.\n", + "\n", + "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", + "---------------------------------------------------------------------------------------------------------------------------------------\n", + " 0.0 | 1.5434e-01 | 5.2275e-01 | 19756 | 128000 | 2091 | 4000 | 1.4 |reached target block errors\n", + " 1.0 | 1.0290e-01 | 3.5517e-01 | 19756 | 192000 | 2131 | 6000 | 0.0 |reached target block errors\n", + " 2.0 | 5.6455e-02 | 1.9882e-01 | 19872 | 352000 | 2187 | 11000 | 0.1 |reached target block errors\n", + " 3.0 | 2.7315e-02 | 9.6905e-02 | 18356 | 672000 | 2035 | 21000 | 0.2 |reached target block errors\n", + " 4.0 | 9.7741e-03 | 3.5088e-02 | 17828 | 1824000 | 2000 | 57000 | 0.4 |reached target block errors\n", + " 5.0 | 2.6675e-03 | 9.6300e-03 | 8536 | 3200000 | 963 | 100000 | 0.7 |reached max iter \n", + " 6.0 | 5.4000e-04 | 1.9300e-03 | 1728 | 3200000 | 193 | 100000 | 0.7 |reached max iter \n", + " 7.0 | 9.1250e-05 | 3.1000e-04 | 292 | 3200000 | 31 | 100000 | 0.6 |reached max iter \n", + "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", + "---------------------------------------------------------------------------------------------------------------------------------------\n", + " 0.0 | 9.6812e-02 | 3.4633e-01 | 18588 | 192000 | 2078 | 6000 | 1.6 |reached target block errors\n", + " 1.0 | 4.4288e-02 | 1.6415e-01 | 18424 | 416000 | 2134 | 13000 | 0.1 |reached target block errors\n", + " 2.0 | 1.6648e-02 | 6.3062e-02 | 17048 | 1024000 | 2018 | 32000 | 0.2 |reached target block errors\n", + " 3.0 | 4.0575e-03 | 1.5580e-02 | 12984 | 3200000 | 1558 | 100000 | 0.7 |reached max iter \n", + " 4.0 | 6.1625e-04 | 2.4300e-03 | 1972 | 3200000 | 243 | 100000 | 0.7 |reached max iter \n", + " 5.0 | 5.1250e-05 | 2.0000e-04 | 164 | 3200000 | 20 | 100000 | 0.7 |reached max iter \n", + " 6.0 | 0.0000e+00 | 0.0000e+00 | 0 | 3200000 | 0 | 100000 | 0.7 |reached max iter \n", + "\n", + "Simulation stopped as no error occurred @ EbNo = 6.0 dB.\n", + "\n", + "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", + "---------------------------------------------------------------------------------------------------------------------------------------\n", + " 0.0 | 8.7375e-02 | 3.1486e-01 | 19572 | 224000 | 2204 | 7000 | 1.6 |reached target block errors\n", + " 1.0 | 4.2279e-02 | 1.5646e-01 | 17588 | 416000 | 2034 | 13000 | 0.1 |reached target block errors\n", + " 2.0 | 1.4625e-02 | 5.5486e-02 | 17316 | 1184000 | 2053 | 37000 | 0.3 |reached target block errors\n", + " 3.0 | 3.5088e-03 | 1.3550e-02 | 11228 | 3200000 | 1355 | 100000 | 0.7 |reached max iter \n", + " 4.0 | 4.1625e-04 | 1.6500e-03 | 1332 | 3200000 | 165 | 100000 | 0.7 |reached max iter \n", + " 5.0 | 3.8750e-05 | 1.5000e-04 | 124 | 3200000 | 15 | 100000 | 0.7 |reached max iter \n", + " 6.0 | 0.0000e+00 | 0.0000e+00 | 0 | 3200000 | 0 | 100000 | 0.7 |reached max iter \n", + "\n", + "Simulation stopped as no error occurred @ EbNo = 6.0 dB.\n", + "\n", + "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", + "---------------------------------------------------------------------------------------------------------------------------------------\n", + " 0.0 | 8.8036e-02 | 3.1657e-01 | 19720 | 224000 | 2216 | 7000 | 1.7 |reached target block errors\n", + " 1.0 | 4.0661e-02 | 1.5100e-01 | 18216 | 448000 | 2114 | 14000 | 0.1 |reached target block errors\n", + " 2.0 | 1.4823e-02 | 5.6278e-02 | 17076 | 1152000 | 2026 | 36000 | 0.3 |reached target block errors\n", + " 3.0 | 3.3800e-03 | 1.3080e-02 | 10816 | 3200000 | 1308 | 100000 | 0.7 |reached max iter \n", + " 4.0 | 4.5000e-04 | 1.7400e-03 | 1440 | 3200000 | 174 | 100000 | 0.6 |reached max iter \n", + " 5.0 | 2.6250e-05 | 1.0000e-04 | 84 | 3200000 | 10 | 100000 | 0.7 |reached max iter \n", + " 6.0 | 7.5000e-06 | 3.0000e-05 | 24 | 3200000 | 3 | 100000 | 0.6 |reached max iter \n", + " 7.0 | 0.0000e+00 | 0.0000e+00 | 0 | 3200000 | 0 | 100000 | 0.7 |reached max iter \n", + "\n", + "Simulation stopped as no error occurred @ EbNo = 7.0 dB.\n", + "\n", + "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", + "---------------------------------------------------------------------------------------------------------------------------------------\n", + " 0.0 | 8.8000e-02 | 3.1514e-01 | 19712 | 224000 | 2206 | 7000 | 1.8 |reached target block errors\n", + " 1.0 | 4.2269e-02 | 1.5638e-01 | 17584 | 416000 | 2033 | 13000 | 0.1 |reached target block errors\n", + " 2.0 | 1.5094e-02 | 5.6944e-02 | 17388 | 1152000 | 2050 | 36000 | 0.3 |reached target block errors\n", + " 3.0 | 3.3413e-03 | 1.2970e-02 | 10692 | 3200000 | 1297 | 100000 | 0.8 |reached max iter \n", + " 4.0 | 4.2375e-04 | 1.6700e-03 | 1356 | 3200000 | 167 | 100000 | 0.8 |reached max iter \n", + " 5.0 | 2.5000e-05 | 1.0000e-04 | 80 | 3200000 | 10 | 100000 | 0.7 |reached max iter \n", + " 6.0 | 2.5000e-06 | 1.0000e-05 | 8 | 3200000 | 1 | 100000 | 0.8 |reached max iter \n", + " 7.0 | 0.0000e+00 | 0.0000e+00 | 0 | 3200000 | 0 | 100000 | 0.8 |reached max iter \n", + "\n", + "Simulation stopped as no error occurred @ EbNo = 7.0 dB.\n", + "\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# very short polar codes\n", + "\n", + "ebno_db = np.linspace(0, 7, 8) # sim SNR range \n", + "\n", + "k = 16\n", + "n = 32\n", + "f, _ = generate_5g_ranking(k, n)\n", + "# init components\n", + "enc = PolarEncoder(f, n)\n", + "dec = OSDecoder(encoder=enc, t=4)\n", + "model = System_Model(enc, dec, cw_estimate=True)\n", + "\n", + "dec_ref = PolarSCLDecoder(f, n, list_size=32)\n", + "model_ref = System_Model(enc, dec_ref, cw_estimate=False)\n", + "\n", + "\n", + "# and run simulation\n", + "ber_plot = PlotBER(f\"Polar n={n},k={k}\")\n", + "\n", + "# reference with Polar SCL\n", + "ber_plot.simulate(model_ref, \n", + " ebno_dbs=ebno_db, \n", + " legend=f\"SCL-{dec_ref.list_size}\",\n", + " max_mc_iter=100, \n", + " num_target_block_errors=2000, \n", + " batch_size=1000, \n", + " soft_estimates=False, \n", + " early_stop=True,\n", + " show_fig=False, \n", + " add_bler=True,\n", + " forward_keyboard_interrupt=True); \n", + "\n", + "# sweep over t\n", + "for t in range(5):\n", + " dec = OSDecoder(encoder=enc, t=t)\n", + " model = System_Model(enc, dec, cw_estimate=True)\n", + " ber_plot.simulate(model, \n", + " ebno_dbs=ebno_db, \n", + " legend=f\"OSD-{dec.t}\",\n", + " max_mc_iter=100, \n", + " num_target_block_errors=2000, \n", + " batch_size=1000, \n", + " soft_estimates=False, \n", + " early_stop=True,\n", + " show_fig=False, \n", + " add_bler=True,\n", + " forward_keyboard_interrupt=True); \n", + "\n", + "\n", + "# ber is not comparable (u_hat vs. c_hat)\n", + "ber_plot(show_ber=False)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Evaluate Convolutional Codes & Viterbi" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", + "---------------------------------------------------------------------------------------------------------------------------------------\n", + " 0.0 | 1.1179e-01 | 9.2033e-01 | 40244 | 360000 | 2761 | 3000 | 1.2 |reached target block errors\n", + " 1.0 | 4.5253e-02 | 6.7300e-01 | 16291 | 360000 | 2019 | 3000 | 0.0 |reached target block errors\n", + " 2.0 | 1.2489e-02 | 3.2414e-01 | 10491 | 840000 | 2269 | 7000 | 0.1 |reached target block errors\n", + " 3.0 | 2.7575e-03 | 1.0905e-01 | 6287 | 2280000 | 2072 | 19000 | 0.3 |reached target block errors\n", + " 4.0 | 6.1399e-04 | 3.6411e-02 | 4126 | 6720000 | 2039 | 56000 | 0.8 |reached target block errors\n", + " 5.0 | 1.5267e-04 | 1.1830e-02 | 1832 | 12000000 | 1183 | 100000 | 1.4 |reached max iter \n", + " 6.0 | 3.7583e-05 | 3.5600e-03 | 451 | 12000000 | 356 | 100000 | 1.4 |reached max iter \n", + " 7.0 | 9.4167e-06 | 9.9000e-04 | 113 | 12000000 | 99 | 100000 | 1.4 |reached max iter \n", + "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", + "---------------------------------------------------------------------------------------------------------------------------------------\n", + " 0.0 | 1.8251e-01 | 9.9190e-01 | 91983 | 504000 | 2083 | 2100 | 1.6 |reached target block errors\n", + " 1.0 | 1.2742e-01 | 9.6333e-01 | 64222 | 504000 | 2023 | 2100 | 0.4 |reached target block errors\n", + " 2.0 | 7.6875e-02 | 8.2080e-01 | 46125 | 600000 | 2052 | 2500 | 0.4 |reached target block errors\n", + " 3.0 | 3.8681e-02 | 5.3079e-01 | 35277 | 912000 | 2017 | 3800 | 0.7 |reached target block errors\n", + " 4.0 | 1.4028e-02 | 2.4036e-01 | 28281 | 2016000 | 2019 | 8400 | 1.4 |reached target block errors\n", + " 5.0 | 4.0779e-03 | 8.2300e-02 | 9787 | 2400000 | 823 | 10000 | 1.7 |reached max iter \n", + " 6.0 | 6.8542e-04 | 1.7200e-02 | 1645 | 2400000 | 172 | 10000 | 1.7 |reached max iter \n", + " 7.0 | 1.3375e-04 | 3.7000e-03 | 321 | 2400000 | 37 | 10000 | 1.7 |reached max iter \n", + "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", + "---------------------------------------------------------------------------------------------------------------------------------------\n", + " 0.0 | 1.3766e-01 | 9.7429e-01 | 69379 | 504000 | 2046 | 2100 | 1.8 |reached target block errors\n", + " 1.0 | 8.0845e-02 | 8.6542e-01 | 46567 | 576000 | 2077 | 2400 | 0.4 |reached target block errors\n", + " 2.0 | 3.4628e-02 | 5.5917e-01 | 29919 | 864000 | 2013 | 3600 | 0.6 |reached target block errors\n", + " 3.0 | 9.0503e-03 | 2.1989e-01 | 19983 | 2208000 | 2023 | 9200 | 1.5 |reached target block errors\n", + " 4.0 | 1.6371e-03 | 6.0400e-02 | 3929 | 2400000 | 604 | 10000 | 1.7 |reached max iter \n", + " 5.0 | 1.8167e-04 | 1.1900e-02 | 436 | 2400000 | 119 | 10000 | 1.7 |reached max iter \n", + " 6.0 | 4.7500e-05 | 4.1000e-03 | 114 | 2400000 | 41 | 10000 | 1.7 |reached max iter \n", + " 7.0 | 7.9167e-06 | 8.0000e-04 | 19 | 2400000 | 8 | 10000 | 1.7 |reached max iter \n", + "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", + "---------------------------------------------------------------------------------------------------------------------------------------\n", + " 0.0 | 1.1702e-01 | 9.5619e-01 | 58978 | 504000 | 2008 | 2100 | 1.9 |reached target block errors\n", + " 1.0 | 5.7415e-02 | 7.6923e-01 | 35827 | 624000 | 2000 | 2600 | 0.5 |reached target block errors\n", + " 2.0 | 1.9572e-02 | 4.2083e-01 | 22547 | 1152000 | 2020 | 4800 | 0.9 |reached target block errors\n", + " 3.0 | 3.8850e-03 | 1.3230e-01 | 9324 | 2400000 | 1323 | 10000 | 1.9 |reached max iter \n", + " 4.0 | 6.3583e-04 | 3.5100e-02 | 1526 | 2400000 | 351 | 10000 | 1.8 |reached max iter \n", + " 5.0 | 1.3792e-04 | 1.1400e-02 | 331 | 2400000 | 114 | 10000 | 1.8 |reached max iter \n", + " 6.0 | 3.9583e-05 | 4.1000e-03 | 95 | 2400000 | 41 | 10000 | 1.8 |reached max iter \n", + " 7.0 | 9.1667e-06 | 1.0000e-03 | 22 | 2400000 | 10 | 10000 | 1.9 |reached max iter \n", + "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", + "---------------------------------------------------------------------------------------------------------------------------------------\n", + " 0.0 | 1.0047e-01 | 9.2864e-01 | 53048 | 528000 | 2043 | 2200 | 3.7 |reached target block errors\n", + " 1.0 | 4.4592e-02 | 6.9200e-01 | 32106 | 720000 | 2076 | 3000 | 2.7 |reached target block errors\n", + " 2.0 | 1.3148e-02 | 3.3033e-01 | 19249 | 1464000 | 2015 | 6100 | 5.6 |reached target block errors\n", + " 3.0 | 2.9642e-03 | 1.1710e-01 | 7114 | 2400000 | 1171 | 10000 | 9.2 |reached max iter \n", + " 4.0 | 5.4875e-04 | 3.4400e-02 | 1317 | 2400000 | 344 | 10000 | 9.2 |reached max iter \n", + " 5.0 | 1.6292e-04 | 1.3200e-02 | 391 | 2400000 | 132 | 10000 | 9.2 |reached max iter \n", + " 6.0 | 4.2917e-05 | 4.2000e-03 | 103 | 2400000 | 42 | 10000 | 9.2 |reached max iter \n", + " 7.0 | 4.5833e-06 | 5.0000e-04 | 11 | 2400000 | 5 | 10000 | 9.2 |reached max iter \n", + "Note: Required memory complexity is large for the given code parameters and t=4. Please consider small batch-sizes to keep the inference complexity small and activate XLA mode if possible.\n", + "EbNo [dB] | BER | BLER | bit errors | num bits | block errors | num blocks | runtime [s] | status\n", + "---------------------------------------------------------------------------------------------------------------------------------------\n", + " 0.0 | 9.7538e-02 | 9.2864e-01 | 51500 | 528000 | 2043 | 2200 | 53.6 |reached target block errors\n", + " 1.0 | 4.2024e-02 | 6.8000e-01 | 30257 | 720000 | 2040 | 3000 | 70.6 |reached target block errors\n", + " 2.0 | 1.2359e-02 | 3.3148e-01 | 18093 | 1464000 | 2022 | 6100 | 143.0 |reached target block errors\n", + " 3.0 | 2.7192e-03 | 1.1180e-01 | 6526 | 2400000 | 1118 | 10000 | 234.1 |reached max iter \n", + " 4.0 | 5.8750e-04 | 3.5000e-02 | 1410 | 2400000 | 350 | 10000 | 234.1 |reached max iter \n", + " 5.0 | 1.5042e-04 | 1.2500e-02 | 361 | 2400000 | 125 | 10000 | 234.1 |reached max iter \n", + " 6.0 | 4.0833e-05 | 4.2000e-03 | 98 | 2400000 | 42 | 10000 | 234.1 |reached max iter \n", + " 7.0 | 8.3333e-06 | 9.0000e-04 | 20 | 2400000 | 9 | 10000 | 233.9 |reached max iter \n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# very short polar codes\n", + "\n", + "ebno_db = np.linspace(0, 7, 8) # sim SNR range \n", + "\n", + "k = 120\n", + "constraint_length = 5\n", + "r = 0.5\n", + "\n", + "# init components\n", + "enc = ConvEncoder(rate=r, constraint_length=constraint_length)\n", + "# init encoder\n", + "enc(tf.zeros((1, k)))\n", + "\n", + "dec_ref = ViterbiDecoder(rate=r, constraint_length=constraint_length)\n", + "model_ref = System_Model(enc, dec_ref, cw_estimate=False)\n", + "\n", + "\n", + "# and run simulation\n", + "ber_plot = PlotBER(f\"Convolutional Codes r={r}, k={k}\")\n", + "\n", + "# reference with Polar SCL\n", + "ber_plot.simulate(model_ref, \n", + " ebno_dbs=ebno_db, \n", + " legend=f\"Viterbi\",\n", + " max_mc_iter=100, \n", + " num_target_block_errors=2000, \n", + " batch_size=1000, \n", + " soft_estimates=False, \n", + " early_stop=True,\n", + " show_fig=False, \n", + " add_bler=True,\n", + " forward_keyboard_interrupt=True); \n", + "\n", + "# sweep over t\n", + "for t in range(5):\n", + " dec = OSDecoder(encoder=enc, t=t)\n", + " model = System_Model(enc, dec, cw_estimate=True)\n", + " ber_plot.simulate(model, \n", + " ebno_dbs=ebno_db, \n", + " legend=f\"OSD-{dec.t}\",\n", + " max_mc_iter=100, \n", + " num_target_block_errors=2000, \n", + " batch_size=100, \n", + " soft_estimates=False, \n", + " early_stop=True,\n", + " show_fig=False, \n", + " add_bler=True,\n", + " forward_keyboard_interrupt=True); \n", + "\n", + "\n", + "# ber is not comparable (u_hat vs. c_hat)\n", + "ber_plot(show_ber=False)\n" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/test/unit/fec/test_conv_decoding.py b/test/unit/fec/test_conv_decoding.py index 001946fd..043bd2eb 100644 --- a/test/unit/fec/test_conv_decoding.py +++ b/test/unit/fec/test_conv_decoding.py @@ -8,6 +8,7 @@ import sys sys.path.append("../") +from itertools import product import unittest import numpy as np import tensorflow as tf @@ -28,40 +29,6 @@ from sionna.utils import BinarySource from sionna.channel import AWGN -class ConvExample(tf.keras.Model): - def __init__(self, - k, - rate, - constraint_length): - super().__init__() - self.rate = rate - self.k = k - - self.binary_source = BinarySource() - self.encoder = ConvEncoder(rate=rate, - constraint_length=constraint_length) - self.channel = AWGN() - self.decoder = ViterbiDecoder(self.encoder.gen_poly, method='soft_llr') - - def call(self, ebno, batch_size): - # Generate a batch of random bit vectors - no = tf.cast((1/self.rate) * (10 ** (-ebno / 10)),tf.float32) - - msg = tf.cast(self.binary_source([batch_size, self.k]), tf.int32) - cw = self.encoder(msg) - x = 2 * cw - 1 - - x_cpx = tf.complex(tf.cast(x, tf.float32), tf.zeros(x.shape)) - - y_cpx = self.channel((x_cpx, no)) - y = tf.math.real(y_cpx) - llr = 2.*y/no - - msghat = tf.cast(self.decoder(llr), tf.int32) - - errs_ = int(tf.math.count_nonzero(msghat-msg)) - return errs_ - class TestViterbiDecoding(unittest.TestCase): @@ -71,17 +38,24 @@ def test_output_dim(self): bs = 10 coderates = [1/2, 1/3] - ks = [10, 20, 40, 100, 1000] + ks = [10, 22, 40] + muterm = 3 + for k, rate in product(ks, coderates): + for dec in ( + ViterbiDecoder(rate=rate, constraint_length=5), + ViterbiDecoder(rate=rate, constraint_length=3, rsc=True), + ViterbiDecoder(rate=rate, constraint_length=muterm+1, terminate=True)): - for rate in coderates: - for k in ks: n = int(k/rate) - dec = ViterbiDecoder(rate=rate, constraint_length=5) + if dec.terminate: + n += int((muterm)/rate) + # all-zero with BPSK (no noise);logits c = -10. * np.ones([bs, n]) u = dec(c).numpy() - self.assertTrue(u.shape[-1]==k) # also check that all-zero input yields all-zero output + self.assertTrue(u.shape[-1]==k) + u_hat = np.zeros([bs, k]) self.assertTrue(np.array_equal(u, u_hat)) @@ -92,116 +66,175 @@ def test_numerical_stab(self): source = GaussianPriorSource() coderates = [1/2, 1/3] - ks = [10, 20, 40, 100, 1000] + ks = [10, 20, 60] + + for k, rate in product(ks, coderates): + n = int(k/rate) + dec = ViterbiDecoder(rate=rate, constraint_length=5) + + # case 1: extremely large inputs + c = source([[bs, n], 0.0001]) + # llrs + u1 = dec(c).numpy() + # no nan + self.assertFalse(np.any(np.isnan(u1))) + #no inftfy + self.assertFalse(np.any(np.isinf(u1))) + self.assertFalse(np.any(np.isneginf(u1))) + + # case 2: zero input + c = tf.zeros([bs, n]) + # llrs + u2 = dec(c).numpy() + # no nan + self.assertFalse(np.any(np.isnan(u2))) + #no inftfy + self.assertFalse(np.any(np.isinf(u2))) + self.assertFalse(np.any(np.isneginf(u2))) - for rate in coderates: - for k in ks: - n = int(k/rate) - dec = ViterbiDecoder(rate=rate, constraint_length=5) + def test_init(self): + """Test different init methods as described in the docstring. + Also test that both implementations lead to the same result.""" - # case 1: extremely large inputs - c = source([[bs, n], 0.0001]) - # llrs - u1 = dec(c).numpy() - # no nan - self.assertFalse(np.any(np.isnan(u1))) - #no inftfy - self.assertFalse(np.any(np.isinf(u1))) - self.assertFalse(np.any(np.isneginf(u1))) - - # case 2: zero input - c = tf.zeros([bs, n]) - # llrs - u2 = dec(c).numpy() - # no nan - self.assertFalse(np.any(np.isnan(u2))) - #no inftfy - self.assertFalse(np.any(np.isinf(u2))) - self.assertFalse(np.any(np.isneginf(u2))) + bs = 10 + n = 120 + no = 0.1 + source = GaussianPriorSource() - def test_identity(self): - """test that info bits can be recovered if no noise is added""" + coderates = [1/3, 1/2] + constraint_lengths = [3, 4, 5, 6] + for r, cs in product(coderates, constraint_lengths): - def test_identity_(enc, msg): - cw = enc(msg) - # BPSK modulation, no noise - code_syms = 20. * (2. * cw - 1) - u_hat = ViterbiDecoder(gen_poly=enc.gen_poly, method='soft_llr')(code_syms) - self.assertTrue(np.array_equal(msg.numpy(), u_hat.numpy())) + enc = ConvEncoder(rate=r, constraint_length=cs) + + # method 1: explicitly provide enc + dec1 = ViterbiDecoder(gen_poly=enc.gen_poly) + + # method 2: provide rate and constraint length + dec2 = ViterbiDecoder(rate=r, constraint_length=cs) + + llr = source([[bs, n], no]) - # No modulation, 0, 1 bits - code_syms = cw - u_hat = ViterbiDecoder(gen_poly=enc.gen_poly, method='hard')(code_syms) - self.assertTrue(np.array_equal(msg.numpy(), u_hat.numpy())) + x_hat1 = dec1(llr) + x_hat2 = dec2(llr) - # BPSK symbols with AWGN noise - bs, n = cw.get_shape().as_list() - code_syms = 6. * (2. * cw - 1) + np.random.randn(bs,n) - u_hat = ViterbiDecoder(gen_poly=enc.gen_poly, method='soft_llr')(code_syms) - self.assertTrue(np.array_equal(msg.numpy(), u_hat.numpy())) + #verify that both decoders produce the same result + self.assertTrue(np.array_equal(x_hat1.numpy(), x_hat2.numpy())) - return + def test_identity(self): + """Test that info bits can be recovered if no noise is added.""" + + def test_identity_(enc, msg, rsc=False): + cw = enc(msg) + + # test that encoder can be directly provided + for api_mode in ("poly", "enc"): + + # BPSK modulation, no noise + code_syms = 20. * (2. * cw - 1) + if api_mode=="poly": + u_hat = ViterbiDecoder( + gen_poly=enc.gen_poly, method='soft_llr', rsc=rsc)(code_syms) + else: + u_hat = ViterbiDecoder(encoder=enc, method='soft_llr')(code_syms) + self.assertTrue(np.array_equal(msg.numpy(), u_hat.numpy())) + + # No modulation, 0, 1 bits + code_syms = cw + if api_mode=="poly": + u_hat = ViterbiDecoder( + gen_poly=enc.gen_poly, method='hard', rsc=rsc)(code_syms) + else: + u_hat = ViterbiDecoder(encoder=enc, method='hard')(code_syms) + self.assertTrue(np.array_equal(msg.numpy(), u_hat.numpy())) + + # BPSK symbols with AWGN noise + bs, n = cw.get_shape().as_list() + code_syms = 6. * (2. * cw - 1) + np.random.randn(bs,n) + if api_mode=="poly": + u_hat = ViterbiDecoder( + gen_poly=enc.gen_poly, method='soft_llr', rsc=rsc)(code_syms) + else: + u_hat = ViterbiDecoder(encoder=enc, method='soft_llr')(code_syms) + self.assertTrue(np.array_equal(msg.numpy(), u_hat.numpy())) bs = 10 + k = 35 coderates = [1/2, 1/3] - ks = [10, 50, 100, 100] - mus = [3, 4, 5, 6 ,7, 8] # constraint length - - for k in ks: - for rate in coderates: - for mu in mus: - u = BinarySource()([bs, k]) - enc = ConvEncoder(rate=rate, constraint_length=mu) - test_identity_(enc, u) + mus = [3, 8] # constraint length + for rate, mu in product(coderates, mus): u = BinarySource()([bs, k]) - - enc = ConvEncoder(gen_poly=['101', '111', '111', '111']) + enc = ConvEncoder(rate=rate, constraint_length=mu) test_identity_(enc, u) - enc = ConvEncoder(gen_poly=['1101', '1111']) + enc = ConvEncoder(rate=rate, constraint_length=mu, rsc=True) + test_identity_(enc, u, rsc=True) + + for gp in (['101', '111', '111', '111'], ['1101', '1111']): + u = BinarySource()([bs, k]) + enc = ConvEncoder(gen_poly=gp) test_identity_(enc, u) def test_keras(self): """Test that Keras model can be compiled (supports dynamic shapes)""" bs = 10 - n = 64 + n1 = 64 + + muterm = 3 + rterm = 1/3 + n2 = 96 + int(muterm/rterm) + source = BinarySource() - inputs = tf.keras.Input(shape=(n), dtype=tf.float32) + + inputs = tf.keras.Input(shape=(n1), dtype=tf.float32) x = ViterbiDecoder(rate=1/2, constraint_length=3)(inputs) model = tf.keras.Model(inputs=inputs, outputs=x) - b = source([bs,n]) - model(b) - # call twice to see that bs can change - b2 = source([bs+1,n]) - model(b2) - model.summary() + # Keras Model using termination + inputs = tf.keras.Input(shape=(n2), dtype=tf.float32) + xterm = ViterbiDecoder( + rate=rterm, constraint_length=muterm+1, terminate=True)(inputs) + modelterm = tf.keras.Model(inputs=inputs, outputs=xterm) + + for n, mod in zip((n1,n2),(model, modelterm)): + b = source([bs, n]) + mod(b) + # call twice to see that bs can change + b2 = source([bs+1,n]) + mod(b2) + mod.summary() def test_multi_dimensional(self): """Test against arbitrary shapes """ k = 100 - n = 200 + rate = 1/2 + mu = 3 source = BinarySource() - dec = ViterbiDecoder(rate=1/2, constraint_length=3) + for term in (True, False): + dec = ViterbiDecoder(rate=rate, constraint_length=mu+1, terminate=term) + + n = int(k/rate) + if dec.terminate: + n += int(mu/rate) - b = source([100, n]) - b_res = tf.reshape(b, [4, 5, 5, n]) + b = source([100, n]) + b_res = tf.reshape(b, [4, 5, 5, n]) - # encode 2D Tensor - c = dec(b).numpy() - # encode 4D Tensor - c_res = dec(b_res).numpy() + # encode 2D Tensor + c = dec(b).numpy() + # encode 4D Tensor + c_res = dec(b_res).numpy() - # test that shape was preserved - self.assertTrue(c_res.shape[:-1]==b_res.shape[:-1]) + # test that shape was preserved + self.assertTrue(c_res.shape[:-1]==b_res.shape[:-1]) - # and reshape to 2D shape - c_res = tf.reshape(c_res, [100, k]) - # both version should yield same result - self.assertTrue(np.array_equal(c, c_res)) + # and reshape to 2D shape + c_res = tf.reshape(c_res, [100, k]) + # both version should yield same result + self.assertTrue(np.array_equal(c, c_res)) def test_batch(self): """Test that all samples in batch yield same output (for same input). @@ -296,7 +329,7 @@ def run_graph_xla(u): k = 100 n = 128 source = BinarySource() - dec = ViterbiDecoder(rate=1/2, constraint_length=3) + dec = ViterbiDecoder(rate=1/2, constraint_length=5) # test that for arbitrary input only 0,1 values are outputed u = source([bs, n]) @@ -324,13 +357,20 @@ def test_output_dim(self): codeword.""" bs = 10 - coderates = [1/3, 1/3] - ks = [10, 20, 40, 100, 1000] + coderates = [1/2, 1/3] + ks = [10, 45] + muterm = 5 + + for k, rate in product(ks, coderates): + for dec in ( + BCJRDecoder(rate=rate, constraint_length=5), + BCJRDecoder(rate=rate, constraint_length=3, rsc=True), + BCJRDecoder(rate=rate, constraint_length=muterm+1, terminate=True)): - for rate in coderates: - for k in ks: n = int(k/rate) - dec = BCJRDecoder(rate=rate, constraint_length=5) + if dec.terminate: + n += int((muterm)/rate) + # all-zero with BPSK (no noise);logits c = -10. * np.ones([bs, n]) u1 = dec(c).numpy() @@ -339,6 +379,39 @@ def test_output_dim(self): u_hat = np.zeros([bs, k]) self.assertTrue(np.array_equal(u1, u_hat)) + def test_numerical_stab(self): + """Test for numerical stability (no nan or infty as output)""" + + bs = 10 + coderates = [1/2, 1/3] + ks = [22, 55] + + source = GaussianPriorSource() + + for k, rate in product(ks, coderates): + n = int(k/rate) + dec = BCJRDecoder(rate=rate, constraint_length=5) + + # case 1: extremely large inputs + c = source([[bs, n], 0.0001]) + # llrs + u1 = dec(c).numpy() + # no nan + self.assertFalse(np.any(np.isnan(u1))) + #no inftfy + self.assertFalse(np.any(np.isinf(u1))) + self.assertFalse(np.any(np.isneginf(u1))) + + # case 2: zero input + c = tf.zeros([bs, n]) + # llrs + u2 = dec(c).numpy() + # no nan + self.assertFalse(np.any(np.isnan(u2))) + #no inftfy + self.assertFalse(np.any(np.isinf(u2))) + self.assertFalse(np.any(np.isneginf(u2))) + def test_init(self): """Test different init methods as described in the docstring. Also test that both implementations lead to the same result.""" @@ -350,136 +423,132 @@ def test_init(self): coderates = [1/3, 1/2] constraint_lengths = [3, 4, 5, 6] - for r in coderates: - for cs in constraint_lengths: - enc = ConvEncoder(rate=r, constraint_length=cs) + for r, cs in product(coderates, constraint_lengths): - # method 1: explicitly provide enc - dec1 = BCJRDecoder(gen_poly=enc.gen_poly) + enc = ConvEncoder(rate=r, constraint_length=cs) - # method 2: provide rate and constraint length - dec2 = BCJRDecoder(rate=r, constraint_length=cs) + # method 1: explicitly provide enc + dec1 = BCJRDecoder(gen_poly=enc.gen_poly) - llr = source([[bs, n], no]) + # method 2: provide rate and constraint length + dec2 = BCJRDecoder(rate=r, constraint_length=cs) - x_hat1 = dec1(llr) - x_hat2 = dec2(llr) + llr = source([[bs, n], no]) - #verify that both decoders produce the same result - self.assertTrue(np.array_equal(x_hat1.numpy(), x_hat2.numpy())) + x_hat1 = dec1(llr) + x_hat2 = dec2(llr) - def test_numerical_stab(self): - """Test for numerical stability (no nan or infty as output)""" - - bs = 10 - coderates = [1/2, 1/3] - ks = [10, 20, 40, 100, 1000] - - source = GaussianPriorSource() - - for rate in coderates: - for k in ks: - n = int(k/rate) - dec = BCJRDecoder(rate=rate, constraint_length=5) - - # case 1: extremely large inputs - c = source([[bs, n], 0.0001]) - # llrs - u1 = dec(c).numpy() - # no nan - self.assertFalse(np.any(np.isnan(u1))) - #no inftfy - self.assertFalse(np.any(np.isinf(u1))) - self.assertFalse(np.any(np.isneginf(u1))) - - # case 2: zero input - c = tf.zeros([bs, n]) - # llrs - u2 = dec(c).numpy() - # no nan - self.assertFalse(np.any(np.isnan(u2))) - #no inftfy - self.assertFalse(np.any(np.isinf(u2))) - self.assertFalse(np.any(np.isneginf(u2))) + #verify that both decoders produce the same result + self.assertTrue(np.array_equal(x_hat1.numpy(), x_hat2.numpy())) def test_identity(self): """test that info bits can be recovered if no noise is added""" - def test_identity_(enc, msg): + def test_identity_(enc, msg, rsc=False): cw = enc(msg) - # BPSK modulation, no noise - code_syms = 20. * (2. * cw - 1) - u_hat = BCJRDecoder(gen_poly=enc.gen_poly)(code_syms) - self.assertTrue(np.array_equal(msg.numpy(), u_hat.numpy())) - - # BPSK symbols with AWGN noise - bs, n = cw.get_shape().as_list() - code_syms = 6. * (2. * cw - 1) + np.random.randn(bs,n) - u_hat = BCJRDecoder(gen_poly=enc.gen_poly)(code_syms) - self.assertTrue(np.array_equal(msg.numpy(), u_hat.numpy())) - return - - bs = 10 + # test that encoder can be directly provided + for api_mode, alg in product( + ("poly", "enc"), ("map", "log", "maxlog")): + + # BPSK modulation, no noise + code_syms = 20. * (2. * cw - 1) + if api_mode=="poly": + u_hat = BCJRDecoder(gen_poly=enc.gen_poly, + algorithm=alg, rsc=rsc)(code_syms) + else: + u_hat = BCJRDecoder(encoder=enc, algorithm=alg)(code_syms) + self.assertTrue(np.array_equal(msg.numpy(), u_hat.numpy())) + + # BPSK symbols with AWGN noise + bs, n = cw.get_shape().as_list() + code_syms = 6. * (2. * cw - 1) + np.random.randn(bs,n) + if api_mode=="poly": + u_hat = BCJRDecoder(gen_poly=enc.gen_poly, + algorithm=alg, rsc=rsc)(code_syms) + else: + u_hat = BCJRDecoder(encoder=enc, algorithm=alg)(code_syms) + self.assertTrue(np.array_equal(msg.numpy(), u_hat.numpy())) + + bs = 5 coderates = [1/2, 1/3] - ks = [10, 50, 100, 100] - mus = [3, 4, 5, 6 ,7, 8] # constraint length - - for k in ks: - for rate in coderates: - for mu in mus: - u = BinarySource()([bs, k]) - enc = ConvEncoder(rate=rate, constraint_length=mu) - test_identity_(enc, u) + k = 40 + mus = [3, 8] # constraint length + for rate, mu in product(coderates, mus): u = BinarySource()([bs, k]) - - enc = ConvEncoder(gen_poly=['101', '111', '111', '111']) + enc = ConvEncoder(rate=rate, constraint_length=mu) test_identity_(enc, u) - enc = ConvEncoder(gen_poly=['1101', '1111']) + enc = ConvEncoder(rate=rate, constraint_length=mu, rsc=True) + test_identity_(enc, u, rsc=True) + + for gp in ( + ['101', '111', '111'], + ['1101', '1111']): + u = BinarySource()([bs, k]) + enc = ConvEncoder(gen_poly=gp) test_identity_(enc, u) def test_keras(self): """Test that Keras model can be compiled (supports dynamic shapes)""" bs = 10 - n = 64 + n1 = 64 + + muterm = 3 + rterm = 1/3 + n2 = 96 + int(muterm/rterm) + source = BinarySource() - inputs = tf.keras.Input(shape=(n), dtype=tf.float32) + + inputs = tf.keras.Input(shape=(n1), dtype=tf.float32) x = BCJRDecoder(rate=1/2, constraint_length=3)(inputs) model = tf.keras.Model(inputs=inputs, outputs=x) - b = source([bs,n]) - model(b) - # call twice to see that bs can change - b2 = source([bs+1,n]) - model(b2) - model.summary() + # Keras Model using termination + inputs = tf.keras.Input(shape=(n2), dtype=tf.float32) + xterm = BCJRDecoder( + rate=rterm, constraint_length=muterm+1, terminate=True)(inputs) + modelterm = tf.keras.Model(inputs=inputs, outputs=xterm) + + for n, mod in zip((n1,n2),(model, modelterm)): + b = source([bs,n]) + mod(b) + # call twice to see that bs can change + b2 = source([bs+1,n]) + mod(b2) + mod.summary() def test_multi_dimensional(self): """Test against arbitrary shapes """ - k = 100 - n = 200 + k = 40 + rate = 1/2 + mu = 3 source = BinarySource() - dec = BCJRDecoder(rate=1/2, constraint_length=3) + for term in (True, False): + dec = BCJRDecoder(rate=rate, constraint_length=mu+1, terminate=term) - b = source([100, n]) - b_res = tf.reshape(b, [4, 5, 5, n]) + n = int(k/rate) + if dec.terminate: + n += int(mu/rate) - # encode 2D Tensor - c = dec(b).numpy() - # encode 4D Tensor - c_res = dec(b_res).numpy() + b = source([100, n]) + b_res = tf.reshape(b, [4, 5, 5, n]) - # test that shape was preserved - self.assertTrue(c_res.shape[:-1]==b_res.shape[:-1]) + # encode 2D Tensor + c = dec(b).numpy() + # encode 4D Tensor + c_res = dec(b_res).numpy() - # and reshape to 2D shape - c_res = tf.reshape(c_res, [100, k]) - # both version should yield same result - self.assertTrue(np.array_equal(c, c_res)) + # test that shape was preserved + self.assertTrue(c_res.shape[:-1]==b_res.shape[:-1]) + + # and reshape to 2D shape + c_res = tf.reshape(c_res, [100, k]) + # both version should yield same result + self.assertTrue(np.array_equal(c, c_res)) def test_batch(self): """Test that all samples in batch yield same output (for same input). @@ -570,10 +639,9 @@ def run_graph_xla(u): return dec(u) bs = 10 - k = 100 n = 128 source = BinarySource() - dec = BCJRDecoder(rate=1/2, constraint_length=3) + dec = BCJRDecoder(rate=1/2, constraint_length=5) # test that for arbitrary input only 0,1 values are outputed u = source([bs, n]) @@ -592,3 +660,18 @@ def run_graph_xla(u): x = run_graph_xla(u).numpy() u = source([bs+1, n]) x = run_graph_xla(u).numpy() + + def test_dynamic_shapes(self): + """Test for dynamic (=unknown) batch-sizes""" + + n = 1536 + enc = ConvEncoder(gen_poly=('1101', '1011'), rate=1/3, terminate=False) + dec = BCJRDecoder(enc) + + @tf.function(jit_compile=True) + def run_graph(batch_size): + llr_ch = tf.zeros((batch_size, n)) + u_hat = dec(llr_ch) + return u_hat + + run_graph(tf.constant(1)) diff --git a/test/unit/fec/test_conv_encoding.py b/test/unit/fec/test_conv_encoding.py index 5c0aef7f..4501a6b8 100644 --- a/test/unit/fec/test_conv_encoding.py +++ b/test/unit/fec/test_conv_encoding.py @@ -7,6 +7,7 @@ except ImportError as e: import sys sys.path.append("../") +from itertools import product import unittest import numpy as np import tensorflow as tf @@ -29,13 +30,20 @@ def test_output_dim(self): r"""Test with allzero codeword that output dims are correct (=n) and output also equals all-zero.""" bs = 10 + mu = 4 coderates = [1/2, 1/3] ks = [10, 20, 50, 100] - for rate in coderates: - for k in ks: - n = int(k/rate) # calculate coderate - enc = ConvEncoder(rate=rate, constraint_length=5) + for rate, k in product(coderates, ks): + n = int(k/rate) # calculate coderate + for enc in ( + ConvEncoder(rate=rate, constraint_length=mu+1), + ConvEncoder(rate=rate, constraint_length=mu+1, rsc=True), + ConvEncoder(rate=rate, constraint_length=mu+1, terminate=True)): + + if enc.terminate: + n+= int(mu/rate) + u = np.zeros([bs, k]) c = enc(u).numpy() self.assertTrue(c.shape[-1]==n) @@ -46,9 +54,12 @@ def test_output_dim(self): # test that output dim can change (in eager mode) k = k+1 # increase length n = int(k/rate) # calculate coderate + if enc.terminate is True: + n+= int(mu/rate) u = np.zeros([bs, k]) c = enc(u).numpy() self.assertTrue(c.shape[-1]==n) + # also check that all-zero input yields all-zero output c_hat = np.zeros([bs, n]) self.assertTrue(np.array_equal(c, c_hat)) @@ -70,14 +81,23 @@ def test_invalid_inputs(self): for mu in constraint_length_valid: with self.assertRaises(AssertionError): enc = ConvEncoder(rate=rate, constraint_length= mu) + enc = ConvEncoder(rate=rate, rsc=True) + enc = ConvEncoder(rate=rate, terminate=True) + enc = ConvEncoder(rate=rate, rsc=True, terminate=True) gmat = [['101', '111', '000'], ['000', '010', '011']] with self.assertRaises(AssertionError): enc = ConvEncoder(gen_poly=gmat) + enc = ConvEncoder(gen_poly=gmat, rsc=True) def test_polynomial_input(self): r"""Test that different formats of input polynomials are accepted and raises exceptions when the generator polynomials fail assertions.""" + def util_check_assertion_err(gen_poly_, msg_): + with self.assertRaises(AssertionError) as exception_context: + enc = ConvEncoder(gen_poly=gen_poly_) + self.assertEqual(str(exception_context.exception), msg_) + bs = 10 k = 100 rate = 1/2 @@ -89,17 +109,15 @@ def test_polynomial_input(self): g = [g1, g2] for gen_poly in g: - enc = ConvEncoder(gen_poly=gen_poly) - c = enc(u).numpy() - self.assertTrue(c.shape[-1]==n) - # also check that all-zero input yields all-zero output - c_hat = np.zeros([bs, n]) - self.assertTrue(np.array_equal(c, c_hat)) + for enc in ( + ConvEncoder(gen_poly=gen_poly), + ConvEncoder(gen_poly=gen_poly, rsc=True)): - def util_check_assertion_err(gen_poly_, msg_): - with self.assertRaises(AssertionError) as exception_context: - enc = ConvEncoder(gen_poly=gen_poly_) - self.assertEqual(str(exception_context.exception), msg_) + c = enc(u).numpy() + self.assertTrue(c.shape[-1]==n) + # also check that all-zero input yields all-zero output + c_hat = np.zeros([bs, n]) + self.assertTrue(np.array_equal(c, c_hat)) gs = [ ['1001', '111'], @@ -107,8 +125,8 @@ def util_check_assertion_err(gen_poly_, msg_): ('1211', '1101')] msg_s = [ "Each polynomial must be of same length.", - "Each polynomial must be a string.", - "Each Polynomial must be a string of 0/1 s." + "Each polynomial must be a string.", + "Each Polynomial must be a string of 0/1 s." ] for idx, g in enumerate(gs): util_check_assertion_err(g,msg_s[idx]) @@ -120,51 +138,66 @@ def test_keras(self): source = BinarySource() inputs = tf.keras.Input(shape=(k), dtype=tf.float32) + x = ConvEncoder(rate=0.5, constraint_length=4)(inputs) model = tf.keras.Model(inputs=inputs, outputs=x) + xterm = ConvEncoder(rate=1/3, constraint_length=3, terminate=True)(inputs) + modelterm = tf.keras.Model(inputs=inputs, outputs=xterm) + b = source([bs, k]) model(b) + modelterm(b) + # call twice to see that bs can change b2 = source([bs+1, k]) model(b2) + modelterm(b2) model.summary() + modelterm.summary() source = BinarySource() - enc = ConvEncoder(rate=0.5, constraint_length=8) + enc = ConvEncoder(rate=0.5, constraint_length=6) u = source([1, 32]) x = enc(u) - print(x.shape) + self.assertTrue(x.shape == [1,64]) + u = source([2, 30]) x = enc(u) - print(x.shape) + self.assertTrue(x.shape == [2,60]) def test_multi_dimensional(self): """Test against arbitrary shapes """ k = 120 - n = 240 # rate must be 1/2 or 1/3 + rate = 1/2 + n = int(k/rate) + mu = 4 source = BinarySource() - enc = ConvEncoder(rate=k/n, constraint_length=5) + for enc in ( + ConvEncoder(rate=rate, constraint_length=mu+1), + ConvEncoder(rate=rate, constraint_length=mu+1, terminate=True)): - b = source([100, k]) - b_res = tf.reshape(b, [4, 5, 5, k]) + if enc.terminate: + n += int(mu/rate) - # encode 2D Tensor - c = enc(b).numpy() - # encode 4D Tensor - c_res = enc(b_res).numpy() + b = source([100, k]) + b_res = tf.reshape(b, [4, 5, 5, k]) - # test that shape was preserved - self.assertTrue(c_res.shape[:-1]==b_res.shape[:-1]) + # encode 2D Tensor + c = enc(b).numpy() + # encode 4D Tensor + c_res = enc(b_res).numpy() + # test that shape was preserved + self.assertTrue(c_res.shape[:-1]==b_res.shape[:-1]) - # and reshape to 2D shape - c_res = tf.reshape(c_res, [100,n]) - # both version should yield same result - self.assertTrue(np.array_equal(c, c_res)) + # and reshape to 2D shape + c_res = tf.reshape(c_res, [100, n]) + # both version should yield same result + self.assertTrue(np.array_equal(c, c_res)) def test_ref_implementation(self): r"""Test against pre-encoded codewords from reference implementation. @@ -199,19 +232,21 @@ def test_batch(self): """Test that all samples in batch yield same output (for same input). """ bs = 100 - k = 120 + k = 117 source = BinarySource() - enc = ConvEncoder(rate=0.5, constraint_length=7) + for enc in ( + ConvEncoder(rate=0.5, constraint_length=8), + ConvEncoder(rate=0.5, constraint_length=7, terminate=True)): - b = source([1, 15, k]) - b_rep = tf.tile(b, [bs, 1, 1]) + b = source([1, 15, k]) + b_rep = tf.tile(b, [bs, 1, 1]) - # and run tf version (to be tested) - c = enc(b_rep).numpy() + # and run tf version (to be tested) + c = enc(b_rep).numpy() - for i in range(bs): - self.assertTrue(np.array_equal(c[0,:,:], c[i,:,:])) + for i in range(bs): + self.assertTrue(np.array_equal(c[0,:,:], c[i,:,:])) def test_dtypes_flexible(self): """Test that encoder supports variable dtypes and @@ -227,6 +262,7 @@ def test_dtypes_flexible(self): enc_ref = ConvEncoder(rate=0.5, constraint_length=7, + rsc=True, output_dtype=tf.float32) u = source([bs, k]) @@ -235,6 +271,7 @@ def test_dtypes_flexible(self): for dt in dt_supported: enc = ConvEncoder(rate=0.5, constraint_length=7, + rsc=True, output_dtype=dt) u_dt = tf.cast(u, dt) c = enc(u_dt) @@ -258,21 +295,23 @@ def run_graph_xla(u): k = 100 source = BinarySource() - enc = ConvEncoder(rate=0.5, constraint_length=7) + for enc in ( + ConvEncoder(rate=0.5, constraint_length=7), + ConvEncoder(rate=0.5, constraint_length=4, terminate=True)): - # test that for arbitrary input only 0,1 values are outputed - u = source([bs, k]) - x = run_graph(u).numpy() + # test that for arbitrary input only 0,1 values are outputed + u = source([bs, k]) + x = run_graph(u).numpy() - # execute the graph twice - x = run_graph(u).numpy() + # execute the graph twice + x = run_graph(u).numpy() - # and change batch_size - u = source([bs+1, k]) - x = run_graph(u).numpy() + # and change batch_size + u = source([bs+1, k]) + x = run_graph(u).numpy() - #check XLA - x = run_graph_xla(u).numpy() - u = source([bs, k]) - x = run_graph_xla(u).numpy() + #check XLA + x = run_graph_xla(u).numpy() + u = source([bs, k]) + x = run_graph_xla(u).numpy() diff --git a/test/unit/fec/test_crc.py b/test/unit/fec/test_crc.py index 351072be..60868be7 100644 --- a/test/unit/fec/test_crc.py +++ b/test/unit/fec/test_crc.py @@ -214,6 +214,10 @@ def test_valid_encoding(self): self.assertTrue(np.array_equal(x_crc, x_ref_np)) + # test properties k,n + self.assertTrue(crc_enc.k==u.shape[-1]) + self.assertTrue(crc_enc.n==x.shape[-1]) + def test_keras(self): """Test that Keras model can be compiled (=supports dynamic shapes).""" diff --git a/test/unit/fec/test_fec_utils.py b/test/unit/fec/test_fec_utils.py index 0761777c..a2de00fb 100644 --- a/test/unit/fec/test_fec_utils.py +++ b/test/unit/fec/test_fec_utils.py @@ -23,7 +23,7 @@ tf.config.experimental.set_memory_growth(gpus[gpu_num], True) except RuntimeError as e: print(e) -from sionna.fec.utils import bin2int_tf, j_fun, j_fun_inv, j_fun_tf, j_fun_inv_tf, GaussianPriorSource, llr2mi, bin2int, int2bin, int2bin_tf, alist2mat, load_alist, gm2pcm, pcm2gm, verify_gm_pcm, make_systematic, load_parity_check_examples, LinearEncoder, generate_reg_ldpc, generate_prng_seq +from sionna.fec.utils import bin2int_tf, j_fun, j_fun_inv, j_fun_tf, j_fun_inv_tf, GaussianPriorSource, llr2mi, bin2int, int2bin, int2bin_tf, alist2mat, load_alist, gm2pcm, pcm2gm, verify_gm_pcm, make_systematic, load_parity_check_examples, LinearEncoder, generate_reg_ldpc, generate_prng_seq, int_mod_2 from sionna.utils import log2, log10, BinarySource from sionna.fec.polar.utils import generate_dense_polar, generate_5g_ranking from sionna.fec.polar import PolarEncoder @@ -497,179 +497,23 @@ def test_gen_rand_seq(self): s = generate_prng_seq(l, n_rnti, n_id, c_init+1) self.assertFalse(np.array_equal(s, s_ref)) + def test_mod2(self): + """Test modulo 2 operation.""" -class TestGenericLinearEncoder(unittest.TestCase): - """Test Generic Linear Encoder.""" + s = [10, 20, 30] - def test_dim_mismatch(self): - """Test against inconsistent inputs. """ - id = 2 - pcm, k, _, _ = load_parity_check_examples(id) - bs = 20 - enc = LinearEncoder(pcm, is_pcm=True) + # int inputs + x = tf.random.uniform(s, minval=-1000, maxval=1000, dtype=tf.int32) - # test for non-invalid input shape - with self.assertRaises(BaseException): - x = enc(tf.zeros([bs, k+1])) + y = int_mod_2(x) + y_ref = tf.math.mod(tf.cast(x, tf.float32), 2.) + self.assertTrue(np.array_equal(y.numpy(), y_ref.numpy())) - # test for non-binary matrix - with self.assertRaises(BaseException): - pcm[0,0]=2 - enc = LinearEncoder(pcm) # we interpret the pcm as gm for this test - - # test for non-binary matrix - with self.assertRaises(BaseException): - pcm[0,0]=2 - enc = LinearEncoder(pcm, is_pcm=True) - - def test_tf_fun(self): - """Test that tf.function works as expected and XLA is supported.""" - - @tf.function - def run_graph(u): - c = enc(u) - return c - - @tf.function(jit_compile=True) - def run_graph_xla(u): - c = enc(u) - return c - - id = 2 - pcm, k, _, _ = load_parity_check_examples(id) - bs = 20 - enc = LinearEncoder(pcm, is_pcm=True) - source = BinarySource() - - u = source([bs,k]) - run_graph(u) - run_graph_xla(u) - - def test_dtypes_flexible(self): - """Test that encoder supports variable dtypes and - yields same result.""" - - dt_supported = (tf.float16, tf.float32, tf.float64, tf.int32, tf.int64) - - id = 2 - pcm, k, _, _ = load_parity_check_examples(id) - bs = 20 - enc_ref = LinearEncoder(pcm, is_pcm=True, dtype=tf.float32) - source = BinarySource() - - u = source([bs, k]) - c_ref = enc_ref(u) - - for dt in dt_supported: - enc = LinearEncoder(pcm, is_pcm=True, dtype=dt) - u_dt = tf.cast(u, dt) - c = enc(u_dt) - - c_32 = tf.cast(c, tf.float32) - - self.assertTrue(np.array_equal(c_ref.numpy(), c_32.numpy())) - - def test_multi_dimensional(self): - """Test against arbitrary input shapes. - - The encoder should only operate on axis=-1. - """ - id = 3 - pcm, k, n, _ = load_parity_check_examples(id) - shapes =[[10, 20, 30, k], [1, 40, k], [10, 2, 3, 4, 3, k]] - enc = LinearEncoder(pcm, is_pcm=True) - source = BinarySource() - - for s in shapes: - u = source(s) - u_ref = tf.reshape(u, [-1, k]) - - c = enc(u) # encode with shape s - c_ref = enc(u_ref) # encode as 2-D array - s[-1] = n - c_ref = tf.reshape(c_ref, s) - self.assertTrue(np.array_equal(c.numpy(), c_ref.numpy())) - - # and verify that wrong last dimension raises an error - with self.assertRaises(tf.errors.InvalidArgumentError): - s = [10, 2, k-1] - u = source(s) - x = enc(u) - - def test_against_baseline(self): - """Test that PolarEncoder leads to same result. - """ - bs = 1000 - k = 57 - n = 128 - - # generate polar frozen positions - f,_ = generate_5g_ranking(k, n) - - enc_ref = PolarEncoder(f, n) # reference encoder - - # get polar encoding matrix - pcm, gm = generate_dense_polar(f, n, verbose=False) - enc = LinearEncoder(gm) - - # draw random info bits - source = BinarySource() - u = source([bs, k]) - - # encode u with both encoders - c = enc(u) - c_ref = enc_ref(u) - - # and compare results - self.assertTrue(np.array_equal(c.numpy(), c_ref.numpy())) - - def test_keras(self): - """Test that Keras model can be compiled (supports dynamic shapes).""" - bs = 10 - id = 2 - pcm, k, _, _ = load_parity_check_examples(id) - - source = BinarySource() - - inputs = tf.keras.Input(shape=(k), dtype=tf.float32) - x = LinearEncoder(pcm, is_pcm=True)(inputs) - model = tf.keras.Model(inputs=inputs, outputs=x) - - b = source([bs,k]) - model(b) - # call twice to see that bs can change - b2 = source([bs+1,k]) - model(b2) - model.summary() - - def test_random_matrices(self): - """Test against random parity-check matrices.""" - - n_trials = 100 # test against multiple random pcm realizations - bs = 100 - k = 89 - n = 123 - source = BinarySource() - - for _ in range(n_trials): - # sample a random matrix - pcm = np.random.uniform(low=0, high=2, size=(n-k, n)).astype(int) - - # catch internal errors due to non-full rank of pcm (randomly - # sampled!) - # in this test we only test that if the encoder initalization - # succeeds and the resulting encoder object produces valid codewords - try: - enc = LinearEncoder(pcm, is_pcm=True) - except: - pass # ignore this pcm realization - - u = source([bs, k]) - c = enc(u) - # verify that all codewords fullfil all parity-checks - c = tf.expand_dims(c, axis=2) - pcm = tf.expand_dims(tf.cast(pcm, tf.float32),axis=0) - s = tf.matmul(pcm,c).numpy() - s = np.mod(s, 2) - self.assertTrue(np.sum(np.abs(s))==0) + # float inputs + x = tf.random.uniform(s, minval=-1000, maxval=1000, dtype=tf.float32) + y = int_mod_2(x) + # model implicit cast + x_f = tf.sign(x) * tf.math.floor(tf.abs(x)) + y_ref = tf.math.mod(tf.math.ceil(x_f), 2.) + self.assertTrue(np.array_equal(y.numpy(), y_ref.numpy())) diff --git a/test/unit/fec/test_ldpc_encoding.py b/test/unit/fec/test_ldpc_encoding.py index 4f30c276..1ff46840 100644 --- a/test/unit/fec/test_ldpc_encoding.py +++ b/test/unit/fec/test_ldpc_encoding.py @@ -23,103 +23,9 @@ tf.config.experimental.set_memory_growth(gpus[gpu_num], True) except RuntimeError as e: print(e) -from sionna.fec.ldpc.encoding import LDPC5GEncoder, AllZeroEncoder +from sionna.fec.ldpc.encoding import LDPC5GEncoder from sionna.utils import BinarySource -class TestAllZeroEncoder(unittest.TestCase): - """Testcases for the AllZeroEncoder.""" - - def test_invalid_inputs(self): - """Test against invalid values of n and k.""" - - param_invalid = [[-1, 10],[10, -3],["a", 10],[3, "10"],[10, 9]] # (k,n) - for p in param_invalid: - with self.assertRaises(AssertionError): - AllZeroEncoder(p[0], p[1]) - - # (k,n) - param_valid = [[1, 10],[10, 30],[1000, 1566],[3, 1013],[10, 10],[0, 1]] - for p in param_valid: - AllZeroEncoder(p[0], p[1]) - - def test_output_dim(self): - """Test that output dims are correct (=n) and output is all-zero - codeword.""" - - bs = 10 - # (k,n) - param_valid = [[1, 10],[10,30],[100, 1566],[3, 1013], [10,10], [1,2]] - for p in param_valid: - enc = AllZeroEncoder(p[0], p[1]) - u = tf.zeros([bs, p[0]]) - c = enc(u).numpy() - self.assertTrue(c.shape[-1]==p[1]) - c_hat = np.zeros([bs, p[1]]) - self.assertTrue(np.array_equal(c, c_hat)) - - def test_multi_dimensional(self): - """Test against arbitrary shapes.""" - - k = 100 - n = 200 - shapes =[[10, 20, 30, k], [1, 40, k],[10, 2, 3, 4, 3, k]] - enc = AllZeroEncoder(k, n) - - for s in shapes: - source = BinarySource() - u = source(s) - u_ref = tf.reshape(u, [-1, k]) - - c = enc(u) - c_ref = enc(u_ref) - s[-1] = n - c_ref = tf.reshape(c_ref, s) - # Remark: output is allzero in both cases - self.assertTrue(np.array_equal(c.numpy(), c_ref.numpy())) - - def test_keras(self): - """Test that Keras model can be compiled (supports dynamic shapes).""" - - bs = 10 - k = 100 - n = 200 - source = BinarySource() - - inputs = tf.keras.Input(shape=(k), dtype=tf.float32) - x = AllZeroEncoder(k, n)(inputs) - model = tf.keras.Model(inputs=inputs, outputs=x) - - b = source([bs, k]) - model(b) - # call twice to see that bs can change - b2 = source([bs+1, k]) - model(b2) - model.summary() - - def test_tf_fun(self): - """Test that tf.function works as expected and XLA is supported""" - - @tf.function - def run_graph(u): - c = enc(u) - return c - - @tf.function(jit_compile=True) - def run_graph_xla(u): - c = enc(u) - return c - - k = 100 - n = 200 - bs = 10 - enc = AllZeroEncoder(k, n) - source = BinarySource() - - u = source([bs,k]) - run_graph(u) - run_graph_xla(u) - - class TestLDPC5GEncoder(unittest.TestCase): """Testcases for the LDPC5GEncoder.""" diff --git a/test/unit/fec/test_linear_decoding.py b/test/unit/fec/test_linear_decoding.py new file mode 100644 index 00000000..fa4764ac --- /dev/null +++ b/test/unit/fec/test_linear_decoding.py @@ -0,0 +1,275 @@ +# +# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +try: + import sionna +except ImportError as e: + import sys + sys.path.append("../") +from numpy.lib.npyio import load + +import unittest +import scipy as sp +import numpy as np +import tensorflow as tf +gpus = tf.config.list_physical_devices('GPU') +print('Number of GPUs available :', len(gpus)) +if gpus: + gpu_num = 0 # Number of the GPU to be used + try: + tf.config.set_visible_devices(gpus[gpu_num], 'GPU') + print('Only GPU number', gpu_num, 'used.') + tf.config.experimental.set_memory_growth(gpus[gpu_num], True) + except RuntimeError as e: + print(e) + +from sionna.fec.utils import GaussianPriorSource, LinearEncoder, load_parity_check_examples, pcm2gm +from sionna.fec.linear import OSDecoder +from sionna.utils import BinarySource, ebnodb2no, sim_ber +from sionna.channel.awgn import AWGN +from sionna.mapping import Mapper, Demapper + +class System_Model(tf.keras.Model): + """System model for channel coding BER simulations. + """ + def __init__(self, + encoder, + decoder): + + super().__init__() + + self.source = BinarySource() + self.channel = AWGN() + self.mapper = Mapper("pam", 1) + self.demapper = Demapper("app", "pam", 1) + + self.decoder = decoder + self.encoder = encoder + self.coderate = encoder.k/encoder.n + + @tf.function(jit_compile=True) + def call(self, batch_size, ebno_db): + + no = ebnodb2no(ebno_db, coderate=self.coderate, num_bits_per_symbol=1) + + b = self.source([batch_size, self.encoder.k]) + c = self.encoder(b) + x = self.mapper(c) + y = self.channel([x, no]) + llr_ch = self.demapper([y, no]) + c_hat = self.decoder(llr_ch) + return c, c_hat + +class TestOSD(unittest.TestCase): + """"Unittests for the OSD Algorithm.""" + + def test_numerical_stability(self): + """test numerical stability of the decoder for large LLRs """ + + bs = 100 + pcm, k, _, _ = load_parity_check_examples(1) + enc = LinearEncoder(pcm, is_pcm=True) + dec = OSDecoder(pcm, is_pcm=True) + source = BinarySource() + + u = source((bs, k)) + c = enc(u) + + # very large LLRs (decoder clips internally at 1000) + llr_ch = 1000. * (2*c-1) + c_hat = dec(llr_ch) + self.assertTrue(np.array_equal(c_hat.numpy(), c.numpy())) + + # very small LLRs (but still correct) + llr_ch = 0.0001 * (2*c-1) + c_hat = dec(llr_ch) + self.assertTrue(np.array_equal(c_hat.numpy(), c.numpy())) + + def test_error_patterns(self): + """test that _num_error_patterns() returns correct values.""" + + ns = [10, 45, 100, 250] # test for different lengths + ts = [1, 2, 3, 4, 5] # test for different orders + + # init dummy decoder + pcm, _, _, _ = load_parity_check_examples(0) + dec = OSDecoder(pcm, is_pcm=True) + + for n in ns: + for t in ts: + + # skip large values + if n>50 and t>3: + break + + # compute number of error patterns + num_eps = dec._num_error_patterns(n, t) + # ref from scipy + num_eps_ref= sp.special.comb(n, t, exact=True, repetition=False) + + # numbers must be equal + self.assertTrue(num_eps==num_eps_ref) + + # Number of generated error patterns must also equal + ep = dec._gen_error_patterns(n, t) + num_com = dec._num_error_patterns(n, t) + self.assertTrue(num_com==len(ep)), \ + "Number of error patterns does not match." + + def test_dtype(self): + """Test support for variable dtypes.""" + + pcm, _, n, _ = load_parity_check_examples(1, verbose=True) + gm = pcm2gm(pcm) + + # only floating point is currently supported + dt = [tf.float16, tf.float32, tf.float64] + shape = [100, n] + source = GaussianPriorSource() + + for d_in in dt: + for d_out in dt: + dec = OSDecoder(gm, dtype=d_out) + # variable input dtype + llr_ch = tf.cast(source((shape, 0.1)), d_in) + c = dec(llr_ch) + # output dtype must be as specified + self.assertTrue(c.dtype==d_out) + + def test_input_consistency(self): + """Test against inconsistent inputs.""" + id = 2 + pcm, k, n, _ = load_parity_check_examples(id) + bs = 20 + dec = OSDecoder(pcm, is_pcm=True) + + dec(tf.zeros([bs, n])) + + # batch dimension is flexible + dec(tf.zeros([bs+1, n])) + + # test for non-invalid input shape + with self.assertRaises(BaseException): + x = dec(tf.zeros([bs, n+1])) + + # test for non-binary matrix + with self.assertRaises(BaseException): + pcm[1,2] = 2 + dec = OSDecoder(pcm) # we interpret the pcm as gm for this test + + # test for non-binary matrix + with self.assertRaises(BaseException): + pcm[3,27] = 2 + dec = OSDecoder(pcm, is_pcm=True) + + def test_tf_fun(self): + """Test that graph and XLA mode are supported.""" + + @tf.function + def run_graph(u): + c = dec(u) + return c + + @tf.function(jit_compile=True) + def run_graph_xla(u): + c = dec(u) + return c + + pcm, _, n, _ = load_parity_check_examples(2) + bs = 20 + dec = OSDecoder(pcm, is_pcm=True) + source = GaussianPriorSource() + + u = source(([bs, n], 0.1)) + run_graph(u) + run_graph_xla(u) + + def test_multi_dimensional(self): + """Test against arbitrary input shapes. + + The decoder should only operate on axis=-1. + """ + id = 3 + pcm, _, n, _ = load_parity_check_examples(id) + # test different shapes + shapes =[[10, 20, 30, n], [1, 40, n], [10, 2, 3, 4, 3, n]] + dec = OSDecoder(pcm, is_pcm=True, t=2) + source = GaussianPriorSource() + + for s in shapes: + llr = source((s, 0.2)) + llr_ref = tf.reshape(llr, [-1, n]) + + c = dec(llr) # encode with shape s + c_ref = dec(llr_ref) # encode as 2-D array + s[-1] = n + c_ref = tf.reshape(c_ref, s) + self.assertTrue(np.array_equal(c.numpy(), c_ref.numpy())) + + def test_keras(self): + """Test that Keras model can be compiled (supports dynamic shapes).""" + bs = 10 + id = 2 + pcm, k, n, _ = load_parity_check_examples(id) + + source = GaussianPriorSource() + + # define keras model + inputs = tf.keras.Input(shape=(n), dtype=tf.float32) + dec = OSDecoder(pcm, is_pcm=True)(inputs) + model = tf.keras.Model(inputs=inputs, outputs=dec) + + b = source(([bs, n], 0.1)) + model(b) + # call twice to see that batch_size can change + b2 = source(([bs+1,n], 0.1)) + model(b2) + model.summary() + + def test_reference(self): + """Test against reference implementations. + + We test against ML results for the (7,4) Hamming and + the (63,45) BCH code. + """ + + ########### (7,4)) Hamming code ########### + snrs_ref = np.linspace(0, 5, 6) + blers_ref = np.array([1.832e-01, 1.253e-01, 7.047e-02, 2.899e-02, 1.252e-02, 4.371e-03]) + + id = 0 # load code + pcm, k, n, coderate = load_parity_check_examples(id) + encoder = LinearEncoder(pcm, is_pcm=True) + decoder = OSDecoder(encoder=encoder, t=2) + + model = System_Model(encoder, decoder) + + _, bler = sim_ber(model, + ebno_dbs=snrs_ref, + batch_size=1000, + max_mc_iter=100, + num_target_block_errors=10000) + # we allow 20% tolerance to ML; + self.assertTrue(np.all(np.isclose(bler.numpy(), blers_ref, rtol=0.2))) + + ########### (63,45) BCH code ########### + snrs_ref = np.array([0, 1.5, 3., 4]) + blers_ref = np.array([6.329e-01,2.445e-01, 2.595e-02, 2.134e-03]) + + id = 1 # load code + pcm, k, n, coderate = load_parity_check_examples(id) + encoder = LinearEncoder(pcm, is_pcm=True) + decoder = OSDecoder(encoder=encoder, t=4) + + model = System_Model(encoder, decoder) + + _, bler = sim_ber(model, + ebno_dbs=snrs_ref, + batch_size=1000, + max_mc_iter=100, + num_target_block_errors=1000) + # we allow 20% tolerance to ML; + self.assertTrue(np.all(np.isclose(bler.numpy(), blers_ref, rtol=0.2))) + + diff --git a/test/unit/fec/test_linear_encoding.py b/test/unit/fec/test_linear_encoding.py new file mode 100644 index 00000000..f0582931 --- /dev/null +++ b/test/unit/fec/test_linear_encoding.py @@ -0,0 +1,297 @@ +# +# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +try: + import sionna +except ImportError as e: + import sys + sys.path.append("../") +from numpy.lib.npyio import load + +import unittest +import numpy as np +import tensorflow as tf +gpus = tf.config.list_physical_devices('GPU') +print('Number of GPUs available :', len(gpus)) +if gpus: + gpu_num = 0 # Number of the GPU to be used + try: + tf.config.set_visible_devices(gpus[gpu_num], 'GPU') + print('Only GPU number', gpu_num, 'used.') + tf.config.experimental.set_memory_growth(gpus[gpu_num], True) + except RuntimeError as e: + print(e) +from sionna.fec.utils import load_parity_check_examples +from sionna.fec.linear import LinearEncoder, AllZeroEncoder +from sionna.utils import BinarySource +from sionna.fec.polar.utils import generate_dense_polar, generate_5g_ranking +from sionna.fec.polar import PolarEncoder + +class TestGenericLinearEncoder(unittest.TestCase): + """Test Generic Linear Encoder.""" + + def test_dim_mismatch(self): + """Test against inconsistent inputs. """ + id = 2 + pcm, k, _, _ = load_parity_check_examples(id) + bs = 20 + enc = LinearEncoder(pcm, is_pcm=True) + + # test for non-invalid input shape + with self.assertRaises(BaseException): + x = enc(tf.zeros([bs, k+1])) + + # test for non-binary matrix + with self.assertRaises(BaseException): + pcm[0,0]=2 + enc = LinearEncoder(pcm) # we interpret the pcm as gm for this test + + # test for non-binary matrix + with self.assertRaises(BaseException): + pcm[0,0]=2 + enc = LinearEncoder(pcm, is_pcm=True) + + def test_tf_fun(self): + """Test that tf.function works as expected and XLA is supported.""" + + @tf.function + def run_graph(u): + c = enc(u) + return c + + @tf.function(jit_compile=True) + def run_graph_xla(u): + c = enc(u) + return c + + id = 2 + pcm, k, _, _ = load_parity_check_examples(id) + bs = 20 + enc = LinearEncoder(pcm, is_pcm=True) + source = BinarySource() + + u = source([bs,k]) + run_graph(u) + run_graph_xla(u) + + def test_dtypes_flexible(self): + """Test that encoder supports variable dtypes and + yields same result.""" + + dt_supported = (tf.float16, tf.float32, tf.float64, tf.int32, tf.int64) + + id = 2 + pcm, k, _, _ = load_parity_check_examples(id) + bs = 20 + enc_ref = LinearEncoder(pcm, is_pcm=True, dtype=tf.float32) + source = BinarySource() + + u = source([bs, k]) + c_ref = enc_ref(u) + + for dt in dt_supported: + enc = LinearEncoder(pcm, is_pcm=True, dtype=dt) + u_dt = tf.cast(u, dt) + c = enc(u_dt) + + c_32 = tf.cast(c, tf.float32) + + self.assertTrue(np.array_equal(c_ref.numpy(), c_32.numpy())) + + def test_multi_dimensional(self): + """Test against arbitrary input shapes. + + The encoder should only operate on axis=-1. + """ + id = 3 + pcm, k, n, _ = load_parity_check_examples(id) + shapes =[[10, 20, 30, k], [1, 40, k], [10, 2, 3, 4, 3, k]] + enc = LinearEncoder(pcm, is_pcm=True) + source = BinarySource() + + for s in shapes: + u = source(s) + u_ref = tf.reshape(u, [-1, k]) + + c = enc(u) # encode with shape s + c_ref = enc(u_ref) # encode as 2-D array + s[-1] = n + c_ref = tf.reshape(c_ref, s) + self.assertTrue(np.array_equal(c.numpy(), c_ref.numpy())) + + # and verify that wrong last dimension raises an error + with self.assertRaises(tf.errors.InvalidArgumentError): + s = [10, 2, k-1] + u = source(s) + x = enc(u) + + def test_against_baseline(self): + """Test that PolarEncoder leads to same result. + """ + bs = 1000 + k = 57 + n = 128 + + # generate polar frozen positions + f,_ = generate_5g_ranking(k, n) + + enc_ref = PolarEncoder(f, n) # reference encoder + + # get polar encoding matrix + pcm, gm = generate_dense_polar(f, n, verbose=False) + enc = LinearEncoder(gm) + + # draw random info bits + source = BinarySource() + u = source([bs, k]) + + # encode u with both encoders + c = enc(u) + c_ref = enc_ref(u) + + # and compare results + self.assertTrue(np.array_equal(c.numpy(), c_ref.numpy())) + + def test_keras(self): + """Test that Keras model can be compiled (supports dynamic shapes).""" + bs = 10 + id = 2 + pcm, k, _, _ = load_parity_check_examples(id) + + source = BinarySource() + + inputs = tf.keras.Input(shape=(k), dtype=tf.float32) + x = LinearEncoder(pcm, is_pcm=True)(inputs) + model = tf.keras.Model(inputs=inputs, outputs=x) + + b = source([bs,k]) + model(b) + # call twice to see that bs can change + b2 = source([bs+1,k]) + model(b2) + model.summary() + + def test_random_matrices(self): + """Test against random parity-check matrices.""" + + n_trials = 100 # test against multiple random pcm realizations + bs = 100 + k = 89 + n = 123 + source = BinarySource() + + for _ in range(n_trials): + # sample a random matrix + pcm = np.random.uniform(low=0, high=2, size=(n-k, n)).astype(int) + + # catch internal errors due to non-full rank of pcm (randomly + # sampled!) + # in this test we only test that if the encoder initalization + # succeeds and the resulting encoder object produces valid codewords + try: + enc = LinearEncoder(pcm, is_pcm=True) + except: + pass # ignore this pcm realization + + u = source([bs, k]) + c = enc(u) + # verify that all codewords fullfil all parity-checks + c = tf.expand_dims(c, axis=2) + pcm = tf.expand_dims(tf.cast(pcm, tf.float32),axis=0) + s = tf.matmul(pcm,c).numpy() + s = np.mod(s, 2) + self.assertTrue(np.sum(np.abs(s))==0) + +class TestAllZeroEncoder(unittest.TestCase): + """Testcases for the AllZeroEncoder.""" + + def test_invalid_inputs(self): + """Test against invalid values of n and k.""" + + param_invalid = [[-1, 10],[10, -3],["a", 10],[3, "10"],[10, 9]] # (k,n) + for p in param_invalid: + with self.assertRaises(AssertionError): + AllZeroEncoder(p[0], p[1]) + + # (k,n) + param_valid = [[1, 10],[10, 30],[1000, 1566],[3, 1013],[10, 10],[0, 1]] + for p in param_valid: + AllZeroEncoder(p[0], p[1]) + + def test_output_dim(self): + """Test that output dims are correct (=n) and output is all-zero + codeword.""" + + bs = 10 + # (k,n) + param_valid = [[1, 10],[10,30],[100, 1566],[3, 1013], [10,10], [1,2]] + for p in param_valid: + enc = AllZeroEncoder(p[0], p[1]) + u = tf.zeros([bs, p[0]]) + c = enc(u).numpy() + self.assertTrue(c.shape[-1]==p[1]) + c_hat = np.zeros([bs, p[1]]) + self.assertTrue(np.array_equal(c, c_hat)) + + def test_multi_dimensional(self): + """Test against arbitrary shapes.""" + + k = 100 + n = 200 + shapes =[[10, 20, 30, k], [1, 40, k],[10, 2, 3, 4, 3, k]] + enc = AllZeroEncoder(k, n) + + for s in shapes: + source = BinarySource() + u = source(s) + u_ref = tf.reshape(u, [-1, k]) + + c = enc(u) + c_ref = enc(u_ref) + s[-1] = n + c_ref = tf.reshape(c_ref, s) + # Remark: output is allzero in both cases + self.assertTrue(np.array_equal(c.numpy(), c_ref.numpy())) + + def test_keras(self): + """Test that Keras model can be compiled (supports dynamic shapes).""" + + bs = 10 + k = 100 + n = 200 + source = BinarySource() + + inputs = tf.keras.Input(shape=(k), dtype=tf.float32) + x = AllZeroEncoder(k, n)(inputs) + model = tf.keras.Model(inputs=inputs, outputs=x) + + b = source([bs, k]) + model(b) + # call twice to see that bs can change + b2 = source([bs+1, k]) + model(b2) + model.summary() + + def test_tf_fun(self): + """Test that tf.function works as expected and XLA is supported""" + + @tf.function + def run_graph(u): + c = enc(u) + return c + + @tf.function(jit_compile=True) + def run_graph_xla(u): + c = enc(u) + return c + + k = 100 + n = 200 + bs = 10 + enc = AllZeroEncoder(k, n) + source = BinarySource() + + u = source([bs,k]) + run_graph(u) + run_graph_xla(u) diff --git a/test/unit/fec/test_turbo_decoding.py b/test/unit/fec/test_turbo_decoding.py index c39b6035..e121b82e 100644 --- a/test/unit/fec/test_turbo_decoding.py +++ b/test/unit/fec/test_turbo_decoding.py @@ -8,11 +8,11 @@ import sys sys.path.append("../") +from itertools import product import unittest import numpy as np import tensorflow as tf - gpus = tf.config.list_physical_devices('GPU') print('Number of GPUs available :', len(gpus)) if gpus: @@ -25,47 +25,10 @@ print(e) from sionna.fec.turbo import TurboEncoder, TurboDecoder from sionna.fec.utils import GaussianPriorSource -from sionna.utils import BinarySource, compute_ber, compute_bler, sim_ber, ebnodb2no +from sionna.utils import BinarySource, sim_ber, ebnodb2no from sionna.channel import AWGN from sionna.mapping import Mapper, Demapper, Constellation -class TurboExample(tf.keras.Model): - def __init__(self, - k, - gen_poly, - turbo_iter=10, - terminate=False, - rate=1/3): - super().__init__() - - self.k = k - self.rate = rate - self.binary_source = BinarySource() - self.encoder = TurboEncoder(gen_poly=gen_poly, - rate=rate, - terminate=terminate) - self.channel = AWGN() - self.decoder = TurboDecoder(self.encoder, num_iter=turbo_iter) - - def call(self, ebno, batch_size): - # Generate a batch of random bit vectors - no = tf.cast((1/self.rate) * (10 ** (-ebno / 10)),tf.float32) - msg = tf.cast(self.binary_source([batch_size, self.k]), tf.int32) - cw = self.encoder(msg) - x = 2 * cw - 1 - x_cpx = tf.complex(tf.cast(x, tf.float32), tf.zeros(x.shape)) - y_cpx = self.channel((x_cpx, no)) - y = tf.math.real(y_cpx) - llr = 4.*y/no - msghat = self.decoder(llr) - msghat = tf.cast(msghat, tf.int32) - - diff = tf.abs(msghat-msg) - blerrs_ = int(tf.math.count_nonzero(tf.reduce_sum(diff, axis=1))) - biterrs_ = int(tf.math.count_nonzero(diff)) - return blerrs_, biterrs_ - - class TestTurboDecoding(unittest.TestCase): def test_output_dim_num_stab(self): @@ -73,10 +36,10 @@ def test_output_dim_num_stab(self): codeword. Further, test numerical stability (no nan or infty as output).""" - bs = 10 + bs = 6 coderates = [1/2, 1/3] - ks = [10, 40, 400] + ks = [12, 60] source = GaussianPriorSource() @@ -85,6 +48,7 @@ def test_output_dim_num_stab(self): n = int(k/rate) dec = TurboDecoder(rate=rate, constraint_length=5, + num_iter=3, terminate=False) # --- test output dimensions --- @@ -135,37 +99,17 @@ def test_identity_(enc, dec, msg): return - bs = 10 - coderates = [1/2, 1/3] + bs = 5 k = 50 - cls = [3, 4, 5, 6] # constraint length + cl = 4 # constraint length + coderates = [1/3, 1/2] - for terminate in [False, True]: + for terminate, alg in product([True, False], ("map", "log", "maxlog")): for rate in coderates: - for cl in cls: - u = BinarySource()([bs, k]) - enc = TurboEncoder( - constraint_length=cl, - rate=rate, - terminate=terminate) - dec = TurboDecoder( - gen_poly=enc.gen_poly, - rate=rate, - terminate=terminate) - test_identity_(enc, dec, u) - - u = BinarySource()([bs, k]) - for rate in coderates: - enc = TurboEncoder(gen_poly=['101', '111'], - rate=rate, - terminate=terminate) - dec = TurboDecoder(enc) - test_identity_(enc, dec, u) - - enc = TurboEncoder(gen_poly=['1101', '1111'], - rate=rate, - terminate=terminate) - dec = TurboDecoder(enc) + u = BinarySource()([bs, k]) + enc = TurboEncoder( + constraint_length=cl, rate=rate, terminate=terminate) + dec = TurboDecoder(enc, algorithm=alg, num_iter=2) test_identity_(enc, dec, u) def test_keras(self): @@ -174,7 +118,7 @@ def test_keras(self): n = 64 source = BinarySource() inputs = tf.keras.Input(shape=(n), dtype=tf.float32) - x = TurboDecoder(rate=1/2, constraint_length=3, terminate=False)(inputs) + x = TurboDecoder(rate=1/2, constraint_length=3, terminate=False, num_iter=3)(inputs) model = tf.keras.Model(inputs=inputs, outputs=x) b = source([bs, n]) @@ -190,7 +134,7 @@ def test_multi_dimensional(self): n = 200 source = BinarySource() - dec = TurboDecoder(rate=1/2, constraint_length=3, terminate=False) + dec = TurboDecoder(rate=1/2, constraint_length=3, num_iter=2, terminate=False) b = source([30, n]) b_res = tf.reshape(b, [2, 3, 5, n]) @@ -216,7 +160,7 @@ def test_batch(self): n = 240 source = GaussianPriorSource() - dec = TurboDecoder(rate=1/2, constraint_length=3, terminate=False) + dec = TurboDecoder(rate=1/2, constraint_length=3, terminate=False, num_iter=2) b = source([[1, n], 1.]) b_rep = tf.tile(b, [bs, 1]) @@ -228,7 +172,7 @@ def test_batch(self): for i in range(bs): self.assertTrue(np.array_equal(c[0,:], c[i,:])) - def test_ref_implementation(self): + def test_ber_match(self): """Test against results from reference implementation. """ def simulation(k, num_iter, snrs): @@ -253,13 +197,13 @@ def run_graph(batch_size, ebno_db): return u, u_hat ber, _ = sim_ber(run_graph, - ebno_dbs=snrs, - max_mc_iter=20, - num_target_bit_errors=1000, - batch_size=10000, - soft_estimates=False, - early_stop=True, - forward_keyboard_interrupt=False) + ebno_dbs=snrs, + max_mc_iter=20, + num_target_bit_errors=500, + batch_size=10000, + soft_estimates=False, + early_stop=True, + forward_keyboard_interrupt=False) return ber k = 512 snrs = [0, 0.5, 1, 1.5, 2] @@ -274,10 +218,27 @@ def run_graph(batch_size, ebno_db): snrs = snrs[:-1] ber = simulation(k, num_iters, snrs) for idx in range(len(snrs)): - print(idx) self.assertTrue(np.less_equal(ber[idx], ber_ub[num_iters][idx])) self.assertTrue(np.greater_equal(ber[idx], ber_lb[num_iters][idx])) + def test_ref_implementation(self): + r"""Test against pre-decoded noisy codewords from reference + implementation. + """ + ref_path = 'codes/turbo/' + r = 1/3 + ks = [40, 112, 168] + enc = TurboEncoder(rate=1/3, terminate=True, constraint_length=4) + dec = TurboDecoder(enc, num_iter=10) + ebno = 0.0 + no = 1/(r* (10 ** (-ebno / 10))) + + for k in ks: + uhatref = np.load(ref_path + 'ref_k{}_uhat.npy'.format(k)) + yref = np.load(ref_path + 'ref_k{}_y.npy'.format(k)) + uhat = dec(-4.*yref/no).numpy() + self.assertTrue(np.array_equal(uhat, uhatref)) + def test_dtype_flexible(self): """Test that output_dtype can be flexible.""" batch_size = 40 @@ -304,6 +265,7 @@ def test_dtype_flexible(self): llr_c = tf.complex(llr, tf.zeros_like(llr)) dec = TurboDecoder(rate=1/2, constraint_length=3, + num_iter=1, output_dtype=tf.float32) with self.assertRaises(TypeError): @@ -318,7 +280,7 @@ def test_tf_fun(self): for t in [False, True]: - dec = TurboDecoder(rate=1/3, constraint_length=3, terminate=t) + dec = TurboDecoder(rate=1/3, constraint_length=3, terminate=t, num_iter=3) @tf.function def run_graph(u): @@ -346,3 +308,18 @@ def run_graph_xla(u): # and change the batch_size again u = source([bs+1, n]) x = run_graph_xla(u).numpy() + + def test_dynamic_shapes(self): + """Test for dynamic (=unknown) batch-sizes""" + + n = 1536 + enc = TurboEncoder(gen_poly=('1101', '1011'), rate=1/3, terminate=False) + dec = TurboDecoder(enc, num_iter=3) + + @tf.function(jit_compile=True) + def run_graph(batch_size): + llr_ch = tf.zeros((batch_size, n)) + u_hat = dec(llr_ch) + return u_hat + + run_graph(tf.constant(1)) diff --git a/test/unit/fec/test_turbo_encoding.py b/test/unit/fec/test_turbo_encoding.py index 429e01f2..12762ff4 100644 --- a/test/unit/fec/test_turbo_encoding.py +++ b/test/unit/fec/test_turbo_encoding.py @@ -190,7 +190,6 @@ def test_multi_dimensional(self): # both version should yield same result self.assertTrue(np.array_equal(c, c_res)) - def test_batch(self): """Test that all samples in batch yield same output (for same input). """ @@ -275,3 +274,15 @@ def run_graph_xla(u): u = source([bs, k]) x = run_graph_xla(u).numpy() + def test_ref_implementation(self): + r"""Test against pre-encoded codewords from reference implementation. + """ + ref_path = 'codes/turbo/' + ks = [40, 112, 168, 432] + enc = TurboEncoder(rate=1/3, terminate=True, constraint_length=4) + + for k in ks: + uref = np.load(ref_path + 'ref_k{}_u.npy'.format(k)) + cref = np.load(ref_path + 'ref_k{}_x.npy'.format(k)) + c = enc(uref).numpy() + self.assertTrue(np.array_equal(c, cref)) diff --git a/test/unit/mapping/test_mapping.py b/test/unit/mapping/test_mapping.py index 405b066c..64147b7c 100644 --- a/test/unit/mapping/test_mapping.py +++ b/test/unit/mapping/test_mapping.py @@ -1489,7 +1489,7 @@ def run(batch_size): l2m = SymbolLogits2Moments("qam", num_bits_per_symbol, dtype=tf.float64) @tf.function def run(batch_size): - logits = tf.random.normal([batch_size, 150, 2**num_bits_per_symbol]) + logits = tf.random.normal([batch_size, 150, 2**num_bits_per_symbol], dtype=tf.float64) return l2m(logits) m,v = run(100) @@ -1524,7 +1524,7 @@ def run(batch_size): l2m = SymbolLogits2Moments("qam", num_bits_per_symbol, dtype=tf.float64) @tf.function(jit_compile=True) def run(batch_size): - logits = tf.random.normal([batch_size, 150, 2**num_bits_per_symbol]) + logits = tf.random.normal([batch_size, 150, 2**num_bits_per_symbol], dtype=tf.float64) return l2m(logits) m,v = run(100) @@ -1535,19 +1535,19 @@ def run(batch_size): self.assertEqual(m.shape, [400, 150]) self.assertEqual(v.shape, [400, 150]) - def test_model_mode(self): - l2m = SymbolLogits2Moments("qam", 4) - logits = tf.keras.Input(shape=(150, 16), dtype=tf.float32) - m,v = l2m(logits) - model = tf.keras.Model(inputs=[logits], outputs=(m,v)) - model.compile() - - in1 = tf.random.normal([100, 150, 16]) - m,v = model([in1]) - self.assertEqual(m.shape, [100, 150]) - self.assertEqual(v.shape, [100, 150]) - - in1 = tf.random.normal([256, 150, 16]) - m,v = model([in1]) - self.assertEqual(m.shape, [256, 150]) - self.assertEqual(v.shape, [256, 150]) + # def test_model_mode(self): + # l2m = SymbolLogits2Moments("qam", 4) + # logits = tf.keras.Input(shape=(150, 16), dtype=tf.float32) + # m,v = l2m(logits) + # model = tf.keras.Model(inputs=[logits], outputs=(m,v)) + # model.compile() + + # in1 = tf.random.normal([100, 150, 16]) + # m,v = model([in1]) + # self.assertEqual(m.shape, [100, 150]) + # self.assertEqual(v.shape, [100, 150]) + + # in1 = tf.random.normal([256, 150, 16]) + # m,v = model([in1]) + # self.assertEqual(m.shape, [256, 150]) + # self.assertEqual(v.shape, [256, 150]) diff --git a/test/unit/mimo/test_ep_det.py b/test/unit/mimo/test_ep_det.py new file mode 100644 index 00000000..e642d48d --- /dev/null +++ b/test/unit/mimo/test_ep_det.py @@ -0,0 +1,207 @@ +# +# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +try: + import sionna +except ImportError as e: + import sys + sys.path.append("../") +import pytest +import unittest +import numpy as np +import tensorflow as tf +gpus = tf.config.list_physical_devices('GPU') +print('Number of GPUs available :', len(gpus)) +if gpus: + gpu_num = 0 # Number of the GPU to be used + try: + tf.config.set_visible_devices(gpus[gpu_num], 'GPU') + print('Only GPU number', gpu_num, 'used.') + tf.config.experimental.set_memory_growth(gpus[gpu_num], True) + except RuntimeError as e: + print(e) +import sionna +from sionna.mimo import EPDetector +from sionna.mapping import Constellation, Mapper +from sionna.utils import BinarySource, QAMSource, PAMSource, compute_ser, compute_ber, ebnodb2no, sim_ber +from sionna.channel import FlatFadingChannel +from sionna.fec.ldpc.encoding import LDPC5GEncoder +from sionna.fec.ldpc.decoding import LDPC5GDecoder + +class MIMODetection(tf.keras.Model): + def __init__(self, + num_tx, + num_rx_ant, + num_bits_per_symbol, + output, + coded, + dtype=tf.complex64): + super().__init__() + self._dtype = dtype + self._n = (2000//num_bits_per_symbol)*num_bits_per_symbol + self._k = 1750 + self._coderate = self._k/self._n + self._num_tx = num_tx + self._num_rx_ant = num_rx_ant + self._num_bits_per_symbol = num_bits_per_symbol + self._output = output + self._coded = coded + + self._binary_source = BinarySource() + + if self._coded: + self._encoder = LDPC5GEncoder(self._k, self._n, num_bits_per_symbol=num_bits_per_symbol, dtype=dtype.real_dtype) + self._decoder = LDPC5GDecoder(self._encoder, hard_out=False) + if self._output=="symbol": + self._hard_out = True + else: + self._hard_out = False + + self._mapper = Mapper("qam", self._num_bits_per_symbol, return_indices=True, dtype=dtype) + self._channel = FlatFadingChannel(self._num_tx, + self._num_rx_ant, + add_awgn=True, + return_channel=True, + dtype=dtype) + ep_detector = EPDetector(self._output, num_bits_per_symbol, hard_out=self._hard_out, dtype=dtype) + self._detector = ep_detector + + def call(self, batch_size, ebno_db): + + if self._coded: + b = self._binary_source([batch_size, self._num_tx, self._k]) + c = self._encoder(b) + else: + c = self._binary_source([batch_size, self._num_tx, self._n]) + + shape = tf.shape(c) + x, x_ind = self._mapper(c) + x = tf.reshape(x, [-1, self._num_tx]) + no = tf.cast(self._num_tx, tf.float32)*tf.pow(10.0, -ebno_db/10.0) + y, h = self._channel([x, no]) + s = tf.cast(no*tf.eye(self._num_rx_ant), self._dtype) + det_out = self._detector([y, h, s]) + + if self._output=="bit": + llr = tf.reshape(det_out, shape) + if self._coded: + b_hat = self._decoder(llr) + return b, b_hat + else: + return c, llr + elif self._output=="symbol": + x_hat = tf.reshape(det_out, tf.shape(x_ind)) + return x_ind, x_hat + +class TestEPDetector(unittest.TestCase): + def test_wrong_parameters(self): + with self.assertRaises(AssertionError): + "Neither constellation nor constellation_type" + EPDetector("bit", 4, dtype=tf.float32) + + with self.assertRaises(AssertionError): + "Wrong output" + EPDetector("sym", 4) + + with self.assertRaises(AssertionError): + "Wrong number of iterations" + EPDetector("sym", 4, l=0) + + with self.assertRaises(AssertionError): + "Beta out of bounds" + EPDetector("sym", 4, beta=1.1) + + def test_symbol_errors(self): + """Test that we get no symbol errors on a noise free channel""" + tf.random.set_seed(1) + sionna.Config.xla_compat = False + num_tx = 3 + num_rx_ant = 7 + num_bits_per_symbols = [2,4,6,8] + batch_size = 100 + for num_bits_per_symbol in num_bits_per_symbols: + qam_source = QAMSource(num_bits_per_symbol, return_indices=True) + channel = FlatFadingChannel(num_tx, num_rx_ant, add_awgn=False, return_channel=True) + kbest = EPDetector("symbol", num_bits_per_symbol, hard_out=True) + x, x_ind = qam_source([batch_size, num_tx]) + y, h = channel(x) + s = tf.cast(1e-4*tf.eye(num_rx_ant), tf.complex64) + x_ind_hat = kbest([y, h, s]) + self.assertEqual(0, compute_ser(x_ind, x_ind_hat)) + + def test_no_bit_errors(self): + "Test that we get no uncoded bit errors on a noise free channel" + tf.random.set_seed(1) + sionna.Config.xla_compat = False + num_tx = 3 + num_rx_ant = 7 + num_bits_per_symbols = [2,4,6,8] + batch_size = 100 + for num_bits_per_symbol in num_bits_per_symbols: + qam_source = QAMSource(num_bits_per_symbol, return_indices=True, return_bits=True) + channel = FlatFadingChannel(num_tx, num_rx_ant, add_awgn=False, return_channel=True) + kbest = EPDetector("bit", num_bits_per_symbol, hard_out=True) + x, _, b = qam_source([batch_size, num_tx]) + y, h = channel(x) + s = tf.cast(1e-4*tf.eye(num_rx_ant), tf.complex64) + b_hat = kbest([y, h, s]) + self.assertEqual(0, compute_ber(b, b_hat)) + + def test_all_execution_modes(self): + "Test that the detector work in all execution modes" + def evaluate(ep): + @tf.function() + def func(): + return ep(100, 20.0) + _, x_hat = tf.function(func)() + self.assertFalse(np.any(np.isnan(x_hat))) + _, x_hat = tf.function(func, jit_compile=False)() + self.assertFalse(np.any(np.isnan(x_hat))) + _, x_hat = tf.function(func, jit_compile=True)() + self.assertFalse(np.any(np.isnan(x_hat))) + + for dtype in [tf.complex64, tf.complex128]: + evaluate(MIMODetection(1, 1, 4, "bit", False, dtype)) + evaluate(MIMODetection(3, 3, 2, "bit", True, dtype)) + evaluate(MIMODetection(3, 6, 4, "symbol", False, dtype)) + evaluate(MIMODetection(3, 5, 4, "bit", False, dtype)) + evaluate(MIMODetection(2, 6, 4, "bit", True, dtype)) + evaluate(MIMODetection(4, 8, 4, "symbol", False, dtype)) + + def test_ser_against_references(self): + tf.random.set_seed(2) + sionna.Config.xla_compat = False + def sim(ep, snr_db): + ser, _ = sim_ber(ep, + [snr_db], + batch_size=64, + max_mc_iter=1000, + num_target_block_errors=1000, + soft_estimates=False, + graph_mode="graph") + return ser[0] + # Values taken from https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9832663 + # Fig. 8 (a) + ser = sim(MIMODetection(4, 8, 2, "symbol", False, tf.complex64), 12.0) + self.assertTrue(4e-5<=ser<=5e-5) + + # Fig. 8 (b) + ser = sim(MIMODetection(6, 8, 2, "symbol", False, tf.complex64), 13.0) + self.assertTrue(1.5e-4<=ser<=2.5e-4) + + # Fig. 8 (c) + ser = sim(MIMODetection(8, 8, 2, "symbol", False, tf.complex64), 18.0) + self.assertTrue(3e-5<=ser<=4e-5) + + # Fig. 9 (c) + ser = sim(MIMODetection(32, 32, 2, "symbol", False, tf.complex64), 13.0) + self.assertTrue(7.5e-5<=ser<=9.5e-5) + + # Fig. 10 (c) + ser = sim(MIMODetection(32, 32, 4, "symbol", False, tf.complex64), 27.0) + self.assertTrue(9e-5<=ser<=1e-4) + + # Fig. 11 (c) + ser = sim(MIMODetection(8, 8, 6, "symbol", False, tf.complex128), 40.0) + self.assertTrue(3e-4<=ser<=4e-4) diff --git a/test/unit/mimo/test_kbest_det.py b/test/unit/mimo/test_kbest_det.py new file mode 100644 index 00000000..7cdeddf0 --- /dev/null +++ b/test/unit/mimo/test_kbest_det.py @@ -0,0 +1,423 @@ +# +# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +try: + import sionna +except ImportError as e: + import sys + sys.path.append("../") +import pytest +import unittest +import numpy as np +import tensorflow as tf +gpus = tf.config.list_physical_devices('GPU') +print('Number of GPUs available :', len(gpus)) +if gpus: + gpu_num = 0 # Number of the GPU to be used + try: + tf.config.set_visible_devices(gpus[gpu_num], 'GPU') + print('Only GPU number', gpu_num, 'used.') + tf.config.experimental.set_memory_growth(gpus[gpu_num], True) + except RuntimeError as e: + print(e) +import sionna +from sionna.mimo import KBestDetector, MaximumLikelihoodDetector +from sionna.mapping import Constellation, Mapper +from sionna.utils import BinarySource, QAMSource, PAMSource, compute_ser, compute_ber, ebnodb2no, sim_ber +from sionna.channel import FlatFadingChannel +from sionna.fec.ldpc.encoding import LDPC5GEncoder +from sionna.fec.ldpc.decoding import LDPC5GDecoder + +class MIMODetectionBER(tf.keras.Model): + """Simple class to evalute (un-)coded BER of different detectors""" + def __init__(self, + num_tx, + num_rx_ant, + num_bits_per_symbol, + detector, + coded=True, + dtype=tf.complex64): + super().__init__() + self._dtype = dtype + self._n = 2000 + self._k = 1500 + self._coderate = self._k/self._n + self._num_tx = num_tx + self._num_rx_ant = num_rx_ant + self._num_bits_per_symbol = num_bits_per_symbol + self._binary_source = BinarySource() + self._encoder = LDPC5GEncoder(self._k, self._n, dtype=dtype.real_dtype) + self._mapper = Mapper("qam", self._num_bits_per_symbol, dtype=dtype) + self._channel = FlatFadingChannel(self._num_tx, + self._num_rx_ant, + add_awgn=True, + return_channel=True, + dtype=dtype) + if detector=="kbest": + k = (2**num_bits_per_symbol)**num_tx + kbest_detector = KBestDetector("bit", num_tx, k,"qam", num_bits_per_symbol, use_real_rep=False, hard_out=not coded, dtype=dtype) + self._detector = kbest_detector + elif detector=="ml": + ml_detector = MaximumLikelihoodDetector("bit", "maxlog", num_tx, "qam", num_bits_per_symbol, hard_out=not coded, dtype=dtype) + self._detector = ml_detector + self._decoder = LDPC5GDecoder(self._encoder, hard_out=True) + self._coded = coded + + @tf.function(jit_compile=True) + def call(self, batch_size, ebno_db): + b = self._binary_source([batch_size, self._num_tx, self._k]) + c = self._encoder(b) + shape = tf.shape(c) + x = self._mapper(c) + x = tf.reshape(x, [-1, self._num_tx]) + no = tf.cast(self._num_tx, tf.float32)*tf.pow(10.0, -ebno_db/10.0) + y, h = self._channel([x, no]) + s = tf.cast(no*tf.eye(self._num_rx_ant), self._dtype) + llr = self._detector([y, h, s]) + llr = tf.reshape(llr, shape) + if not self._coded: + return c, llr + else: + b_hat = self._decoder(llr) + return b, b_hat + + + + + + + +class TestKBestDetector(unittest.TestCase): + + def test_wrong_parameters(self): + with self.assertRaises(AssertionError): + "Neither constellation nor constellation_type" + KBestDetector("bit", 4, 16) + + with self.assertRaises(AssertionError): + "Missing num_bits_per_symbol" + KBestDetector("bit", 4, 16, + constellation_type = "qam") + + with self.assertRaises(AssertionError): + "Missing constellation_type" + KBestDetector("bit", 4, 16, + num_bits_per_symbol=4) + + with self.assertRaises(AssertionError): + "Overspecified constellation" + KBestDetector("bit", 4, 16, + num_bits_per_symbol=4, + constellation=Constellation("pam", 4)) + + with self.assertRaises(AssertionError): + "Overspecified constellation" + KBestDetector("bit", 4, 16, + constellation_type="qam", + constellation=Constellation("pam", 4)) + + with self.assertRaises(AssertionError): + "Overspecified constellation" + KBestDetector("bit", 4, 16, + constellation_type="qam", + num_bits_per_symbol = 4, + constellation=Constellation("pam", 4)) + + with self.assertRaises(AssertionError): + "Wrong constellation dtype" + KBestDetector("bit", 4, 16, + constellation=Constellation("pam", 4), + dtype=tf.complex128) + + with self.assertRaises(AssertionError): + "Wrong constellation dtype" + KBestDetector("bit", 4, 16, + constellation=Constellation("pam", 4, dtype=tf.complex128)) + + def test_init_complex_rep(self): + num_bits_per_symbol = 4 + num_tx = 4 + k = 16 + constellation_type = "qam" + + # Test correct initialization for QAM + detector = KBestDetector("bit", num_tx, k, + constellation_type="qam", + num_bits_per_symbol=num_bits_per_symbol) + self.assertEqual(detector._num_streams, num_tx) + self.assertEqual(detector._num_symbols, 2**num_bits_per_symbol) + self.assertEqual(k, detector._k) + self.assertTrue(np.allclose(np.var(detector._constellation), 1.0)) + + # Test correct initialization for PAM + detector = KBestDetector("bit", num_tx, k, + constellation=Constellation("pam", num_bits_per_symbol)) + self.assertEqual(detector._num_streams, num_tx) + self.assertEqual(detector._num_symbols, 2**num_bits_per_symbol) + self.assertEqual(k, detector._k) + self.assertTrue(np.allclose(np.var(detector._constellation), 1.0)) + + # Test that k was limited maximum possible length + num_symbols = 2**num_bits_per_symbol + k_max = num_symbols**num_tx + with self.assertWarns(Warning): + detector = KBestDetector("bit", num_tx, 2*k_max, + constellation_type="qam", + num_bits_per_symbol = 4) + self.assertEqual(detector._k, k_max) + + def test_wrong_constellation_for_real_rep(self): + """Test that PAM cannot be used with use_real_rep""" + output = "bit" + num_tx = 4 + k = 16 + use_real_rep=True + with self.assertRaises(AssertionError): + detector = KBestDetector(output, num_tx, k, + constellation_type="pam", + use_real_rep=use_real_rep) + + constellation = Constellation("pam", 4) + with self.assertRaises(AssertionError): + detector = KBestDetector(output, num_tx, k, + constellation=constellation, + use_real_rep=use_real_rep) + + def test_too_few_rx_antennas(self): + """Throw a warning if more streams than rx antennas""" + num_tx = 4 + num_rx_ant = 3 + num_bits_per_symbol = 4 + batch_size = 100 + k=64 + qam_source = QAMSource(num_bits_per_symbol, return_indices=True) + channel = FlatFadingChannel(num_tx, num_rx_ant, add_awgn=False, return_channel=True) + kbest_complex = KBestDetector("symbol", num_tx, k, "qam", num_bits_per_symbol, use_real_rep=False, hard_out=True) + kbest_real = KBestDetector("symbol", num_tx, k, "qam", num_bits_per_symbol, use_real_rep=True, hard_out=True) + x, x_ind = qam_source([batch_size, num_tx]) + y, h = channel(x) + s = tf.cast(1e-9*tf.eye(num_rx_ant), tf.complex64) + with self.assertRaises(AssertionError): + x_ind_hat = kbest_complex([y, h, s]) + with self.assertRaises(AssertionError): + x_ind_hat = kbest_real([y, h, s]) + + def test_init_real_rep(self): + num_bits_per_symbol = 4 + num_tx = 4 + k = 16 + constellation_type = "qam" + + detector = KBestDetector("bit", num_tx, k, + constellation_type="qam", + num_bits_per_symbol=num_bits_per_symbol, + use_real_rep=True) + self.assertEqual(detector._num_streams, 2*num_tx) + self.assertEqual(detector._num_symbols, 2**(num_bits_per_symbol//2)) + self.assertEqual(k, detector._k) + self.assertTrue(np.allclose(np.var(detector._constellation), 0.5)) + + detector = KBestDetector("bit", num_tx, k, + constellation=Constellation("qam", num_bits_per_symbol), + use_real_rep=True) + self.assertEqual(detector._num_streams, 2*num_tx) + self.assertEqual(detector._num_symbols, 2**(num_bits_per_symbol//2)) + self.assertEqual(k, detector._k) + self.assertTrue(np.allclose(np.var(detector._constellation), 0.5)) + + def test_symbol_errors_complex_rep(self): + """Test that we get no symbol error using the complex-valued representation""" + num_tx = 3 + num_rx_ant = 7 + num_bits_per_symbols = [2,4,6,8] + batch_size = 100 + k = 64 + for num_bits_per_symbol in num_bits_per_symbols: + qam_source = QAMSource(num_bits_per_symbol, return_indices=True) + channel = FlatFadingChannel(num_tx, num_rx_ant, add_awgn=False, return_channel=True) + kbest = KBestDetector("symbol", num_tx, k, "qam", num_bits_per_symbol, use_real_rep=False, hard_out=True) + x, x_ind = qam_source([batch_size, num_tx]) + y, h = channel(x) + s = tf.cast(1e-9*tf.eye(num_rx_ant), tf.complex64) + x_ind_hat = kbest([y, h, s]) + self.assertEqual(0, compute_ser(x_ind, x_ind_hat)) + + def test_symbol_errors_real_rep(self): + """Test that we get no symbol error using the real-valued representation""" + num_tx = 3 + num_rx_ant = 7 + num_bits_per_symbols = [2,4,6,8] + batch_size = 100 + k = 64 + for num_bits_per_symbol in num_bits_per_symbols: + qam_source = QAMSource(num_bits_per_symbol, return_indices=True) + channel = FlatFadingChannel(num_tx, num_rx_ant, add_awgn=False, return_channel=True) + kbest = KBestDetector("symbol", num_tx, k, "qam", num_bits_per_symbol, use_real_rep=True, hard_out=True) + x, x_ind = qam_source([batch_size, num_tx]) + y, h = channel(x) + s = tf.cast(1e-9*tf.eye(num_rx_ant), tf.complex64) + x_ind_hat = kbest([y, h, s]) + self.assertEqual(0, compute_ser(x_ind, x_ind_hat)) + + def test_symbol_errors_pam(self): + """Test that we get no symbol error using the complex-valued representation and PAM""" + num_tx = 4 + num_rx_ant = 8 + num_bits_per_symbols = [1,2,3,4] + batch_size = 100 + k = 16 + for num_bits_per_symbol in num_bits_per_symbols: + pam_source = PAMSource(num_bits_per_symbol, return_indices=True) + channel = FlatFadingChannel(num_tx, num_rx_ant, add_awgn=False, return_channel=True) + kbest = KBestDetector("symbol", num_tx, k, "pam", num_bits_per_symbol, use_real_rep=False, hard_out=True) + x, x_ind = pam_source([batch_size, num_tx]) + y, h = channel(x) + s = tf.cast(1e-9*tf.eye(num_rx_ant), tf.complex64) + x_ind_hat = kbest([y, h, s]) + self.assertEqual(0, compute_ser(x_ind, x_ind_hat)) + + def test_bit_errors_complex_rep(self): + """Test that we get no bit error using the complex-valued representation""" + num_tx = 3 + num_rx_ant = 7 + num_bits_per_symbols = [2,4,6,8] + batch_size = 100 + k = 64 + for num_bits_per_symbol in num_bits_per_symbols: + qam_source = QAMSource(num_bits_per_symbol, return_indices=True, return_bits=True) + channel = FlatFadingChannel(num_tx, num_rx_ant, add_awgn=False, return_channel=True) + kbest = KBestDetector("bit", num_tx, k, "qam", num_bits_per_symbol, use_real_rep=False, hard_out=True) + x, _, b = qam_source([batch_size, num_tx]) + y, h = channel(x) + s = tf.cast(1e-9*tf.eye(num_rx_ant), tf.complex64) + b_hat = kbest([y, h, s]) + self.assertEqual(0, compute_ber(b, b_hat)) + + def test_bit_errors_real_rep(self): + """Test that we get no bit error using the real-valued representation""" + num_tx = 3 + num_rx_ant = 7 + num_bits_per_symbols = [2,4,6,8] + batch_size = 100 + k = 64 + for num_bits_per_symbol in num_bits_per_symbols: + qam_source = QAMSource(num_bits_per_symbol, return_indices=True, return_bits=True) + channel = FlatFadingChannel(num_tx, num_rx_ant, add_awgn=False, return_channel=True) + kbest = KBestDetector("bit", num_tx, k, "qam", num_bits_per_symbol, use_real_rep=True, hard_out=True) + x, _, b = qam_source([batch_size, num_tx]) + y, h = channel(x) + s = tf.cast(1e-9*tf.eye(num_rx_ant), tf.complex64) + b_hat = kbest([y, h, s]) + self.assertEqual(0, compute_ber(b, b_hat)) + + def test_bit_errors_pam(self): + """Test that we get no bit error using the real-valued representation""" + num_tx = 4 + num_rx_ant = 7 + num_bits_per_symbols = [1,2,3,4] + batch_size = 100 + k = 16 + for num_bits_per_symbol in num_bits_per_symbols: + pam_source = PAMSource(num_bits_per_symbol, return_indices=True, return_bits=True) + channel = FlatFadingChannel(num_tx, num_rx_ant, add_awgn=False, return_channel=True) + kbest = KBestDetector("bit", num_tx, k, "pam", num_bits_per_symbol, use_real_rep=False, hard_out=True) + x, _, b = pam_source([batch_size, num_tx]) + y, h = channel(x) + s = tf.cast(1e-9*tf.eye(num_rx_ant), tf.complex64) + b_hat = kbest([y, h, s]) + self.assertEqual(0, compute_ber(b, b_hat)) + return + + def test_llr_against_ml_qam(self): + tf.random.set_seed(1) + num_tx = 3 + num_rx_ant = 8 + batch_size = 100 + def fun(ebno_db, num_bits_per_symbol, k, real_rep): + qam_source = QAMSource(num_bits_per_symbol, return_indices=True, return_bits=True) + channel = FlatFadingChannel(num_tx, num_rx_ant, add_awgn=True, return_channel=True) + kbest = KBestDetector("bit", num_tx, k, "qam", num_bits_per_symbol, use_real_rep=real_rep, hard_out=False) + kbest._list2llr.llr_clip_val = np.inf + ml = MaximumLikelihoodDetector("bit", "maxlog", num_tx, "qam", num_bits_per_symbol, hard_out=False) + no = ebnodb2no(ebno_db, num_bits_per_symbol, coderate=1) + x, x_ind, b = qam_source([batch_size, num_tx]) + y, h = channel([x, no]) + s = tf.cast(no*tf.eye(num_rx_ant), tf.complex64) + llr = kbest([y, h, s]) + llr_ml = ml([y, h, s]) + return np.allclose(llr, llr_ml, atol=1e-5) + + for ebno_db in [-20,-10,0,10,20,30,50]: + for num_bits_per_symbol in [2, 4]: + for real_rep in [True, False]: + k = (2**num_bits_per_symbol)**num_tx + self.assertTrue(fun(ebno_db, num_bits_per_symbol, k, real_rep)) + + def test_llr_against_ml_pam(self): + tf.random.set_seed(1) + num_tx = 3 + num_rx_ant = 8 + batch_size = 100 + def fun(ebno_db, num_bits_per_symbol, k): + pam_source = PAMSource(num_bits_per_symbol, return_indices=True, return_bits=True) + channel = FlatFadingChannel(num_tx, num_rx_ant, add_awgn=True, return_channel=True) + kbest = KBestDetector("bit", num_tx, k, "pam", num_bits_per_symbol, use_real_rep=False, hard_out=False) + kbest._list2llr.llr_clip_val = np.inf + ml = MaximumLikelihoodDetector("bit", "maxlog", num_tx, "pam", num_bits_per_symbol, hard_out=False) + no = ebnodb2no(ebno_db, num_bits_per_symbol, coderate=1) + x, x_ind, b = pam_source([batch_size, num_tx]) + y, h = channel([x, no]) + s = tf.cast(no*tf.eye(num_rx_ant), tf.complex64) + llr = kbest([y, h, s]) + llr_ml = ml([y, h, s]) + return np.allclose(llr, llr_ml, atol=1e-4) + + for ebno_db in [-20,-10,0,10,20,30,50]: + for num_bits_per_symbol in [1,2,3,4]: + k = (2**num_bits_per_symbol)**num_tx + self.assertTrue(fun(ebno_db, num_bits_per_symbol, k)) + + def test_e2e_uncoded_ber_vs_ml(self): + """Test uncoded BER against ML for some points also in XLA mode""" + sionna.config.xla_compat=True + tf.random.set_seed(1) + num_tx = 3 + num_rx_ant = 6 + num_bits_per_symbol = 4 + kbest = MIMODetectionBER(num_tx, num_rx_ant, num_bits_per_symbol, "kbest", coded=False) + ml = MIMODetectionBER(num_tx, num_rx_ant, num_bits_per_symbol, "ml", coded=False) + snr_range = np.arange(5,19, 1) + kbest_ber, kbest_bler = sim_ber(kbest, + snr_range, + batch_size=64, + max_mc_iter=1000, + num_target_block_errors=1000) + ml_ber, ml_bler = sim_ber(ml, + snr_range, + batch_size=64, + max_mc_iter=1000, + num_target_block_errors=1000) + self.assertTrue(np.allclose(kbest_ber, ml_ber, atol=1e-3)) + + def test_e2e_coded_ber_vs_ml(self): + """Test coded BER against ML for some points also in XLA mode""" + sionna.config.xla_compat=True + tf.random.set_seed(1) + num_tx = 3 + num_rx_ant = 6 + num_bits_per_symbol = 4 + kbest = MIMODetectionBER(num_tx, num_rx_ant, num_bits_per_symbol, "kbest", coded=True) + ml = MIMODetectionBER(num_tx, num_rx_ant, num_bits_per_symbol, "ml", coded=True) + snr_range = np.arange(7,9.5, 0.5) + kbest_ber, kbest_bler = sim_ber(kbest, + snr_range, + batch_size=16, + max_mc_iter=2000, + num_target_block_errors=2000) + ml_ber, ml_bler = sim_ber(ml, + snr_range, + batch_size=16, + max_mc_iter=2000, + num_target_block_errors=2000) + self.assertTrue(np.allclose(kbest_ber, ml_ber, rtol=0.1)) diff --git a/test/unit/mimo/test_mmse_pic_det.py b/test/unit/mimo/test_mmse_pic_det.py new file mode 100644 index 00000000..5585bbff --- /dev/null +++ b/test/unit/mimo/test_mmse_pic_det.py @@ -0,0 +1,385 @@ +# +# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +try: + import sionna +except ImportError as e: + import sys + sys.path.append("../") +import pytest +import unittest +import numpy as np +import tensorflow as tf +gpus = tf.config.list_physical_devices('GPU') +print('Number of GPUs available :', len(gpus)) +if gpus: + gpu_num = 0 # Number of the GPU to be used + try: + tf.config.set_visible_devices(gpus[gpu_num], 'GPU') + print('Only GPU number', gpu_num, 'used.') + tf.config.experimental.set_memory_growth(gpus[gpu_num], True) + except RuntimeError as e: + print(e) +import sionna +from sionna.mimo import LinearDetector, MMSEPICDetector +from sionna.channel import FlatFadingChannel, exp_corr_mat, PerColumnModel +from sionna.utils import BinarySource, sim_ber, ebnodb2no +from sionna.mapping import Mapper + +class TestMMSEPICDetector(unittest.TestCase): + + # Number of bits per symbol for modulation + NUM_BITS_PER_SYMBOL = 4 + + # Channel correlation exponent + CHANNEL_CORR_A = 0.8 + + # Max error : + MAX_ERR = 5e-2 + + + def run_e2e(self, det, batch_dims, num_rx_ant, num_tx_ant, ebno_dbs, exec_mode, dtype): + + tf.random.set_seed(42) + + num_bits_per_symbol = TestMMSEPICDetector.NUM_BITS_PER_SYMBOL + batch_dims = tf.cast(batch_dims, tf.int32) + + # + # Transmitter + # + binary_source = BinarySource(dtype=dtype.real_dtype) + mapper = Mapper("qam", num_bits_per_symbol, dtype=dtype) + + # + # Channel + # + spatial_corr_mat = exp_corr_mat(TestMMSEPICDetector.CHANNEL_CORR_A, + num_rx_ant, dtype) + spatial_corr = PerColumnModel(spatial_corr_mat) + channel = FlatFadingChannel(num_tx_ant, num_rx_ant, + spatial_corr=spatial_corr, + return_channel=True, + dtype=dtype) + + # + # Detector + # + if det == 'mmse-pic': + # MMSE-PIC + detector = MMSEPICDetector(demapping_method="maxlog", + num_iter=1, + output="bit", + constellation_type="qam", + num_bits_per_symbol=num_bits_per_symbol, + dtype=dtype) + elif det == 'lmmse': + # LMMSE + detector = LinearDetector(equalizer="lmmse", + output="bit", + demapping_method="maxlog", + constellation_type="qam", + num_bits_per_symbol=num_bits_per_symbol, + dtype=dtype) + + # Bits shape + bits_shape = tf.concat([batch_dims, [num_tx_ant, num_bits_per_symbol]], axis=0) + # Null prior + prior = tf.zeros(bits_shape, dtype.real_dtype) + # Noise covariance + s = tf.eye(num_rx_ant, dtype=dtype) + + def _run(batch_size, ebno_db): + # `batch_size` is ignored + + no = ebnodb2no(ebno_db, num_bits_per_symbol, 1.0) + + # + # Transmitter + # + + bits = binary_source(bits_shape) + x = mapper(bits) + x = tf.squeeze(x, axis=-1) + + # + # Channel + # + y,h = channel((x, no)) + + # + # Detector + # + s_ = tf.cast(no, dtype)*s + if det == 'mmse-pic': + llrs = detector((y, h, prior, s_)) + elif det == 'lmmse': + llrs = detector((y, h, s_)) + + return bits, llrs + + # Compile according to the specified execution mode + if exec_mode == 'eager': + _run_c = _run + elif exec_mode == 'graph': + _run_c = tf.function(_run) + elif exec_mode == 'xla': + _run_c = tf.function(_run, jit_compile=True) + + # Run over the range of N0s + ber,_ = sim_ber(_run_c, ebno_dbs, 1, + max_mc_iter=100, + num_target_bit_errors=1000, + soft_estimates=True, + early_stop=False, + dtype=dtype) + + return ber + + def run_test(self, batch_dims, num_rx_ant, num_tx_ant, ebno_dbs): + + # + # Test eager - simple precision + # + ber_lmmse = self.run_e2e('lmmse', + batch_dims, + num_rx_ant, + num_tx_ant, + ebno_dbs, + 'eager', + tf.complex64) + ber_mmse_pic = self.run_e2e('mmse-pic', + batch_dims, + num_rx_ant, + num_tx_ant, + ebno_dbs, + 'eager', + tf.complex64) + max_err = np.max(np.abs(ber_lmmse-ber_mmse_pic)/np.abs(ber_lmmse)) + # self.assertTrue(False, f"max err: {max_err}") + self.assertTrue(max_err < TestMMSEPICDetector.MAX_ERR) + + # + # Test graph - simple precision + # + ber_lmmse = self.run_e2e('lmmse', + batch_dims, + num_rx_ant, + num_tx_ant, + ebno_dbs, + 'graph', + tf.complex64) + ber_mmse_pic = self.run_e2e('mmse-pic', + batch_dims, + num_rx_ant, + num_tx_ant, + ebno_dbs, + 'graph', + tf.complex64) + max_err = np.max(np.abs(ber_lmmse-ber_mmse_pic)/np.abs(ber_lmmse)) + self.assertTrue(max_err < TestMMSEPICDetector.MAX_ERR) + + # + # Test xla - simple precision + # + # sionna.Config.xla_compat = True + # ber_lmmse = self.run_e2e('lmmse', + # batch_dims, + # num_rx_ant, + # num_tx_ant, + # ebno_dbs, + # 'xla', + # tf.complex64) + # ber_mmse_pic = self.run_e2e('mmse-pic', + # batch_dims, + # num_rx_ant, + # num_tx_ant, + # ebno_dbs, + # 'xla', + # tf.complex64) + # max_err = np.max(np.abs(ber_lmmse-ber_mmse_pic)/np.abs(ber_lmmse)) + # self.assertTrue(max_err < TestMMSEPICDetector.MAX_ERR) + # sionna.Config.xla_compat = False + + # + # Test eager - double precision + # + ber_lmmse = self.run_e2e('lmmse', + batch_dims, + num_rx_ant, + num_tx_ant, + ebno_dbs, + 'eager', + tf.complex128) + ber_mmse_pic = self.run_e2e('mmse-pic', + batch_dims, + num_rx_ant, + num_tx_ant, + ebno_dbs, + 'eager', + tf.complex128) + max_err = np.max(np.abs(ber_lmmse-ber_mmse_pic)/np.abs(ber_lmmse)) + self.assertTrue(max_err < TestMMSEPICDetector.MAX_ERR) + + # + # Test graph - double precision + # + ber_lmmse = self.run_e2e('lmmse', + batch_dims, + num_rx_ant, + num_tx_ant, + ebno_dbs, + 'graph', + tf.complex128) + ber_mmse_pic = self.run_e2e('mmse-pic', + batch_dims, + num_rx_ant, + num_tx_ant, + ebno_dbs, + 'graph', + tf.complex128) + max_err = np.max(np.abs(ber_lmmse-ber_mmse_pic)/np.abs(ber_lmmse)) + self.assertTrue(max_err < TestMMSEPICDetector.MAX_ERR) + + # + # Test xla - double precision + # + # sionna.Config.xla_compat = True + # ber_lmmse = self.run_e2e('lmmse', + # batch_dims, + # num_rx_ant, + # num_tx_ant, + # ebno_dbs, + # 'xla', + # tf.complex128) + # ber_mmse_pic = self.run_e2e('mmse-pic', + # batch_dims, + # num_rx_ant, + # num_tx_ant, + # ebno_dbs, + # 'xla', + # tf.complex128) + # max_err = np.max(np.abs(ber_lmmse-ber_mmse_pic)) + # self.assertTrue(max_err < TestMMSEPICDetector.MAX_ERR) + # sionna.Config.xla_compat = False + + def test_one_time_one(self): + self.run_test([64], 1, 1, [20.0]) + + def test_one_time_n(self): + self.run_test([64], 16, 1, [-5.0]) + + def test_m_time_n(self): + self.run_test([64], 16, 4, [0.0]) + + def test_batch_dims(self): + detector = MMSEPICDetector(demapping_method="maxlog", + num_iter=1, + output="bit", + constellation_type="qam", + num_bits_per_symbol=2, + dtype=tf.complex64) + # Arbitrary batch dims [8,4,3] + # 16 rx antennas + # 2 tx antennas + y = tf.random.normal([8,4,3,16,2]) + y = tf.complex(y[...,0], y[...,1]) + h = tf.random.normal([8,4,3,16,2,2]) + h = tf.complex(h[...,0], h[...,1]) + # Covariance matrix is the identity matrix + s = tf.eye(16, dtype=tf.complex64) + # Zero prior + # 2 tx + prior = tf.zeros([8,4,3,2,2]) + + # Run the detector + llrs = detector((y,h,prior,s)) + + # Test output shape + self.assertEqual(llrs.shape, [8,4,3,2,2]) + + def test_xla(self): + + detector = MMSEPICDetector(demapping_method="maxlog", + num_iter=1, + output="bit", + constellation_type="qam", + num_bits_per_symbol=2, + dtype=tf.complex64) + + + @tf.function(jit_compile=True) + def _run_xla(): + + # 16 rx antennas + # 2 tx antennas + y = tf.random.normal([64,16,2]) + y = tf.complex(y[...,0], y[...,1]) + h = tf.random.normal([64,16,2,2]) + h = tf.complex(h[...,0], h[...,1]) + # Covariance matrix is the identity matrix + s = tf.eye(16, dtype=tf.complex64) + # Zero prior + # 2 tx + prior = tf.zeros([64,2,2]) + + # Run the detector + llrs = detector((y,h,prior,s)) + + # Run in XLA + _run_xla() + + def test_prior_symbols(self): + + detector = MMSEPICDetector(demapping_method="maxlog", + num_iter=1, + output="symbol", + constellation_type="qam", + num_bits_per_symbol=2, # QPSK + dtype=tf.complex64) + + # 16 rx antennas + # 2 tx antennas + y = tf.random.normal([64,16,2]) + y = tf.complex(y[...,0], y[...,1]) + h = tf.random.normal([64,16,2,2]) + h = tf.complex(h[...,0], h[...,1]) + # Covariance matrix is the identity matrix + s = tf.eye(16, dtype=tf.complex64) + # Zero prior + # 2 tx + prior = tf.random.normal([64,2,4]) # QPSK + + # Run the detector + logits = detector((y,h,prior,s)) + + # Test output shape + self.assertEqual(logits.shape, [64,2,4]) + + def test_multiple_iterations(self): + + detector = MMSEPICDetector(demapping_method="maxlog", + num_iter=3, + output="bit", + constellation_type="qam", + num_bits_per_symbol=2, # QPSK + dtype=tf.complex64) + + # 16 rx antennas + # 2 tx antennas + y = tf.random.normal([64,16,2]) + y = tf.complex(y[...,0], y[...,1]) + h = tf.random.normal([64,16,2,2]) + h = tf.complex(h[...,0], h[...,1]) + # Covariance matrix is the identity matrix + s = tf.eye(16, dtype=tf.complex64) + # Zero prior + # 2 tx + prior = tf.random.normal([64,2,2]) # QPSK + + # Run the detector + logits = detector((y,h,prior,s)) + + # Test output shape + self.assertEqual(logits.shape, [64,2,2]) diff --git a/test/unit/ofdm/test_ofdm_channel_estimation.py b/test/unit/ofdm/test_ofdm_channel_estimation.py index 6a8d3dc3..7f74a4b6 100644 --- a/test/unit/ofdm/test_ofdm_channel_estimation.py +++ b/test/unit/ofdm/test_ofdm_channel_estimation.py @@ -7,19 +7,25 @@ import sionna except ImportError as e: import sys - sys.path.append("../") + sys.path.append("..") + import sionna from sionna.mimo import StreamManagement -from sionna.ofdm import ResourceGrid, ResourceGridMapper, LSChannelEstimator, PilotPattern, KroneckerPilotPattern +from sionna.ofdm import ResourceGrid, ResourceGridMapper, LSChannelEstimator, PilotPattern, KroneckerPilotPattern, LMMSEInterpolator, tdl_freq_cov_mat, tdl_time_cov_mat from sionna.channel.tr38901 import Antenna, AntennaArray, UMi from sionna.channel import gen_single_sector_topology as gen_topology -from sionna.channel import subcarrier_frequencies, cir_to_ofdm_channel, cir_to_time_channel -from sionna.channel import ApplyOFDMChannel +from sionna.channel import subcarrier_frequencies, cir_to_ofdm_channel +from sionna.channel import ApplyOFDMChannel, exp_corr_mat +from sionna.utils import QAMSource,ebnodb2no +from sionna.mapping import Mapper +from sionna.channel.tr38901 import TDL import pytest import unittest import numpy as np import tensorflow as tf +import itertools + gpus = tf.config.list_physical_devices('GPU') print('Number of GPUs available :', len(gpus)) @@ -96,7 +102,7 @@ def time_int(h, time_avg=False): x0 = x1 x1 += 1 h_int = (x-x_0)*np.divide(y_1-y_0, x_1-x_0, out=np.zeros_like(h), where=x_1-x_0!=0) + y_0 - return h_int + return h_int def linear_int(h, i, j, time_avg=False): """Linear interpolation on a 2D resource grid @@ -342,3 +348,839 @@ def test_kronecker_pilot_patterns_with_time_averaging(self): check_linear_interpolation(self, rg.pilot_pattern, mode="eager") check_linear_interpolation(self, rg.pilot_pattern, mode="graph") check_linear_interpolation(self, rg.pilot_pattern, mode="xla") + +####################################################### +# Test LMMSE interpolation +####################################################### + +class TestLMMSEInterpolator(unittest.TestCase): + + # Batch size for the tests + BATCH_SIZE = 1 + + # SNR values for which tests are run + EBN0DBs = [0.0] + + # Allowed absolute error + # Single precision and XLA + ATOL_LOW_PREC = 1e-3 + # Double precision without XLA + ATOL_HIGH_PREC = 1e-10 + + ######################################## + # Reference implementation + ######################################## + + def pilot_pattern_2_pilot_mask(self, pilot_pattern): + # pilot_pattern : PilotPattern + # An instance of PilotPattern + + data_mask = pilot_pattern.mask + pilots = pilot_pattern.pilots + + num_tx = data_mask.shape[0] + num_steams_per_tx = data_mask.shape[1] + num_ofdm_symbols = data_mask.shape[2] + num_effective_subcarriers = data_mask.shape[3] + pilot_mask = np.zeros([num_tx,num_steams_per_tx,num_ofdm_symbols,num_effective_subcarriers], bool) + for tx in range(num_tx): + for st in range(num_steams_per_tx): + pil_ind = 0 # Pilot index for this stream + for sb in range(num_ofdm_symbols): + for sc in range(num_effective_subcarriers): + if data_mask[tx,st,sb,sc]: + if np.abs(pilots[tx,st,pil_ind]) > 0.: + pilot_mask[tx,st,sb,sc] = True + pil_ind += 1 + return pilot_mask + + def map_estimates_to_rg(self, h_hat, err_var, pilot_pattern): + # h_hat : [batch_size, num_tx, num_streams_per_tx, num_rx, num_rx_ant, num_pilots] + # Channel estimates + # + # err_var : [batch_size, num_tx, num_streams_per_tx, num_rx, num_rx_ant, num_pilots] + # Channel estimation error variances + # + # pilot_pattern : PilotPattern + # An instance of PilotPattern + + data_mask = pilot_pattern.mask + pilots = pilot_pattern.pilots + + batch_size = h_hat.shape[0] + num_rx = h_hat.shape[1] + num_rx_ant = h_hat.shape[2] + num_tx = h_hat.shape[3] + num_steams_per_tx = h_hat.shape[4] + num_ofdm_symbols = data_mask.shape[2] + num_effective_subcarriers = data_mask.shape[3] + h_hat_rg = np.zeros([batch_size,num_rx,num_rx_ant,num_tx,num_steams_per_tx,num_ofdm_symbols,num_effective_subcarriers], complex) + err_var_rg = np.zeros([batch_size,num_rx,num_rx_ant,num_tx,num_steams_per_tx,num_ofdm_symbols,num_effective_subcarriers], float) + for bs in range(batch_size): + for rx in range(num_rx): + for ra in range(num_rx_ant): + for tx in range(num_tx): + for st in range(num_steams_per_tx): + pil_ind = 0 # Pilot index for this stream + for sb in range(num_ofdm_symbols): + for sc in range(num_effective_subcarriers): + if data_mask[tx,st,sb,sc]: + if np.abs(pilots[tx,st,pil_ind]) > 0.: + h_hat_rg[bs,rx,ra,tx,st,sb,sc] = h_hat[bs,rx,ra,tx,st,pil_ind] + err_var_rg[bs,rx,ra,tx,st,sb,sc] = err_var[bs,rx,ra,tx,st,pil_ind] + pil_ind += 1 + return h_hat_rg,err_var_rg + + def reference_lmmse_interpolation_1d_one_axis(self, cov_mat, h_hat, err_var, pattern, last_step): + + # cov_mat : [dim_size, dim_size] + # Covariance matrix + # + # h_hat : [dim_size] + # Channel estimate at pilot locations. Zeros elsewhere. + # + # err_var : [dim_size] + # Channel estimation error variance at pilot locations. Zero elsewhere. + # + # pattern : [dim_size] + # Mask indicating where a channel estimate is available. + # + # last_step : bool + # If `False`, this is not the last step, and an additional scaling is done + # to prepare for the next interpolation/smoothing. + + err_var_old = err_var + + # + # Build interpolation matrix + # + dim_size = pattern.shape[0] + pil_ind, = np.where(pattern) + num_pil = pil_ind.shape[0] + + pi_mat = np.zeros([dim_size, num_pil]) + k = 0 + for i in range(dim_size): + if pattern[i]: + pi_mat[i,k] = 1.0 + k += 1 + + int_mat = np.matmul(np.matmul(pi_mat.T, cov_mat), pi_mat) + err_var = np.take(err_var, pil_ind, axis=0) + int_mat = int_mat + np.diag(err_var) + int_mat = np.linalg.inv(int_mat) + int_mat = np.matmul(pi_mat, np.matmul(int_mat, pi_mat.T)) + int_mat = np.matmul(cov_mat, int_mat) + + # + # Interpolation + # + h_hat = np.matmul(int_mat, h_hat) + + # + # Error variance + # + mask_mat = np.zeros([dim_size, dim_size]) + for i in range(dim_size): + if pattern[i]: + mask_mat[i,i] = 1.0 + err_var = cov_mat - np.matmul(int_mat, np.matmul(mask_mat, cov_mat)) + err_var = np.diag(err_var).real + + # + # Scaling if not last step + # + if not last_step: + # Estimate covariance + int_mat_h = np.conj(int_mat.T) + h_hat_var = np.matmul(int_mat, np.matmul(cov_mat+np.diag(err_var_old), int_mat_h)) + h_hat_var = np.diag(h_hat_var).real + # Scaling + s = 2./(1.+h_hat_var-err_var) + h_hat = s*h_hat + # Update error variance + err_var = s*(s-1)*h_hat_var + (1.-s) + s*err_var + + return h_hat, err_var + + def reference_spatial_smoothing_one_re(self, cov_mat, h_hat, err_var, last_step): + + # cov_mat : [num_rx_ant, num_rx_ant] + # Covariance matrix + # + # h_hat : [num_rx_ant] + # Channel estimate at pilot locations. Zeros elsewhere. + # + # err_var : [num_rx_ant] + # Channel estimation error variance at pilot locations. Zero elsewhere. + # + # last_step : bool + # If `False`, this is not the last step, and an additional scaling is done + # to prepare for the next interpolation/smoothing. + + A = cov_mat + np.diag(err_var) + A = np.linalg.inv(A) + A = np.matmul(cov_mat,A) + + h_hat = np.expand_dims(h_hat, axis=-1) + h_hat = np.matmul(A, h_hat) + h_hat = np.squeeze(h_hat, axis=-1) + + err_var_out = cov_mat - np.matmul(A,cov_mat) + err_var_out = np.diag(err_var_out).real + + if not last_step: + # Estimate covariance + Ah = np.conj(A.T) + h_hat_var = np.matmul(A, np.matmul(cov_mat+np.diag(err_var), Ah)) + h_hat_var = np.diag(h_hat_var).real + # Scaling + s = 2./(1.+h_hat_var-err_var_out) + h_hat = s*h_hat + # Update error variance + err_var_out = s*(s-1)*h_hat_var + (1.-s) + s*err_var_out + + return h_hat, err_var_out + + def reference_spatial_smoothing(self, cov_mat, h_hat, err_var, last_step): + + # cov_mat : [num_rx_ant, num_rx_ant] + # Covariance matrix + # + # h_hat : [batch_size, num_rx, num_rx_ant, num_tx, num_tx_streams, num_ofdm_symbols, num_effectve_subcarriers] + # Channel estimate at pilot locations. Zeros elsewhere. + # + # err_var : [batch_size, num_rx, num_rx_ant, num_tx, num_tx_streams, num_ofdm_symbols, num_effectve_subcarriers] + # Channel estimation error variance at pilot locations. Zero elsewhere. + # + # last_step : bool + # If `False`, this is not the last step, and an additional scaling is done + # to prepare for the next interpolation/smoothing. + + # [batch_size, num_rx, num_tx, num_tx_streams, num_ofdm_symbols, num_effectve_subcarriers, num_rx_ant] + h_hat = np.transpose(h_hat, [0, 1, 3, 4, 5, 6, 2]) + err_var = np.transpose(err_var, [0, 1, 3, 4, 5, 6, 2]) + + h_hat_shape = h_hat.shape + num_rx_ant = h_hat.shape[-1] + h_hat = np.reshape(h_hat, [-1, num_rx_ant]) + err_var = np.reshape(err_var, [-1, num_rx_ant]) + + i = 0 + for h_hat_, err_var_ in zip(h_hat, err_var): + h_hat_new, err_var_new = self.reference_spatial_smoothing_one_re(cov_mat, h_hat_, err_var_, last_step) + h_hat[i] = h_hat_new + err_var[i] = err_var_new + i = i + 1 + + h_hat = np.reshape(h_hat, h_hat_shape) + err_var = np.reshape(err_var, h_hat_shape) + + # [batch_size, num_rx, num_rx_ant, num_tx, num_tx_streams, num_ofdm_symbols, num_effectve_subcarriers] + h_hat = np.transpose(h_hat, [0, 1, 6, 2, 3, 4, 5]) + err_var = np.transpose(err_var, [0, 1, 6, 2, 3, 4, 5]) + + return h_hat, err_var + + def reference_lmmse_interpolation_1d(self, cov_mat, h_hat, err_var, pattern, last_step): + + # cov_mat : [dim_size, dim_size] + # Covariance matrix + # + # h_hat : [batch_size, num_rx, num_rx_ant, num_tx, num_tx_streams, outer_dim_size, inner_dim_size] + # Channel estimate at pilot locations. Zeros elsewhere. + # + # err_var : [batch_size, num_rx, num_rx_ant, num_tx, num_tx_streams, outer_dim_size, inner_dim_size] + # Channel estimation error variance at pilot locations. Zero elsewhere. + # + # pattern : [num_tx, num_tx_streams, outer_dim_size, inner_dim_size] + # Mask indicating where a channel estimate is available. + # + # last_step : bool + # If `False`, this is not the last step, and an additional scaling is done + # to prepare for the next interpolation/smoothing. + + batch_size = h_hat.shape[0] + num_rx = h_hat.shape[1] + num_rx_ant = h_hat.shape[2] + num_tx = h_hat.shape[3] + num_tx_streams = h_hat.shape[4] + outer_dim_size = h_hat.shape[5] + inner_dim_size = h_hat.shape[6] + + for b,rx,ra,tx,ts,od in itertools.product(range(batch_size), + range(num_rx), + range(num_rx_ant), + range(num_tx), + range(num_tx_streams), + range(outer_dim_size)): + h_hat_ = h_hat[b,rx,ra,tx,ts,od] + err_var_ = err_var[b,rx,ra,tx,ts,od] + pattern_ = pattern[tx,ts,od] + if np.any(pattern_): + h_hat_, err_var_ = self.reference_lmmse_interpolation_1d_one_axis(cov_mat, h_hat_, err_var_, pattern_, last_step) + h_hat[b,rx,ra,tx,ts,od] = h_hat_ + err_var[b,rx,ra,tx,ts,od] = err_var_ + + # Updating the pattern + pattern_update_mask = np.any(pattern, axis=-1, keepdims=True) + pattern = np.logical_or(pattern, pattern_update_mask) + + return h_hat, err_var, pattern + + def reference_lmmse_interpolation(self, cov_mat_time, cov_mat_freq, cov_mat_space, h_hat, err_var, pattern, order): + + # cov_mat_time : [num_ofdm_symbols, num_ofdm_symbols] + # Time covariance matrix + # + # cov_mat_freq : [num_effectve_subcarriers, num_effectve_subcarriers] + # Frequency covariance matrix + # + # h_hat : [batch_size, num_rx, num_rx_ant, num_tx, num_tx_streams, num_ofdm_symbols, num_effectve_subcarriers] + # Channel estimate at pilot locations. Zeros elsewhere. + # + # err_var : [batch_size, num_rx, num_rx_ant, num_tx, num_tx_streams, num_ofdm_symbols, num_effectve_subcarriers] + # Channel estimation error variance at pilot locations. Zero elsewhere. + # + # pattern : PilotPattern + # A Sionna pilot pattern + # + # order : 'freq_first' or 'time_first' + # Order in which to do the 1D interpolation + + pilot_mask = self.pilot_pattern_2_pilot_mask(pattern) + h_hat,err_var = self.map_estimates_to_rg(h_hat, err_var, pattern) + + if order == 'f-t': + h_hat, err_var, pilot_mask = self.reference_lmmse_interpolation_1d(cov_mat_freq, h_hat, err_var, pilot_mask, False) + h_hat = np.transpose(h_hat, [0, 1, 2, 3, 4, 6, 5]) + err_var = np.transpose(err_var, [0, 1, 2, 3, 4, 6, 5]) + pilot_mask = np.transpose(pilot_mask, [0, 1, 3, 2]) + h_hat, err_var,_ = self.reference_lmmse_interpolation_1d(cov_mat_time, h_hat, err_var, pilot_mask, True) + h_hat = np.transpose(h_hat, [0, 1, 2, 3, 4, 6, 5]) + err_var = np.transpose(err_var, [0, 1, 2, 3, 4, 6, 5]) + elif order == 't-f': + h_hat = np.transpose(h_hat, [0, 1, 2, 3, 4, 6, 5]) + err_var = np.transpose(err_var, [0, 1, 2, 3, 4, 6, 5]) + pilot_mask = np.transpose(pilot_mask, [0, 1, 3, 2]) + h_hat, err_var,pilot_mask = self.reference_lmmse_interpolation_1d(cov_mat_time, h_hat, err_var, pilot_mask, False) + h_hat = np.transpose(h_hat, [0, 1, 2, 3, 4, 6, 5]) + err_var = np.transpose(err_var, [0, 1, 2, 3, 4, 6, 5]) + pilot_mask = np.transpose(pilot_mask, [0, 1, 3, 2]) + h_hat, err_var,_ = self.reference_lmmse_interpolation_1d(cov_mat_freq, h_hat, err_var, pilot_mask, True) + elif order == 't-s-f': + h_hat = np.transpose(h_hat, [0, 1, 2, 3, 4, 6, 5]) + err_var = np.transpose(err_var, [0, 1, 2, 3, 4, 6, 5]) + pilot_mask = np.transpose(pilot_mask, [0, 1, 3, 2]) + h_hat, err_var,pilot_mask = self.reference_lmmse_interpolation_1d(cov_mat_time, h_hat, err_var, pilot_mask, False) + h_hat = np.transpose(h_hat, [0, 1, 2, 3, 4, 6, 5]) + err_var = np.transpose(err_var, [0, 1, 2, 3, 4, 6, 5]) + pilot_mask = np.transpose(pilot_mask, [0, 1, 3, 2]) + h_hat, err_var = self.reference_spatial_smoothing(cov_mat_space, h_hat, err_var, False) + h_hat, err_var,_ = self.reference_lmmse_interpolation_1d(cov_mat_freq, h_hat, err_var, pilot_mask, True) + + return h_hat, err_var + + ########################################## + # Tests + ########################################## + + # Run an E2E link with reference and Sionna LMMSE interpolation and compute + # the maximums error for both the estimate and error variance and both + # time first and frequency first interpolation + def run_e2e_link(self, batch_size, num_rx, num_rx_ant, num_tx, num_streams_per_tx, + num_ofdm_symbols, fft_size, pilot_pattern, ebno_db, exec_mode, dtype): + + assert exec_mode in ('eager', 'graph', 'xla'), "Wrong execution mode" + + tdl_model = 'A' + subcarrier_spacing = 30e3 # Hz + num_bits_per_symbol = 2 + delay_spread = 300e-9 # s + carrier_frequency = 3.5e9 # Hz + speed = 5. # m/s + + sm = StreamManagement(np.ones([num_rx, num_tx]), num_streams_per_tx) + rg = ResourceGrid(num_ofdm_symbols=num_ofdm_symbols, + fft_size=fft_size, + subcarrier_spacing=subcarrier_spacing, + num_tx=num_tx, + num_streams_per_tx=num_streams_per_tx, + cyclic_prefix_length=0, + pilot_pattern=pilot_pattern, + dtype=dtype) + + # Transmitter + qam_source = QAMSource(num_bits_per_symbol, dtype=dtype) + mapper = Mapper("qam", num_bits_per_symbol, dtype=dtype) + rg_mapper = ResourceGridMapper(rg, dtype=dtype) + + # OFDM CHannel + los_angle_of_arrival=np.pi/4. + channel_model = TDL(tdl_model, delay_spread, carrier_frequency, min_speed=speed, max_speed=speed, + los_angle_of_arrival=los_angle_of_arrival, dtype=dtype) + channel_freq = ApplyOFDMChannel(add_awgn=True, dtype=dtype) + frequencies = subcarrier_frequencies(fft_size, subcarrier_spacing, dtype=dtype) + + # The LS channel estimator will provide channel estimates and error variances + cov_mat_freq = tdl_freq_cov_mat(tdl_model, subcarrier_spacing, fft_size, delay_spread, dtype) + cov_mat_time = tdl_time_cov_mat(tdl_model, speed, carrier_frequency, rg.ofdm_symbol_duration, + num_ofdm_symbols, los_angle_of_arrival, dtype) + cov_mat_space = exp_corr_mat(0.9, num_rx_ant, dtype) + lmmse_inter_ft = LMMSEInterpolator(pilot_pattern, cov_mat_time, cov_mat_freq, order="f-t") + ls_est_lmmse_ft = LSChannelEstimator(rg, interpolator=lmmse_inter_ft, dtype=dtype) + lmmse_inter_tf = LMMSEInterpolator(pilot_pattern, cov_mat_time, cov_mat_freq, order="t-f") + ls_est_lmmse_tf = LSChannelEstimator(rg, interpolator=lmmse_inter_tf, dtype=dtype) + lmmse_inter_tsf = LMMSEInterpolator(pilot_pattern, cov_mat_time, cov_mat_freq, cov_mat_space, order="t-s-f") + ls_est_lmmse_tsf = LSChannelEstimator(rg, interpolator=lmmse_inter_tsf, dtype=dtype) + + # For computing the reference interpolation + ls_no_interp = LSChannelEstimator(rg, interpolation_type=None, dtype=dtype) + + def _run(): + no = ebnodb2no(ebno_db, num_bits_per_symbol, coderate=1.0) + x = qam_source([batch_size, num_tx, num_streams_per_tx, rg.num_data_symbols]) + x_rg = rg_mapper(x) + + a, tau = channel_model(batch_size, num_ofdm_symbols, sampling_frequency=1./rg.ofdm_symbol_duration) + h_freq = cir_to_ofdm_channel(frequencies, a, tau, normalize=True) + y = channel_freq([x_rg, h_freq, no]) + + h_hat_lmmse_ft,err_var_lmmse_ft = ls_est_lmmse_ft([y, no]) + h_hat_lmmse_tf,err_var_lmmse_tf = ls_est_lmmse_tf([y, no]) + h_hat_lmmse_tsf,err_var_lmmse_tsf = ls_est_lmmse_tsf([y, no]) + h_hat_no_int, err_var_no_int = ls_no_interp([y, no]) + + return h_hat_no_int, err_var_no_int, h_hat_lmmse_ft, err_var_lmmse_ft, h_hat_lmmse_tf, err_var_lmmse_tf, h_hat_lmmse_tsf, err_var_lmmse_tsf, h_freq + + if exec_mode == 'eager': + _run_compiled = _run + elif exec_mode == 'graph': + _run_compiled = tf.function(_run) + elif exec_mode == 'xla': + _run_compiled = tf.function(_run, jit_compile=True) + + run_output = _run_compiled() + h_hat_no_int = run_output[0].numpy() + err_var_no_int = run_output[1].numpy() + err_var_no_int = np.broadcast_to(err_var_no_int, h_hat_no_int.shape) + h_hat_lmmse_ft = run_output[2].numpy() + err_var_lmmse_ft = run_output[3].numpy() + h_hat_lmmse_tf = run_output[4].numpy() + err_var_lmmse_tf = run_output[5].numpy() + h_hat_lmmse_tsf = run_output[6].numpy() + err_var_lmmse_tsf = run_output[7].numpy() + h_freq = run_output[8].numpy() + + # Reference estimate + h_hat_lmmse_ft_ref, err_var_lmmse_ft_ref = self.reference_lmmse_interpolation(cov_mat_time.numpy(), + cov_mat_freq.numpy(), + cov_mat_space.numpy(), + h_hat_no_int, err_var_no_int, + pilot_pattern, "f-t") + h_hat_lmmse_tf_ref, err_var_lmmse_tf_ref = self.reference_lmmse_interpolation(cov_mat_time.numpy(), + cov_mat_freq.numpy(), + cov_mat_space.numpy(), + h_hat_no_int, err_var_no_int, + pilot_pattern, "t-f") + h_hat_lmmse_tsf_ref, err_var_lmmse_tsf_ref = self.reference_lmmse_interpolation(cov_mat_time.numpy(), + cov_mat_freq.numpy(), + cov_mat_space.numpy(), + h_hat_no_int, err_var_no_int, + pilot_pattern, "t-s-f") + + # Compute errors + max_err_h_hat_ft = np.max(np.abs(h_hat_lmmse_ft_ref-h_hat_lmmse_ft)) + max_err_err_var_lmmse_ft = np.max(np.abs(err_var_lmmse_ft_ref-err_var_lmmse_ft)) + max_err_h_hat_tf = np.max(np.abs(h_hat_lmmse_tf_ref-h_hat_lmmse_tf)) + max_err_err_var_lmmse_tf = np.max(np.abs(err_var_lmmse_tf_ref-err_var_lmmse_tf)) + max_err_h_hat_tsf = np.max(np.abs(h_hat_lmmse_tsf_ref-h_hat_lmmse_tsf)) + max_err_err_var_lmmse_tsf = np.max(np.abs(err_var_lmmse_tsf_ref-err_var_lmmse_tsf)) + + return max_err_h_hat_ft,max_err_err_var_lmmse_ft,max_err_h_hat_tf,max_err_err_var_lmmse_tf,max_err_h_hat_tsf,max_err_err_var_lmmse_tsf + + def run_test(self, num_rx, num_rx_ant, num_tx, num_streams_per_tx, num_ofdm_symbols, + fft_size, mask, pilots): + + tf.random.set_seed(42) + + def _test(num_rx, num_rx_ant, num_tx, num_streams_per_tx, num_ofdm_symbols, + fft_size, pilot_pattern, ebno_db, exec_mode, dtype): + if exec_mode == 'xla': + sionna.Config.xla_compat = True + outputs = self.run_e2e_link(TestLMMSEInterpolator.BATCH_SIZE, num_rx, num_rx_ant, num_tx, + num_streams_per_tx, num_ofdm_symbols, fft_size, pilot_pattern, ebno_db, exec_mode, dtype) + if exec_mode == 'xla': + sionna.Config.xla_compat = False + + if dtype == tf.complex64 or exec_mode == "xla": + atol = TestLMMSEInterpolator.ATOL_LOW_PREC + else: + atol = TestLMMSEInterpolator.ATOL_HIGH_PREC + + max_err_h_hat_ft = outputs[0] + self.assertTrue(np.allclose(max_err_h_hat_ft, 0.0, atol=atol)) + + max_err_err_var_lmmse_ft = outputs[1] + self.assertTrue(np.allclose(max_err_err_var_lmmse_ft, 0.0, atol=atol)) + + max_err_h_hat_tf = outputs[2] + self.assertTrue(np.allclose(max_err_h_hat_tf, 0.0, atol=atol)) + + max_err_err_var_lmmse_tf = outputs[3] + self.assertTrue(np.allclose(max_err_err_var_lmmse_tf, 0.0, atol=atol)) + + max_err_h_hat_tsf = outputs[4] + self.assertTrue(np.allclose(max_err_h_hat_tsf, 0.0, atol=atol)) + + max_err_err_var_lmmse_tsf = outputs[5] + self.assertTrue(np.allclose(max_err_err_var_lmmse_tsf, 0.0, atol=atol)) + + for ebno_db in TestLMMSEInterpolator.EBN0DBs: + # 32bit precision + pilot_pattern = PilotPattern(mask, pilots, dtype=tf.complex64) + ebno_db_sp = tf.cast(ebno_db, tf.float32) + _test(num_rx, num_rx_ant, num_tx, num_streams_per_tx, num_ofdm_symbols, + fft_size, pilot_pattern, ebno_db_sp, "eager", tf.complex64) + _test(num_rx, num_rx_ant, num_tx, num_streams_per_tx, num_ofdm_symbols, + fft_size, pilot_pattern, ebno_db_sp, "graph", tf.complex64) + # XLA is not supported + # _test(num_rx, num_rx_ant, num_tx, num_streams_per_tx, num_ofdm_symbols, + # fft_size, pilot_pattern, ebno_db_sp, "xla", tf.complex64) + # 64bit precision + pilot_pattern = PilotPattern(mask, pilots, dtype=tf.complex128) + ebno_db_dp = tf.cast(ebno_db, tf.float64) + _test(num_rx, num_rx_ant, num_tx, num_streams_per_tx, num_ofdm_symbols, + fft_size, pilot_pattern, ebno_db_dp, "eager", tf.complex128) + _test(num_rx, num_rx_ant, num_tx, num_streams_per_tx, num_ofdm_symbols, + fft_size, pilot_pattern, ebno_db_dp, "graph", tf.complex128) + # XLA is not supported + # _test(num_rx, num_rx_ant, num_tx, num_streams_per_tx, num_ofdm_symbols, + # fft_size, pilot_pattern, ebno_db_dp, "xla", tf.complex128) + + def test_sparse_pilot_pattern(self): + "One UT has two pilots, three others have just one" + num_tx = 4 + num_streams_per_tx = 1 + num_ofdm_symbols = 14 + fft_size = 12 + mask = np.zeros([num_tx, num_streams_per_tx, num_ofdm_symbols, fft_size], bool) + mask[...,5,:] = True + num_pilots = np.sum(mask[0,0]) + pilots = np.zeros([num_tx, num_streams_per_tx, num_pilots]) + pilots[0,0,[0,11]] = 1 + pilots[1,0,1] = 1 + pilots[2,0,5] = 1 + pilots[3,0,10] = 1 + self.run_test(1, 1, num_tx, num_streams_per_tx, num_ofdm_symbols, fft_size, mask, pilots) + + def test_kronecker_pilot_patterns_01(self): + num_tx = 1 + num_streams_per_tx = 1 + num_ofdm_symbols = 14 + fft_size = 64 + pilot_ofdm_symbol_indices = [2, 11] + rg = ResourceGrid(num_ofdm_symbols=num_ofdm_symbols, + fft_size=fft_size, + subcarrier_spacing=30e3, + num_tx=num_tx, + num_streams_per_tx=num_streams_per_tx, + cyclic_prefix_length=0, + pilot_pattern="kronecker", + pilot_ofdm_symbol_indices=pilot_ofdm_symbol_indices) + pilot_pattern = rg.pilot_pattern + pilot_pattern = KroneckerPilotPattern(rg, pilot_ofdm_symbol_indices) + self.run_test(1, 1, num_tx, num_streams_per_tx, num_ofdm_symbols, fft_size, + pilot_pattern.mask, pilot_pattern.pilots) + + def test_kronecker_pilot_patterns_02(self): + "Only a single pilot symbol" + num_tx = 4 + num_streams_per_tx = 1 + num_ofdm_symbols = 14 + fft_size = 64 + pilot_ofdm_symbol_indices = [2] + rg = ResourceGrid(num_ofdm_symbols=num_ofdm_symbols, + fft_size=fft_size, + subcarrier_spacing=30e3, + num_tx=num_tx, + num_streams_per_tx=num_streams_per_tx, + cyclic_prefix_length=0, + pilot_pattern="kronecker", + pilot_ofdm_symbol_indices=pilot_ofdm_symbol_indices) + pilot_pattern = rg.pilot_pattern + self.run_test(1, 1, num_tx, num_streams_per_tx, num_ofdm_symbols, fft_size, + pilot_pattern.mask, pilot_pattern.pilots) + + def test_kronecker_pilot_patterns_03(self): + "Only one pilot per UT" + num_tx = 16 + num_streams_per_tx = 1 + num_ofdm_symbols = 14 + fft_size = 16 + pilot_ofdm_symbol_indices = [2] + rg = ResourceGrid(num_ofdm_symbols=num_ofdm_symbols, + fft_size=fft_size, + subcarrier_spacing=30e3, + num_tx=num_tx, + num_streams_per_tx=num_streams_per_tx, + cyclic_prefix_length=0, + pilot_pattern="kronecker", + pilot_ofdm_symbol_indices=pilot_ofdm_symbol_indices) + pilot_pattern = rg.pilot_pattern + self.run_test(1, 1, num_tx, num_streams_per_tx, num_ofdm_symbols, fft_size, + pilot_pattern.mask, pilot_pattern.pilots) + + def test_kronecker_pilot_patterns_04(self): + "Multi UT, multi stream" + num_tx = 4 + num_streams_per_tx = 2 + num_ofdm_symbols = 14 + fft_size = 64 + pilot_ofdm_symbol_indices = [2, 5, 8] + rg = ResourceGrid(num_ofdm_symbols=num_ofdm_symbols, + fft_size=fft_size, + subcarrier_spacing=30e3, + num_tx=num_tx, + num_streams_per_tx=num_streams_per_tx, + cyclic_prefix_length=0, + pilot_pattern="kronecker", + pilot_ofdm_symbol_indices=pilot_ofdm_symbol_indices) + pilot_pattern = rg.pilot_pattern + self.run_test(1, 1, num_tx, num_streams_per_tx, num_ofdm_symbols, fft_size, + pilot_pattern.mask, pilot_pattern.pilots) + + def test_kronecker_pilot_patterns_05(self): + "Single UT, only pilots" + num_tx = 1 + num_streams_per_tx = 1 + num_ofdm_symbols = 5 + fft_size = 64 + pilot_ofdm_symbol_indices = np.arange(0, num_ofdm_symbols) + rg = ResourceGrid(num_ofdm_symbols=num_ofdm_symbols, + fft_size=fft_size, + subcarrier_spacing=30e3, + num_tx=num_tx, + num_streams_per_tx=num_streams_per_tx, + cyclic_prefix_length=0, + pilot_pattern="kronecker", + pilot_ofdm_symbol_indices=pilot_ofdm_symbol_indices) + pilot_pattern = rg.pilot_pattern + self.run_test(1, 1, num_tx, num_streams_per_tx, num_ofdm_symbols, fft_size, + pilot_pattern.mask, pilot_pattern.pilots) + + def test_kronecker_pilot_patterns_06(self): + num_tx = 4 + num_streams_per_tx = 1 + num_ofdm_symbols = 14 + fft_size = 64 + pilot_ofdm_symbol_indices = [2,3,8, 11] + rg = ResourceGrid(num_ofdm_symbols=num_ofdm_symbols, + fft_size=fft_size, + subcarrier_spacing=30e3, + num_tx=num_tx, + num_streams_per_tx=num_streams_per_tx, + cyclic_prefix_length=0, + pilot_pattern="kronecker", + pilot_ofdm_symbol_indices=pilot_ofdm_symbol_indices) + pilot_pattern = rg.pilot_pattern + self.run_test(1, 1, num_tx, num_streams_per_tx, num_ofdm_symbols, fft_size, + pilot_pattern.mask, pilot_pattern.pilots) + + def test_order_error(self): + + tdl_model = 'A' + subcarrier_spacing = 30e3 # Hz + num_bits_per_symbol = 2 + delay_spread = 300e-9 # s + carrier_frequency = 3.5e9 # Hz + speed = 5. # m/s + los_angle_of_arrival=np.pi/4. + fft_size = 12 + num_rx_ant = 16 + num_tx = 4 + num_streams_per_tx = 1 + num_ofdm_symbols = 14 + pilot_ofdm_symbol_indices = [2,3,8, 11] + rg = ResourceGrid(num_ofdm_symbols=num_ofdm_symbols, + fft_size=fft_size, + subcarrier_spacing=subcarrier_spacing, + num_tx=num_tx, + num_streams_per_tx=num_streams_per_tx, + cyclic_prefix_length=0, + pilot_pattern="kronecker", + pilot_ofdm_symbol_indices=pilot_ofdm_symbol_indices) + pilot_pattern = rg.pilot_pattern + cov_mat_freq = tdl_freq_cov_mat(tdl_model, subcarrier_spacing, fft_size, delay_spread) + cov_mat_time = tdl_time_cov_mat(tdl_model, speed, carrier_frequency, rg.ofdm_symbol_duration, + num_ofdm_symbols, los_angle_of_arrival) + cov_mat_space = exp_corr_mat(0.9, num_rx_ant) + + # Testing random input order + with self.assertRaises(AssertionError): + lmmse_inter_ft = LMMSEInterpolator(pilot_pattern, cov_mat_time, cov_mat_freq, order="hello") + + # Test multiple -- + with self.assertRaises(AssertionError): + lmmse_inter_ft = LMMSEInterpolator(pilot_pattern, cov_mat_time, cov_mat_freq, order="f--t") + + # Test multiple s,f, or t + with self.assertRaises(AssertionError): + lmmse_inter_ft = LMMSEInterpolator(pilot_pattern, cov_mat_time, cov_mat_freq, order="f-f-t") + lmmse_inter_ft = LMMSEInterpolator(pilot_pattern, cov_mat_time, cov_mat_freq, order="f-t-t") + lmmse_inter_ft = LMMSEInterpolator(pilot_pattern, cov_mat_time, cov_mat_freq, cov_mat_space, order="f-s-s-t") + + # Test multiple s,f, or t + with self.assertRaises(AssertionError): + lmmse_inter_ft = LMMSEInterpolator(pilot_pattern, cov_mat_time, cov_mat_freq, order="f-f-t") + lmmse_inter_ft = LMMSEInterpolator(pilot_pattern, cov_mat_time, cov_mat_freq, order="f-t-t") + lmmse_inter_ft = LMMSEInterpolator(pilot_pattern, cov_mat_time, cov_mat_freq, cov_mat_space, order="f-s-s-t") + + # Test no t or no f + with self.assertRaises(AssertionError): + lmmse_inter_ft = LMMSEInterpolator(pilot_pattern, cov_mat_time, cov_mat_freq, cov_mat_space, order="f-s") + lmmse_inter_ft = LMMSEInterpolator(pilot_pattern, cov_mat_time, cov_mat_freq, cov_mat_space, order="s-t") + + # Test s but no spatial covariance matrix + with self.assertRaises(AssertionError): + lmmse_inter_ft = LMMSEInterpolator(pilot_pattern, cov_mat_time, cov_mat_freq, order="f-t-s") + +####################################################### +# Test utilities +####################################################### + +# class TestUtilities(unittest.TestCase): + +# # Batch size for sampling channel models +# BATCH_SIZE = 1000 + +# # Num samples for every monte carlo estimate +# NUM_SAMPLES = 1000000 + +# # Tested subcarrier spacings +# SUBCARRIER_SPACING = (15e3, 30e3, 120e3) # Hz + +# # Tested delay spreads +# DELAY_SPREAD = (100e-9, 300e-9, 1000e-9) # s + +# # Tested FFT sizes +# FFT_SIZE = 1024 + +# # TDL models +# TDL_MODELS = ('A', 'B', 'C', 'D', 'E') + +# # Tested speeds +# SPEEDS = (0.0, 10.0, 100.) + +# # Tested number of OFDM symbols +# NUM_SYMBOLS = 140 + +# # Tested carrier frequencies +# CARRIER_FREQS = (0.450e6, 3.5e9, 6.0e9) + +# # Absolute error tolerance +# ATOL = 1e-2 + +# def est_tdl_freq_cov_mat(self, num_samples, model, delay_spread, carrier_frequency, +# subcarrier_spacing, ofdm_symbol_duration, fft_size): + +# tf.random.set_seed(42) + +# channel_model = TDL(model, delay_spread, carrier_frequency) +# frequencies = subcarrier_frequencies(fft_size, subcarrier_spacing) + +# batch_size = TestUtilities.BATCH_SIZE +# num_it = (num_samples//batch_size) + 1 +# hs = [] + +# @tf.function(jit_compile=True) +# def _run(): +# cov_mat = tf.zeros([fft_size, fft_size], tf.complex64) +# for _ in tf.range(num_it): +# a, tau = channel_model(batch_size, 1, +# sampling_frequency=1./ofdm_symbol_duration) +# h = cir_to_ofdm_channel(frequencies, a, tau)[:,0,0,0,0] # [batch size, 1, fft size] +# h = tf.transpose(h, [0,2,1]) # [batch size, fft size, 1] +# cov_mat_ = tf.matmul(h, h, adjoint_b=True) +# cov_mat_ = tf.reduce_mean(cov_mat_, axis=0) +# cov_mat += cov_mat_ +# cov_mat = cov_mat / tf.cast(num_it,tf.complex64) +# return cov_mat + +# cov_mat = _run().numpy() +# return cov_mat + +# def test_tdl_freq_cov_mat(self): + +# fft_size = TestUtilities.FFT_SIZE + +# parameters = itertools.product(TestUtilities.TDL_MODELS, +# TestUtilities.SUBCARRIER_SPACING, +# TestUtilities.DELAY_SPREAD) +# for p in parameters: +# model = p[0] # Model +# scs = p[1] # subcarrier spacing +# ds = p[2] # delay spread +# # Empirical covariance +# cov_mat_emp = self.est_tdl_freq_cov_mat(TestUtilities.NUM_SAMPLES, model, ds, 3.5e9, +# scs, 1.0, fft_size) +# # Expected covariance +# cov_mat = tdl_freq_cov_mat(model, scs,fft_size, ds) +# cov_mat = cov_mat.numpy() +# # Error +# max_err = np.max(np.abs(cov_mat - cov_mat_emp)) +# self.assertTrue(max_err < TestUtilities.ATOL) + +# def est_tdl_time_cov_mat(self, num_samples, model, carrier_frequency, +# subcarrier_spacing, speed, num_ofdm_symbols, los_angle_of_arrival): + +# tf.random.set_seed(42) + +# channel_model = TDL(model, 300e-9, carrier_frequency, min_speed=speed, max_speed=speed, +# los_angle_of_arrival=los_angle_of_arrival) +# frequencies = subcarrier_frequencies(1, subcarrier_spacing) + +# batch_size = TestUtilities.BATCH_SIZE +# num_it = (num_samples//batch_size) + 1 +# hs = [] + +# @tf.function(jit_compile=True) +# def _run(): +# cov_mat = tf.zeros([num_ofdm_symbols, num_ofdm_symbols], tf.complex64) +# for _ in tf.range(num_it): +# a, tau = channel_model(batch_size, num_ofdm_symbols, +# sampling_frequency=subcarrier_spacing) +# h = cir_to_ofdm_channel(frequencies, a, tau)[:,0,0,0,0] # [batch size, num_ofdm_symbols, 1] +# cov_mat_ = tf.matmul(h, h, adjoint_b=True) +# cov_mat_ = tf.reduce_mean(cov_mat_, axis=0) +# cov_mat += cov_mat_ +# cov_mat = cov_mat / tf.cast(num_it,tf.complex64) +# return cov_mat + +# cov_mat = _run().numpy() +# return cov_mat + +# def test_tdl_time_cov_mat(self): + +# num_ofdm_symbols = TestUtilities.NUM_SYMBOLS +# los_angle_of_arrival = np.pi/4. + +# parameters = itertools.product(TestUtilities.TDL_MODELS, +# TestUtilities.SPEEDS, +# TestUtilities.SUBCARRIER_SPACING, +# TestUtilities.CARRIER_FREQS) +# for p in parameters: +# model = p[0] # Model +# speed = p[1] # Speed +# subcarrier_spacing = p[2] # Subcarrier spacing +# carr_freq = p[3] # Carrier frequency +# # Empirical covariance +# cov_mat_emp = self.est_tdl_time_cov_mat(TestUtilities.NUM_SAMPLES, model, carr_freq, +# subcarrier_spacing, speed, num_ofdm_symbols, los_angle_of_arrival) +# # Expected covariance +# cov_mat = tdl_time_cov_mat(model, speed, carr_freq, 1./subcarrier_spacing, +# num_ofdm_symbols, los_angle_of_arrival) +# cov_mat = cov_mat.numpy() +# # # Error +# max_err = np.max(np.abs(cov_mat - cov_mat_emp)) +# self.assertTrue(max_err < TestUtilities.ATOL)