v0.12.1

Fixes

• Issue with CRCEncoder and int_mod_2  function for large input tensors
• (#76) Issue related to step noise calculation in SSFM
• (#75) Issue related to llr_max of LDPC decoder and adds new attribute
• Issue in the tests for KBest
• Updates documentation and fixes typos

Misc

• Add support for TensorFlow 2.11
• Version adjustments in requirements

v0.12.0

MIMO

• Add MMSEPICDetector layer for MMSE-PIC detection, contribution from rwiesmayr
• Add EPDetector layer for expectation propagation (EP) detection
• Add KBestDetector for K-Best detection
• Utility layer PAM2QAM now also supports soft information

FEC

• Add OSDDecoder layer for ordered statistics decoding (OSD)
• Add submodule fec.linear for universal support of Linear Codes
• Add support for max-log BCJR decoding to the convolutional and Turbo decoders
• Align Turbo and convolutional encoders
• Add support for code termination to convolutional codes
• Add input LLR clipping to LDPC BP decoding

OFDM

• Add base class BaseChannelEstimator to ease the implementation of OFDM channel estimators
• Add base class BaseChannelInterpolator to ease the implementation of OFDM channel interpolators
• Add LMMSEInterpolator for LMMSE channel time and frequency interpolation and optional spatial smoothing
• Add utility functions to compute time and frequency covariance matrices of TDL models
• Add base class BaseEqualizer to ease implementation of OFDM MIMO equalizers
• Add base classes BaseDetector and BaseDetectorWithPrior to ease the implementation of OFDM MIMO detectors without and with prior information
• Add LinearDetector layer for LMMSE, MR, or ZF equalization followed by app or max-log demapping for OFDM MIMO detection
• Add KBestDetector for K-Best-based OFDM MIMO detection
• Add EPDetector for EP-based OFDM MIMO detection
• Add MMSEPICDetector for MMSE-PIC-based OFDM MIMO detection
• Add a setter to PilotPattern to support one-the-fly update of pilot symbols

Channel

• Add MIMO support to TDL channel models through the specification of arbitrary correlation matrices
• Add support for TDL A30, B100, and C300 channel models
• Fix base station orientation in gen_topology_* utilities

Tutorials

• Add a notebook showing how to use and compare some of the OFDM channel estimators and MIMO detectors available in Sionna

MISC

• Typos fix and new test cases
• Drop support for TensorFlow 2.6

v0.11.0

Features

• Support for optical fiber simulations

  • Split step Fourier method (SSFM)
  • Erbium doped fiber amplifier
  • New tutorial notebook on optical fiber simulations
  • Refactorization of the channel documentation

• BCJR decoder for convolutional codes

• Support for Turbo codes

  • Turbo encoding layer
  • Turbo decoding layer
  • New interleaver layer supporting the LTE Turbo code interleaver patterns (cf. 36.212)
  • Utility functions for code generation and for puncturing

• Improved scrambling layer

  • Support for custom scrambling sequences
  • Utility function to support scrambling sequences as defined in for 38.211

• Adds LDPC rate-matching output interleaver to LDPC5GEncoder and LDPC5GDecoder

• Utility function to generate random regular LDPC parity-check matrices

• Zero-forcing (ZF) and matched filter (MF) MIMO equalizers

• Tensor utility function for pseudo inverse computation

• Several MIMO utility functions for channel whitening and complex-to-real valued representations

• (OFDM) MIMO maximum likelihood detector with prior

• Utility layer for computing logits on constellation points from LLRs

• New tutorial on using the DeepMIMO dataset with Sionna

• Restructured website

  • Made with Sionna section
  • Direct links to Github discussion/issues

Breaking Changes

• In LDPCBPDecoder and LDPC5GDecoder, keep_state has been replaced by stateful flag and the decoder now returns the states instead of keeping an internal reference
• TDL and CDL power delay profiles are now normalized to have unit total power (related to issue #12).

Fixes

• (#38) Fixes issue with PanelArray()
• (#43) Fixes issue with MLDetector
• Adds support for TensorFlow 2.10
• Bug fix in SymbolLogits2Moments
• Bug fix in mapping module related to double precision
• Adds missing reference of generate_dense_polar in sionna.fec.polar.__init__
• Improves stability of graph mode in Polar SCL decoding
• Issue of LDPC decoding when graph is not pruned
• Fixes missing attribute in Linear Encoder
• Improved selection of polynomials for convolutional codes
• Removes deprecated items in pylintrc due to updated version of Lynter
• Typos in the documentation
• Corrects the LoS path power calculation oft the 3GPP 38.901 channel models
• Fixes a bug in the effective noise variance computation of the lmmse_equalizer

v0.10.0

New features

• Universal FEC encoder for binary linear codes
• Utility functions for FEC to
  • Convert parity-check matrix to generator matrix
  • Make generator matrix systematic
  • Generate dense Polar parity-check and generator matrices
  • Verify that generator and parity check matrix are orthogonal in GF(2)
• Linear interpolator for OFDM channel estimation (with optional time-averaging)
• MIMO maximum likelihood detector that computes either LLRs on bits or logits on constellation points
• SymbolSource layer for sampling arbitrary constellations
• PAMSource layer for sampling PAM constellations
• The SymbolSource, PAMSource, QAMSource, and Mapper optionally return the indices of the constellation points and/or the bit labels
• Utility layer to compute logits on constellation points to LLRs on bits (with optional priors)
• Utility layer to compute the mean and variance of a constellation given logits on the points
• Addition of integration tests

Fixes

• (#25) XLA compatibility with TF2.9 for the BP decoder
• (#29) SNR property in PlotBER
• Adds support for TF2.9
• Minor bug fixes
• Typos in the documentation

v0.9.2

Fixes

• Drop support for TensorFlow 2.5 as it no longer receives updates
• Update minimum TensorFlow version requirements to address recent CVEs

v0.9.1

Fixes

• Solves deprecated warning in utils.metrics (#14)
• Issue related to explicit interleaver seed in graph mode
• Issues related to RessourceGridDemapper (#19)
• Output shape of signal.utils.convolution()
• Corrects year of Gallager quote in channel coding example notebook
• Corrects Eb/No rate calculation in channel coding example notebook
• Typos in documentation
• Solves issue related to PanelArray.show() (#23)
• Optimization of signal.utils.convolution() for complex-valued inputs

v0.9.0

Features

New Signal Module

• Layers for up-/downsampling
• Layers for  (trainable) filters (e.g., pulse shaping)
• Layers for  (trainable) windowing functions
• Utility functions for convolution, (I)FFT, PSD, and ACLR calculation
• New example notebook on pulse-shaping

Fixes

• OFDMModulator issue
• Removes several warnings during unittests

v0.8.1

Fixes

• OFDMDemodulator() issue for a cyclic_prefix equal to zero (closes github #6)
• Fixes issue with Jupyter notebook requesting a token
• Improve installation on macOS (closes github #2)
• Other small bug fixes and enhancements

v0.8.0

Features

Forward error correction (FEC)

• 5G LDPC codes including rate matching
• 5G Polar codes including rate matching
• Cyclic redundancy check (CRC)
• Reed-Muller & Convolutional codes
• Interleaving & Scrambling
• Belief propagation (BP) decoder and variants
• SC, SCL, and SCL-CRC Polar decoders
• Viterbi decoder
• Demapper with prior
• EXIT chart simulations
• Import of partity-check matrices in alist format

Channel models

• Additive white Gaussian noise (AWGN) channel
• Flat-fading channel models with antenna correlation
• 3GPP 38.901 TDL, CDL, UMa, UMi, RMa models
• Import of channel impulse response from datasets
• Channel output computed in time or frequency domain

MIMO processing

• Multiuser & multicell MIMO support
• 3GPP 38.901 & custom antenna arrays/patterns
• Zero forcing (ZF) precoding
• Minimum mean squared error (MMSE) equalization

Orthogonal frequency-division multiplexing (OFDM)

• OFDM modulation & demodulation
• Cyclic prefix insertion & removal
• Flexible 5G slot-like frame structure
• Arbitrary pilot patterns
• LS channel estimation & Nearest neighbor interpolation