nrx_rt_var_mcs.cfg

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# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
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[global]
label = 'nrx_rt_var_mcs' # all relevant files such as weights will use this label
ebno = True # activate rate-adjusted SNR

[system]
n_size_bwp = 4
num_rx_antennas = 4
mcs_index = [9, 14]     # QPSK and 16-QAM
mcs_table = 1
carrier_frequency = 2140000000.0
subcarrier_spacing = 30000.0
n_start_grid = 0
slot_number = 0
frame_number = 0
cyclic_prefix = 'normal'
precoding = 'codebook'
n_cell_id = 1
tpmi = 2
symbol_allocation = [0, 14]
num_antenna_ports = 2
dmrs_mapping_type = "A"
dmrs_config_type = 1
dmrs_type_a_position = 2
dmrs_additional_position = 1
dmrs_length = 1
dmrs_nid = [[1, 1], [1, 1]]
n_scid = 1
num_cdm_groups_without_data = 2
verbose = False
dmrs_port_sets = [[0], [2]]
n_rntis = [1, 1]
n_ids = [1, 1]

[baseline]
demapping_type = 'maxlog'
num_bp_iter = 20
cn_type = 'boxplus'
# For large num_prbs (>100), a low reduced complexity
# LMMSE estimator is used where LMMSE is only performed over the
# lmmse_num_prbs PRBS
# if set to -1, the splitting parameters are calculated via an heursitic
# the target of the heuristic is to find the best split such that
# the resulting LMMSE is done over at least 20 PRBs
lmmse_num_prbs = -1 # n_size_bwp must be multiple of this constant

[neural_receiver]
num_nrx_iter = 2 # defines number of cgnn_it stages
num_nrx_iter_eval = 2 # iterations used for evaluation; must be <= num_nrx_iter
d_s = 56 # feature space dimensions
num_units_init = [128, 128] # num filter kernels for input CNN (each list entry defines one layer)
num_units_agg = [[64],[64]] # number of neurons of state aggregation MLP (each list entry defines one layer)
num_units_state = [[128, 128], [128, 128]] # num filter kernels for stage update CNN (each list entry defines one layer)
num_units_readout = [128]  # number of neurons of state aggregation MLP (each list entry defines one layer)
max_num_tx = 2 # max number of active DMRS ports
min_num_tx = 1 # only relevant during training for random user sampling
initial_chest = "ls" # "None" deactivates initial LS estimation
custom_constellation = False # activates trainable transmitter
mask_pilots = False # mask DMRS positions for e2e experiments

# quantization and other custom layer types
layer_type_dense = "dense"
layer_type_conv = "sepconv" # or "conv"
layer_type_readout = "dense"
nrx_dtype = tf.float32

[training]
# Each parameter in the training schedule is defined as list
# The training loops over these parameters, i.e., performs num_iter[i]
# SGD iterations for the ith set of parameters
training_schedule = {
    "num_iter": [1e5, 2e6, 7e6],
    "learning_rate": [0.0005, 0.001, 0.0005],
    "batch_size": [128, 128, 128],
    "train_tx": [False, False, False],
    "min_training_snr_db": [[-2.,-2.], [-2., -2.], [-2., 0.0]], # 1 / 2 active UEs, is Eb/No [dB] if ebno==True
    "max_training_snr_db": [[12.,14.], [8., 10.], [3., 5.]], # 1 / 2 active UEs, is Eb/No [dB] if ebno==True
    "double_readout": [True, True, True], # use additional MSE loss on h_hat
    "weighting_double_readout": [0.02, 0.02, 0.01], # weighting between MSE & BCE loss
    "apply_multiloss": [False, False, False]}

mcs_training_snr_db_offset = [[0.0, 4.0], [0.0, 2.]]    # first list is for 1 UE, second for 2 UEs

num_iter_train_save = 1000
max_ut_velocity = 56.
min_ut_velocity = 0.
channel_norm = False
cfo_offset_ppm = 0.0 # randomly sampled in [-cfo_offset_ppm, cfo_offset_ppm]
# UMi
channel_type = 'UMi'
eval_ebno_db_arr = [1.0, 4.0]  # EbNo to evaluate model for each MCS during training every 1k iterations
# TDL (for two UEs)
#channel_type = 'DoubleTDLlow'
xla = True # Activate XLA for the training loop
tfrecord_filename = "na" # only relevant if training is done with a dataset
double_readout = True

[evaluation]
# the following parameters are used during evaluation
snr_db_eval_min = -3
snr_db_eval_max = 8
snr_db_eval_stepsize = 1
max_ut_velocity_eval = 56
min_ut_velocity_eval = 56
cfo_offset_ppm_eval = 0.0
tfrecord_filename_eval = "na"
channel_type_eval = "DoubleTDLlow" # 2 Users
#channel_type_eval = 'TDL-B100' # 1 User
channel_norm_eval = False
n_size_bwp_eval = 132
batch_size_eval = 30
batch_size_eval_small = 3 # for k-best-based baselines