Using pyAerial to run a PUSCH link simulation

            

            
[1]:
        

            

            
%matplotlib widget
import datetime
from collections import defaultdict
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
os.environ['TF_CPP_MIN_LOG_LEVEL'] = "3"  # Silence TensorFlow.

import numpy as np
import matplotlib.pyplot as plt
import sionna
import tensorflow as tf

from aerial.phy5g.pdsch import PdschTx
from aerial.phy5g.pusch import PuschRx
from aerial.phy5g.algorithms import ChannelEstimator
from aerial.phy5g.algorithms import ChannelEqualizer
from aerial.phy5g.algorithms import NoiseIntfEstimator
from aerial.phy5g.algorithms import Demapper
from aerial.phy5g.ldpc import get_mcs
from aerial.phy5g.ldpc import random_tb
from aerial.phy5g.ldpc import LdpcDeRateMatch
from aerial.phy5g.ldpc import LdpcDecoder
from aerial.phy5g.ldpc import code_block_desegment
from aerial.phy5g.types import PuschLdpcKernelLaunch
from aerial.util.cuda import get_cuda_stream

# Configure the notebook to use only a single GPU and allocate only as much memory as needed.
# For more details, see https://www.tensorflow.org/guide/gpu.
gpus = tf.config.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(gpus[0], True)
        

            

            
[2]:
        

            

            
# Simulation parameters.
esno_db_range = np.arange(-5.4, -4.4, 0.2)
num_slots = 10000
min_num_tb_errors = 250

# Numerology and frame structure. See TS 38.211.
num_ofdm_symbols = 14
fft_size = 4096
cyclic_prefix_length = 288
subcarrier_spacing = 30e3
num_guard_subcarriers = (410, 410)
num_slots_per_frame = 20

num_tx_ant = 1             # UE antennas
num_rx_ant = 2             # gNB antennas
cell_id = 41               # Physical cell ID

rnti = 1234                # UE RNTI
scid = 0                   # DMRS scrambling ID
data_scid = 0              # Data scrambling ID
layers = 1                 # Number of layers
mcs = 1                    # MCS index as per TS 38.214 table
dmrs_port = 1              # Used DMRS port.
start_prb = 0              # Start PRB index.
num_prbs = 273             # Number of allocated PRBs.
start_sym = 2              # Start symbol index.
num_symbols = 12           # Number of symbols.
dmrs_scrm_id = 41          # DMRS scrambling ID
dmrs_position = [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]  # Indicates which symbols are used for DMRS.
dmrs_max_len=1
dmrs_add_ln_pos=0
num_dmrs_cdm_grps_no_data = 2
enable_pusch_tdi = 0       # Enable time interpolation for equalizer coefficients
eq_coeff_algo = 1          # Equalizer algorithm

# Channel parameters
carrier_frequency = 3.5e9  # Carrier frequency in Hz.
delay_spread = 100e-9      # Nominal delay spread in [s]. Please see the CDL documentation
                           # about how to choose this value.
link_direction = "uplink"
channel_model = "Rayleigh" # Channel model: Suitable values:
                           # "Rayleigh" - Rayleigh block fading channel model (sionna.channel.RayleighBlockFading)
                           # "CDL-x", where x is one of ["A", "B", "C", "D", "E"] - for 3GPP CDL channel models
                           # as per TR 38.901.
speed = 0.8333             # UE speed [m/s]. The direction of travel will chosen randomly within the x-y plane.
        

            

            
[3]:
        

            

            
pusch_tx = PdschTx(
    cell_id=cell_id,
    num_rx_ant=num_tx_ant,
    num_tx_ant=num_tx_ant,
)

# This is the fully fused PUSCH receiver chain.
pusch_rx = PuschRx(
    cell_id=cell_id,
    num_rx_ant=num_rx_ant,
    num_tx_ant=num_rx_ant,
    enable_pusch_tdi=enable_pusch_tdi,
    eq_coeff_algo=eq_coeff_algo,
    # To make this equal separate PUSCH Rx components configuration:
    ldpc_kernel_launch=PuschLdpcKernelLaunch.PUSCH_RX_LDPC_STREAM_SEQUENTIAL
)

# The PUSCH receiver chain built from separately called pyAerial Python components is defined here.
class PuschRxSeparate:
    """PUSCH receiver class.

This class encapsulates the whole PUSCH receiver chain built using
pyAerial components.
"""

    def __init__(self,
                 num_rx_ant,
                 enable_pusch_tdi,
                 eq_coeff_algo):
        """Initialize the PUSCH receiver."""
        self.cuda_stream = get_cuda_stream()

        # Build the components of the receiver.
        self.channel_estimator = ChannelEstimator(
            num_rx_ant=num_rx_ant,
            cuda_stream=self.cuda_stream
        )
        self.channel_equalizer = ChannelEqualizer(
            num_rx_ant=num_rx_ant,
            enable_pusch_tdi=enable_pusch_tdi,
            eq_coeff_algo=eq_coeff_algo,
            cuda_stream=self.cuda_stream
        )
        self.noise_intf_estimator = NoiseIntfEstimator(
            num_rx_ant=num_rx_ant,
            eq_coeff_algo=eq_coeff_algo,
            cuda_stream=self.cuda_stream
        )
        self.derate_match = LdpcDeRateMatch(
            enable_scrambling=True,
            cuda_stream=self.cuda_stream
        )
        self.decoder = LdpcDecoder(cuda_stream=self.cuda_stream)

    def run(
        self,
        rx_slot,
        num_ues,
        slot,
        num_dmrs_cdm_grps_no_data,
        dmrs_scrm_id,
        start_prb,
        num_prbs,
        dmrs_syms,
        dmrs_max_len,
        dmrs_add_ln_pos,
        start_sym,
        num_symbols,
        scids,
        layers,
        dmrs_ports,
        rntis,
        data_scids,
        code_rates,
        mod_orders,
        tb_sizes
    ):
        """Run the receiver."""
        # Channel estimation.
        ch_est = self.channel_estimator.estimate(
            rx_slot=rx_slot,
            num_ues=num_ues,
            slot=slot,
            num_dmrs_cdm_grps_no_data=num_dmrs_cdm_grps_no_data,
            dmrs_scrm_id=dmrs_scrm_id,
            start_prb=start_prb,
            num_prbs=num_prbs,
            dmrs_syms=dmrs_syms,
            dmrs_max_len=dmrs_max_len,
            dmrs_add_ln_pos=dmrs_add_ln_pos,
            start_sym=start_sym,
            num_symbols=num_symbols,
            scids=scids,
            layers=layers,
            dmrs_ports=dmrs_ports
        )

        # Noise and interference estimation.
        lw_inv, noise_var_pre_eq = self.noise_intf_estimator.estimate(
            rx_slot=rx_slot,
            channel_est=ch_est,
            num_ues=num_ues,
            slot=slot,
            num_dmrs_cdm_grps_no_data=num_dmrs_cdm_grps_no_data,
            dmrs_scrm_id=dmrs_scrm_id,
            start_prb=start_prb,
            num_prbs=num_prbs,
            dmrs_syms=dmrs_syms,
            dmrs_max_len=dmrs_max_len,
            dmrs_add_ln_pos=dmrs_add_ln_pos,
            start_sym=start_sym,
            num_symbols=num_symbols,
            scids=scids,
            layers=layers,
            dmrs_ports=dmrs_ports
        )

        # Channel equalization and soft demapping. The first return value are the LLRs,
        # second are the equalized symbols. We only want the LLRs now.
        llr = self.channel_equalizer.equalize(
            rx_slot=rx_slot,
            channel_est=ch_est,
            lw_inv=lw_inv,
            noise_var_pre_eq=noise_var_pre_eq,
            num_ues=num_ues,
            num_dmrs_cdm_grps_no_data=num_dmrs_cdm_grps_no_data,
            start_prb=start_prb,
            num_prbs=num_prbs,
            dmrs_syms=dmrs_syms,
            dmrs_max_len=dmrs_max_len,
            dmrs_add_ln_pos=dmrs_add_ln_pos,
            start_sym=start_sym,
            num_symbols=num_symbols,
            layers=layers,
            mod_orders=mod_orders
        )[0]

        decoded_tbs = []
        num_data_sym = (np.array(dmrs_syms[start_sym:start_sym + num_symbols]) == 0).sum()
        tot_layers = 0
        for ue_idx in range(num_ues):
            ue_layers = range(tot_layers, tot_layers + layers[ue_idx])
            tot_layers += layers[ue_idx]
            # De-rate matching and descrambling.
            cinit = (rntis[ue_idx] << 15) + data_scids[ue_idx]
            rate_match_len = num_data_sym * mod_orders[ue_idx] * num_prbs * 12 * layers[ue_idx]
            coded_blocks = self.derate_match.derate_match(
                input_data=llr[0][:, ue_layers, ...],
                tb_size=tb_sizes[ue_idx] * 8,
                code_rate=code_rates[ue_idx] / 1024.,
                rate_match_len=rate_match_len,
                mod_order=mod_orders[ue_idx],
                num_layers=layers[ue_idx],
                redundancy_version=0,
                ndi=1,
                cinit=cinit
            )

            # LDPC decoding of the derate matched blocks.
            code_blocks = self.decoder.decode(
                input_llr=coded_blocks,
                tb_size=tb_sizes[ue_idx] * 8,
                code_rate=code_rates[ue_idx] / 1024.,
                redundancy_version=0,
                rate_match_len=rate_match_len
            )

            # Combine the code blocks into a transport block.
            tb = code_block_desegment(
                code_blocks=code_blocks,
                tb_size=tb_sizes[ue_idx] * 8,
                code_rate=code_rates[ue_idx] / 1024.,
                return_bits=False,
            )

            # Remove CRC - no checking, check TBs/bits directly.
            tb = tb[:-3]
            decoded_tbs.append(tb)

        return decoded_tbs

pusch_rx_separate = PuschRxSeparate(
    num_rx_ant=num_rx_ant,
    enable_pusch_tdi=enable_pusch_tdi,
    eq_coeff_algo=eq_coeff_algo
)
        

            

            
[4]:
        

            

            
# Define the resource grid.
resource_grid = sionna.ofdm.ResourceGrid(
    num_ofdm_symbols=num_ofdm_symbols,
    fft_size=fft_size,
    subcarrier_spacing=subcarrier_spacing,
    num_tx=1,
    num_streams_per_tx=1,
    cyclic_prefix_length=cyclic_prefix_length,
    num_guard_carriers=num_guard_subcarriers,
    dc_null=False,
    pilot_pattern=None,
    pilot_ofdm_symbol_indices=None
)
resource_grid_mapper = sionna.ofdm.ResourceGridMapper(resource_grid)
remove_guard_subcarriers = sionna.ofdm.RemoveNulledSubcarriers(resource_grid)

# Define the antenna arrays.
ue_array = sionna.channel.tr38901.Antenna(
    polarization="single",
    polarization_type="V",
    antenna_pattern="38.901",
    carrier_frequency=carrier_frequency
)
gnb_array = sionna.channel.tr38901.AntennaArray(
    num_rows=1,
    num_cols=int(num_rx_ant/2),
    polarization="dual",
    polarization_type="cross",
    antenna_pattern="38.901",
    carrier_frequency=carrier_frequency
)

if channel_model == "Rayleigh":
    ch_model = sionna.channel.RayleighBlockFading(
        num_rx=1,
        num_rx_ant=num_rx_ant,
        num_tx=1,
        num_tx_ant=num_tx_ant
    )

elif "CDL" in channel_model:
    cdl_model = channel_model[-1]

    # Configure a channel impulse reponse (CIR) generator for the CDL model.
    ch_model = sionna.channel.tr38901.CDL(
        cdl_model,
        delay_spread,
        carrier_frequency,
        ue_array,
        gnb_array,
        link_direction,
        min_speed=speed
    )
else:
    raise ValueError(f"Invalid channel model{channel_model}!")

channel = sionna.channel.OFDMChannel(
    ch_model,
    resource_grid,
    add_awgn=True,
    normalize_channel=True,
    return_channel=False
)

def apply_channel(tx_tensor, No):
    """Transmit the Tx tensor through the radio channel."""
    # Add batch and num_tx dimensions that Sionna expects and reshape.
    tx_tensor = tf.transpose(tx_tensor, (2, 1, 0))
    tx_tensor = tf.reshape(tx_tensor, (1, -1))[None, None]
    tx_tensor = resource_grid_mapper(tx_tensor)
    rx_tensor = channel((tx_tensor, No))
    rx_tensor = remove_guard_subcarriers(rx_tensor)
    rx_tensor = rx_tensor[0, 0]
    rx_tensor = tf.transpose(rx_tensor, (2, 1, 0))
    return rx_tensor
        

            

            
[5]:
        

            

            
class SimulationMonitor:
    """Helper class to show the progress and results of the simulation."""

    markers = ["d", "o", "s"]
    linestyles = ["-", "--", ":"]
    colors = ["blue", "black", "red"]

    def __init__(self, cases, esno_db_range):
        """Initialize the SimulationMonitor.

Initialize the figure and the results table.
"""
        self.cases = cases
        self.esno_db_range = esno_db_range
        self.current_esno_db_range = []

        self.start_time = None
        self.esno_db = None
        self.bler = defaultdict(list)

        self._print_headers()

    def step(self, esno_db):
        """Start next Es/No value."""
        self.start_time = datetime.datetime.now()
        self.esno_db = esno_db
        self.current_esno_db_range.append(esno_db)

    def update(self, num_tbs, num_tb_errors):
        """Update current state for the current Es/No value."""
        self._print_status(num_tbs, num_tb_errors, False)

    def _print_headers(self):
        """Print result table headers."""
        cases_str = " " * 21
        separator = " " * 21
        for case in self.cases:
            cases_str += case.center(20) + " "
            separator += "-" * 20 + " "
        print(cases_str)
        print(separator)
        title_str = "Es/No (dB)".rjust(12) + "TBs".rjust(8) + " "
        for case in self.cases:
            title_str += "TB Errors".rjust(12) + "BLER".rjust(8) + " "
        title_str += "ms/TB".rjust(8)
        print(title_str)
        print(("=" * 20) + " " + ("=" * 20 + " ") * len(self.cases) + "=" * 8)

    def _print_status(self, num_tbs, num_tb_errors, finish):
        """Print simulation status in a table."""
        end_time = datetime.datetime.now()
        t_delta = end_time - self.start_time

        if finish:
            newline_char = '\n'
        else:
            newline_char = '\r'
        result_str = f"{self.esno_db:9.2f}".rjust(12) + f"{num_tbs:8d}".rjust(8) + " "
        for case in self.cases:
            result_str += f"{num_tb_errors[case]:8d}".rjust(12)
            result_str += f"{(num_tb_errors[case] / num_tbs):.4f}".rjust(8) + " "
        result_str += f"{(t_delta.total_seconds() * 1000 / num_tbs):6.1f}".rjust(8)
        print(result_str, end=newline_char)

    def finish_step(self, num_tbs, num_tb_errors):
        """Finish simulating the current Es/No value and add the result in the plot."""
        self._print_status(num_tbs, num_tb_errors, True)
        for case_idx, case in enumerate(self.cases):
            self.bler[case].append(num_tb_errors[case] / num_tbs)

    def finish(self):
        """Finish simulation and plot the results."""
        self.fig = plt.figure()
        for case_idx, case in enumerate(self.cases):
            plt.plot(
                self.current_esno_db_range,
                self.bler[case],
                marker=SimulationMonitor.markers[case_idx],
                linestyle=SimulationMonitor.linestyles[case_idx],
                color=SimulationMonitor.colors[case_idx],
                markersize=8,
                label=case
            )
        plt.yscale('log')
        plt.ylim(0.001, 1)
        plt.xlim(np.min(self.esno_db_range), np.max(self.esno_db_range))
        plt.title("Receiver BLER Performance vs. Es/No")
        plt.ylabel("BLER")
        plt.xlabel("Es/No [dB]")
        plt.grid()
        plt.legend()
        plt.show()
        

            

            
[6]:
        

            

            
cases = ["Fused", "Separate"]
monitor = SimulationMonitor(cases, esno_db_range)

# Loop the Es/No range.
bler = []
for esno_db in esno_db_range:
    monitor.step(esno_db)
    num_tb_errors = defaultdict(int)

    # Run multiple slots and compute BLER.
    for slot_idx in range(num_slots):
        slot_number = slot_idx % num_slots_per_frame

        # Get modulation order and coderate.
        mod_order, coderate = get_mcs(mcs)
        tb_input = random_tb(mod_order, coderate, dmrs_position, num_prbs, start_sym, num_symbols, layers)

        # Transmit PUSCH. This is where we set the dynamically changing parameters.
        # Input parameters are given as lists as the interface supports multiple UEs.
        tx_tensor = pusch_tx.run(
            tb_inputs=[tb_input],          # Input transport block in bytes.
            num_ues=1,                     # We simulate only one UE here.
            slot=slot_number,              # Slot number.
            num_dmrs_cdm_grps_no_data=num_dmrs_cdm_grps_no_data,
            dmrs_scrm_id=dmrs_scrm_id,     # DMRS scrambling ID.
            start_prb=start_prb,           # Start PRB index.
            num_prbs=num_prbs,             # Number of allocated PRBs.
            dmrs_syms=dmrs_position,       # List of binary numbers indicating which symbols are DMRS.
            start_sym=start_sym,           # Start symbol index.
            num_symbols=num_symbols,       # Number of symbols.
            scids=[scid],                  # DMRS scrambling ID.
            layers=[layers],               # Number of layers (transmission rank).
            dmrs_ports=[dmrs_port],        # DMRS port(s) to be used.
            rntis=[rnti],                  # UE RNTI.
            data_scids=[data_scid],        # Data scrambling ID.
            code_rates=[coderate],         # Code rate x 1024.
            mod_orders=[mod_order]         # Modulation order.
        )[0]

        # Channel transmission using TF and Sionna.
        No = pow(10., -esno_db / 10.)
        rx_tensor = apply_channel(tx_tensor, No)
        rx_tensor = np.array(rx_tensor)

        # Run the fused PUSCH receiver.
        # Note that this is where we set the dynamically changing parameters.
        tb_crcs, tbs = pusch_rx.run(
            rx_slot=rx_tensor,
            num_ues=1,
            slot=slot_number,
            num_dmrs_cdm_grps_no_data=num_dmrs_cdm_grps_no_data,
            dmrs_scrm_id=dmrs_scrm_id,
            start_prb=start_prb,
            num_prbs=num_prbs,
            dmrs_syms=dmrs_position,
            dmrs_max_len=dmrs_max_len,
            dmrs_add_ln_pos=dmrs_add_ln_pos,
            start_sym=start_sym,
            num_symbols=num_symbols,
            scids=[scid],
            layers=[layers],
            dmrs_ports=[dmrs_port],
            rntis=[rnti],
            data_scids=[data_scid],
            code_rates=[coderate],
            mod_orders=[mod_order],
            tb_sizes=[len(tb_input)]
        )
        num_tb_errors["Fused"] += int(np.array_equal(tbs[0][:-3], tb_input) == False)

        # Run the receiver built from separately called components.
        tbs = pusch_rx_separate.run(
            rx_slot=rx_tensor,
            num_ues=1,
            slot=slot_number,
            num_dmrs_cdm_grps_no_data=num_dmrs_cdm_grps_no_data,
            dmrs_scrm_id=dmrs_scrm_id,
            start_prb=start_prb,
            num_prbs=num_prbs,
            dmrs_syms=dmrs_position,
            dmrs_max_len=dmrs_max_len,
            dmrs_add_ln_pos=dmrs_add_ln_pos,
            start_sym=start_sym,
            num_symbols=num_symbols,
            scids=[scid],
            layers=[layers],
            dmrs_ports=[dmrs_port],
            rntis=[rnti],
            data_scids=[data_scid],
            code_rates=[coderate],
            mod_orders=[mod_order],
            tb_sizes=[len(tb_input)]
        )
        num_tb_errors["Separate"] += int(np.array_equal(tbs[0], tb_input) == False)

        monitor.update(num_tbs=slot_idx + 1, num_tb_errors=num_tb_errors)
        if (np.array(list(num_tb_errors.values())) >= min_num_tb_errors).all():
            break  # Next Es/No value.

    monitor.finish_step(num_tbs=slot_idx + 1, num_tb_errors=num_tb_errors)
monitor.finish()