使用 pyAerial 通过仿真生成数据#
此笔记本使用 NVIDIA cuPHY，通过其在 pyAerial 中的 Python 绑定，为 PUSCH/PDSCH 插槽生成，并使用 NVIDIA Sionna 进行无线信道建模，生成完全符合 5G NR 标准的 PUSCH/PDSCH 数据集。PUSCH/PDSCH 插槽通过不同的无线信道生成和传输。通常，为了使模型尽可能具有通用性，最好使用尽可能多种不同的信道模型来训练模型。此笔记本能够生成包含使用多种不同信道模型（包括例如 3GPP 使用的信道模型）以及不同的 MCS 类和其他传输参数生成的样本的数据集。
[1]:
# Check platform.
import platform
if platform.machine() != 'x86_64':
raise SystemExit("Unsupported platform!")
导入#
[2]:
import warnings
warnings.filterwarnings('ignore')
import itertools
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
os.environ['TF_CPP_MIN_LOG_LEVEL'] = "3" # Silence TensorFlow.
import numpy as np
import pandas as pd
import sionna
import tensorflow as tf
from tqdm.notebook import tqdm
from aerial.phy5g.pdsch import PdschTx
from aerial.phy5g.ldpc.util import get_mcs, random_tb
from aerial.util.fapi import dmrs_bit_array_to_fapi
from aerial.util.data import PuschRecord
from aerial.util.data import save_pickle
# This is for Sionna and pyAerial to coexist on the same GPU:
# Configure the notebook to use only a single GPU and allocate only as much memory as needed.
# For more details, see https://tensorflowcn.cn/guide/gpu.
gpus = tf.config.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(gpus[0], True)
数据集生成参数#
用于生成数据集的参数在此处修改。请注意，某些参数以列表形式给出，这意味着可以为这些参数提供多个值。通常，人们希望训练数据集尽可能多样化，以便使模型能够很好地推广到各种信道条件和不同的传输参数。
[3]:
# This is the target directory. It gets created if it does not exist.
dataset_dir = 'data/example_simulated_dataset/QPSK'
os.makedirs(dataset_dir, exist_ok=True)
# Number of samples is divided roughly evenly between the options below.
num_samples = 12000
# A list of channel models: 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.
channel_models = ["CDL-D"]
# Speeds to include in the dataset
# This is UE speed in m/s. The direction of travel will be chosen randomly within the x-y plane.
speeds = [0.8333]
# Delay spreads to include in the dataset.
# This is the nominal delay spread in [s]. Please see the CDL documentation
# about how to choose this value.
delay_spreads = [100e-9]
# A list of MCS indices (as per TS 38.214) to include in the dataset.
# MCS table value refers to TS 38.214 as follows:
# 1: TS38.214, table 5.1.3.1-1.
# 2: TS38.214, table 5.1.3.1-2.
# 3: TS38.214, table 5.1.3.1-3.
mcss = [1] # 1, 10, 19 used for QPSK, 16QAM and 64QAM, respectively.
mcs_table = 2
# Es/No values to include in the dataset.
# esnos = [9.0, 9.25, 9.5, 9.75, 10.0, 10.25, 10.5, 10.75, 11.0] # MCS 19
# esnos = [-0.5, -0.25, 0.0, 0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0] # MCS 10
esnos = [-7.75, -7.5, -7.25, -7.0, -6.75, -6.5] # MCS 1
# These are fixed for the dataset.
num_tx_ant = 1
num_rx_ant = 4
cell_id = 41
carrier_frequency = 3.5e9 # Carrier frequency in Hz.
link_direction = "uplink"
layers = 1
rnti = 20001
scid = 0
data_scid = 41
dmrs_port = 1
dmrs_position = [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0]
start_sym = 0
num_symbols = 14
start_prb = 0
num_prbs = 273
# Numerology and frame structure. See TS 38.211.
mu = 1
num_ofdm_symbols = 14
fft_size = 4096
cyclic_prefix_length = 288
subcarrier_spacing = 30e3
num_guard_subcarriers = (410, 410)
num_slots_per_frame = 20
信道生成#
无线信道生成使用 NVIDIA Sionna 完成。
[4]:
class Channel(sionna.channel.OFDMChannel):
def __init__(self,
link_direction,
channel_model,
num_tx_ant,
num_rx_ant,
carrier_frequency,
delay_spread,
speed,
resource_grid):
self.resource_grid = resource_grid
self.resource_grid_mapper = sionna.ofdm.ResourceGridMapper(resource_grid)
self.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}!")
super().__init__(
ch_model,
resource_grid,
add_awgn=True,
normalize_channel=True,
return_channel=False
)
def __call__(self, tx_tensor, No):
# 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 = self.resource_grid_mapper(tx_tensor)
rx_tensor = super().__call__((tx_tensor, No))
rx_tensor = self.remove_guard_subcarriers(rx_tensor)
rx_tensor = rx_tensor[0, 0]
rx_tensor = tf.transpose(rx_tensor, (2, 1, 0))
return rx_tensor
# 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
)
PDSCH 发射器#
这将创建 PDSCH 发射器。然而，由于 5G NR PDSCH 和 PUSCH 的对称性，这也可以用于生成具有特定参数化的 PUSCH 帧。在本笔记本中，这被用作 PUSCH 发射器来生成上行链路插槽。
[5]:
pxsch_tx = PdschTx(
cell_id=cell_id,
num_rx_ant=num_tx_ant,
num_tx_ant=num_tx_ant,
)
数据集生成#
实际的数据集生成在此处完成。不同的信道、SNR 和 MCS 参数被遍历，每个参数化选择一定数量的样本，使得样本总数接近期望的数量。
上面创建的 PxSCH 发射器用于生成 Tx 帧。然后，此 Tx 帧被馈送到 Sionna 生成的无线信道中。结果数据记录在 Parquet 文件中，其中包含大致遵循 Small Cell Forum FAPI 规范 格式的 PUSCH 记录。
[6]:
num_cases = len(channel_models) * len(esnos) * len(speeds) * len(delay_spreads) * len(mcss)
num_samples_per_param = num_samples // num_cases
# loop different channel models, speeds, delay spreads, MCS levels etc.
pusch_records = []
for (channel_model, esno, speed, delay_spread, mcs) in \
(pbar := tqdm(itertools.product(channel_models, esnos, speeds, delay_spreads, mcss), total=num_cases)):
status_str = f"Generating... ({channel_model} | {esno} dB | {speed} m/s | {delay_spread} s | MCS {mcs})"
pbar.set_description(status_str)
# Create the channel model.
channel = Channel(
link_direction=link_direction,
channel_model=channel_model,
num_tx_ant=num_tx_ant,
num_rx_ant=num_rx_ant,
carrier_frequency=carrier_frequency,
delay_spread=delay_spread,
speed=speed,
resource_grid=resource_grid
)
for sample in range(num_samples_per_param):
# Generate the dataframe.
slot_number = sample % num_slots_per_frame
# Get modulation order and coderate.
mod_order, coderate = get_mcs(mcs, mcs_table)
tb_input = random_tb(mod_order, coderate, dmrs_position, num_prbs, start_sym, num_symbols, layers)
# Transmit PxSCH. This is where we set the dynamically changing parameters.
# Input parameters are given as lists as the interface supports multiple UEs.
tx_tensor = pxsch_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.
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
mod_orders=[mod_order] # Modulation order
)[0]
# Channel transmission and noise.
No = pow(10., -esno / 10.)
rx_tensor = channel(tx_tensor, No)
rx_tensor = np.array(rx_tensor)
# Save the sample.
rx_iq_data_filename = "rx_iq_{}_esno{}_speed{}_ds{}_mcs{}_{}.pkl".format(channel_model, esno, speed, delay_spread, mcs, sample)
rx_iq_data_fullpath = os.path.join(dataset_dir, rx_iq_data_filename)
save_pickle(data=rx_tensor, filename=rx_iq_data_fullpath)
# Save noise power and SNR data as user data.
user_data_filename = "user_data_{}_esno{}_speed{}_ds{}_mcs{}_{}.pkl".format(channel_model, esno, speed, delay_spread, mcs, sample)
user_data_fullpath = os.path.join(dataset_dir, user_data_filename)
user_data = dict(
snr=esno,
noise_var=No
)
save_pickle(data=user_data, filename=user_data_fullpath)
pusch_record = PuschRecord(
# SCF FAPI 10.02 UL_TTI.request message parameters:
pduIdx=0,
SFN=(sample // num_slots_per_frame) % 1023,
Slot=slot_number,
nPDUs=1,
RachPresent=0,
nULSCH=1,
nULCCH=0,
nGroup=1,
PDUSize=0,
pduBitmap=1,
RNTI=rnti,
Handle=0,
BWPSize=273,
BWPStart=0,
SubcarrierSpacing=mu,
CyclicPrefix=0,
targetCodeRate=coderate * 10,
qamModOrder=mod_order,
mcsIndex=mcs,
mcsTable=mcs_table - 1, # Different indexing
TransformPrecoding=1, # Disabled.
dataScramblingId=data_scid,
nrOfLayers=1,
ulDmrsSymbPos=dmrs_bit_array_to_fapi(dmrs_position),
dmrsConfigType=0,
ulDmrsScramblingId=cell_id,
puschIdentity=cell_id,
SCID=scid,
numDmrsCdmGrpsNoData=2,
dmrsPorts=1, # Note that FAPI uses a different format compared to cuPHY.
resourceAlloc=1,
rbBitmap=np.array(36 * [0]),
rbStart=0,
rbSize=273,
VRBtoPRBMapping=0,
FrequencyHopping=0,
txDirectCurrentLocation=0,
uplinkFrequencyShift7p5khz=0,
StartSymbolIndex=start_sym,
NrOfSymbols=num_symbols,
puschData=None,
puschUci=None,
puschPtrs=None,
dftsOfdm=None,
Beamforming=None,
# SCF FAPI 10.02 RxData.indication message parameters:
HarqID=0,
PDULen=len(tb_input),
UL_CQI=255, # Set to invalid 0xFF.
TimingAdvance=0,
RSSI=65535, # Set to invalid 0xFFFF.
macPdu=tb_input,
TbCrcStatus=0,
NumCb=0,
CbCrcStatus=None,
rx_iq_data_filename=rx_iq_data_filename,
user_data_filename=user_data_filename,
errInd = ""
)
pusch_records.append(pusch_record)
print("Saving...")
df_filename = os.path.join(dataset_dir, "l2_metadata.parquet")
df = pd.DataFrame.from_records(pusch_records, columns=PuschRecord._fields)
df.to_parquet(df_filename, engine="pyarrow")
print("All done!")
Saving...
All done!