pesedoA = 57
numCRC = 24
crcPoly = [24, 23, 21, 20, 17, 15, 13, 12, 8, 4, 2, 1, 0]
K = A + numCRC
PesodoK = pesedoA + numCRC
SNRdB = 0
SNR = 10**(SNRdB / 10)
sigma = np.sqrt(1 / SNR)
experiment_name = "CDF-N={:d}-K=[{:d}, {:d}]-SNR={:.1f} dB".format(
N, A, pesedoA, SNRdB)
if os.path.isdir(
os.path.join(os.path.pardir, "BD_DMetric_EPFL", experiment_name)):
shutil.rmtree(
os.path.join(os.path.pardir, "BD_DMetric_EPFL", experiment_name))
tracer = Tracer('../BD_DMetric_EPFL').attach(experiment_name)
codeConstructor = PolarCodeConstructor(N,
K,
QPath=os.path.join(
os.getcwd(),
"../reliable_sequence.txt"))
frozenbits, messagebits, frozenbits_indicator, messagebits_indicator = codeConstructor.PW(
N, K)
polarEncoder = PolarEnc(N, K, frozenbits, messagebits)
nodeIdentifier = NodeIdentifier(N, K, frozenbits, messagebits)
nodeType = nodeIdentifier.run()
DMetricCalculator = EPFLDMetricCalculator(N, K, nodeType, frozenbits,
messagebits)
frozenbitsPesedo, messagebitsPesedo, _, _ = codeConstructor.PW(N, PesodoK)
# Simulation Parameters
L = args.L
RUN_FAR = args.RUN_FAR
RUN_MDR = 1 - RUN_FAR
# simulation parameter configuration
SNRdBTest = [0, 1, 2, 3, 4]
# initialize Trace instance to maintain simulation hyper-parameters and results and images
if RUN_MDR == True:
experiment_name = "MDR-L = {:d}".format(L)
else:
experiment_name = "FAR-L = {:d}".format(L)
if os.path.isdir(os.path.join(os.getcwd(), "BD_Benchmark", experiment_name)):
shutil.rmtree(os.path.join(os.getcwd(), "BD_Benchmark", experiment_name))
tracer = Tracer('BD_Benchmark').attach(experiment_name)
configure = {"L": L,
"SNRdBTest":SNRdBTest}
tracer.store(Config(configure))
# Initialize PDCCH candidate generator and corresponding decoders
PDCCHGenerator = CandidateGenerator()
CRCEncoder = CRCEnc(PDCCHGenerator.numCRCBits, PDCCHGenerator.crcPoly)
CASCLDecoders = []
for i in range(PDCCHGenerator.numAggregationLevel):
N = PDCCHGenerator.codewordLengthPerAggregationLevel[i]
for m in range(2):
A = PDCCHGenerator.numInformationBits[m]
K = A + PDCCHGenerator.numCRCBits
frozenbits_indicator = PDCCHGenerator.frozenbits_indicator_set[i][m]
messagebits_indicator = PDCCHGenerator.messagebits_indicator_set[i][m]
import shutil
N = 512
A = 57
numCRC = 16
K = A + numCRC
PesodoK = 32 + numCRC
rate = A/N
SNRdB = 3
SNR = 10**(SNRdB/10)
sigma = np.sqrt(1/SNR)
experiment_name = "CDF-N={:d}-K={:d}-SNR={:.1f}".format(N, K, SNR)
if os.path.isdir(os.path.join(os.getcwd(), "BD_DMetric_EPFL", experiment_name)):
shutil.rmtree(os.path.join(os.getcwd(), "BD_DMetric_EPFL", experiment_name))
tracer = Tracer('BD_DMetric_EPFL').attach(experiment_name)
codeConstructor = PolarCodeConstructor(N, K, QPath=os.path.join(os.getcwd(), "reliable_sequence.txt"))
frozenbits, messagebits, frozenbits_indicator, messagebits_indicator = codeConstructor.PW(N, K)
polarEncoder = PolarEnc(N, K, frozenbits, messagebits)
nodeIdentifier = NodeIdentifier(N, K, frozenbits, messagebits)
nodeType = nodeIdentifier.run()
DMetricCalculator = EPFLDMetricCalculator(N, K, nodeType, frozenbits, messagebits)
frozenbitsPesedo, messagebitsPesedo, _, _ = codeConstructor.PW(N, PesodoK)
polarPesedoEncoder = PolarEnc(N, PesodoK, frozenbitsPesedo, messagebitsPesedo)
crcEnc = CRCEnc(crc_n=numCRC, crc_polynominal=[16, 15, 2, 0])
# Try Complete Noise / Random Codewords / Regular Polar Codes
messagebits_indicator = PDCCHGenerator.messagebits_indicator_set[i][m]
SCLOneDec = CASCLDecoder(N=N, K=K, A=A, L=L1, frozen_bits=frozenbits_indicator, message_bits=messagebits_indicator,
crc_n=PDCCHGenerator.numCRCBits, crc_p=PDCCHGenerator.crcPoly)
CASCLDec = CASCLDecoder(N=N, K=K, A=A, L=L2, frozen_bits=frozenbits_indicator, message_bits=messagebits_indicator,
crc_n=PDCCHGenerator.numCRCBits, crc_p=PDCCHGenerator.crcPoly)
SCLOneDecoders.append(SCLOneDec)
CASCLDecoders.append(CASCLDec)
# Initialize Tracer instance to maintain simulation hyper-parameters, results and images
if RUN_MDR == True:
experiment_name = "MDR-L1={:d}-L2={:d}-Omega={:d}".format(L1, L2, Omega)
else:
experiment_name = "FAR-L1={:d}-L2={:d}-Omega={:d}".format(L1, L2, Omega)
if os.path.isdir(os.path.join(os.getcwd(), "BD_Kuangda", experiment_name)):
shutil.rmtree(os.path.join(os.getcwd(), "BD_Kuangda", experiment_name))
tracer = Tracer('BD_Kuangda').attach(experiment_name)
configure = {"L1": L1,
"L2": L2,
"DCI Lengths": PDCCHGenerator.numInformationBits,
"Omega": Omega,
}
tracer.store(Config(configure))
if RUN_MDR == True:
# Start Simulation FAR
print("Simulation for MDR")
# simulation parameter configuration
numSimulation = 10**4
SNRdBTest = [0, 1, 2, 3, 4, 5]
MDR_SNR = []
FER_SNR = []
'Epoch #{:03d}\ttrain_loss: {:.4f}\tvalid_loss: {:.4f}'.format(
epoch, train_loss, valid_loss))
train_losses.append(train_loss)
valid_losses.append(valid_loss)
tracer.epoch_bar.close()
tracer.store(Model(model))
plt.plot(train_losses, label='train loss', c='b')
plt.plot(valid_losses, label='valid loss', c='r')
plt.title('Demo Learning on SQRT')
plt.legend()
tracer.store(plt.gcf(), 'losses.png')
if __name__ == '__main__':
net = nn.Sequential(nn.Linear(1, 6, True), nn.ReLU(),
nn.Linear(6, 12, True), nn.ReLU(),
nn.Linear(12, 12, True), nn.ReLU(),
nn.Linear(12, 1, True))
args = {
'epoch_n': 120,
'batch_size': 50,
'criterion': nn.MSELoss(),
'optimizer': torch.optim.RMSprop(net.parameters(), lr=1e-3),
'dataloader': DataLoader(dataset=torchvision.datasets.fakedata)
}
tracer = Tracer('checkpoints').attach('rabbit')
train(net, tracer, **args)
tracer.detach()