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main_communications.py
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main_communications.py
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from utils import *
from classes import *
data_params = {
'test_size': 30000,
'noise_model': "AWGN", # "PAM_attenuation" "PAM_triangular" "AWGN"
'SNR_vec': range(-15, 20)
}
proc_params = {
'alpha': 0.8,
'batch_size': 128,
'lr': 0.001,
'num_epochs': 500, # 500 for PAM attenuation, 1000 for PAM triangular
'cost_function_v': 5,
'random_seed': 0
}
ber_total = []
ber_total_genie = []
H_x_total = []
H_x_y_total = []
MI_total = []
P_error = []
ber_total_LL = []
ber_total_LL_Mid = []
ber_total_LL_pdf_Mid = []
ber_total_LL_AWGN = []
ber_total_MAP = []
for j, SNR in enumerate(data_params['SNR_vec']):
print("SNR: ", SNR)
eps = np.sqrt(pow(10, -0.1 * SNR) / (2 * 0.5))
if data_params['noise_model'] == "AWGN":
print("AWGN")
latent_dim = 6
output_dim = 2 ** latent_dim
model = Discriminator(input_dim=latent_dim, output_dim=output_dim)
combined = CombinedArchitecture(model, cost_function_v=proc_params['cost_function_v'])
trained_combined = train_communication_awgn(combined, latent_dim, eps=eps, lr=proc_params['lr'],
num_epochs=proc_params['num_epochs'],
batch_size=proc_params['batch_size'],
noise_model=data_params['noise_model'],
cost_function_v=proc_params['cost_function_v'],
alpha=proc_params['alpha'], random_seed=proc_params['random_seed'])
ber, ber_maxL, H_x, H_x_y, MI, P_e, y = test_communication_awgn(trained_combined, latent_dim,
test_size=data_params['test_size'],
noise_model=data_params['noise_model'],
eps=eps, cost_function_v=proc_params[
'cost_function_v'], random_seed=proc_params['random_seed'])
ber_total.append(ber)
ber_total_genie.append(ber_maxL)
H_x_total.append(H_x)
H_x_y_total.append(H_x_y)
MI_total.append(MI)
P_error.append(P_e)
elif data_params['noise_model'] == "PAM_attenuation":
latent_dim = 1
M = 4
model = Discriminator(input_dim=latent_dim, output_dim=M)
combined = CombinedArchitecture(model, cost_function_v=proc_params['cost_function_v'])
trained_combined = train_communication_pam_attenuation(combined, latent_dim, M, eps=eps,
lr=proc_params['lr'],
num_epochs=proc_params['num_epochs'],
batch_size=32, save_training_loss=True,
noise_model=data_params['noise_model'],
cost_function_v=proc_params['cost_function_v'],
alpha=proc_params['alpha'], random_seed=proc_params['random_seed'])
cber, cber_LL, cber_LL_AWGN, MI, R = test_communication_pam_attenuation(
trained_combined, latent_dim, M,
test_size=data_params['test_size'],
noise_model=data_params['noise_model'], eps=eps,
cost_function_v=proc_params['cost_function_v'], random_seed=proc_params['random_seed'])
ber_total.append(np.mean(cber))
ber_total_LL.append(np.mean(cber_LL))
ber_total_LL_AWGN.append(np.mean(cber_LL_AWGN))
MI_total.append(np.mean(MI))
elif data_params['noise_model'] == "PAM_triangular":
latent_dim = 1
M = 4
model = Discriminator(input_dim=latent_dim, output_dim=M)
combined = CombinedArchitecture(model, cost_function_v=proc_params['cost_function_v'])
trained_combined = train_communication_pam_triangular(combined, latent_dim, eps=eps, lr=proc_params['lr'],
num_epochs=proc_params['num_epochs'],
batch_size=32, save_training_loss=True,
noise_model=data_params['noise_model'],
cost_function_v=proc_params['cost_function_v'],
alpha=proc_params['alpha'], random_seed=proc_params['random_seed'])
cber, cber_LL, cber_MAP, fDIME_e, _ = test_communication_pam_triangular(
trained_combined, latent_dim,
test_size=data_params['test_size'],
noise_model=data_params['noise_model'], eps=eps,
cost_function_v=proc_params['cost_function_v'], random_seed=proc_params['random_seed'])
ber_total.append(np.mean(cber))
ber_total_LL.append(np.mean(cber_LL))
ber_total_MAP.append(np.mean(cber_MAP))
MI_total.append(np.mean(fDIME_e))