def lin_cancel_simulation(params: dict, sigma, h_si, h_s) -> np.ndarray:
system_model = PreviousSystemModel(
sigma, params['gamma'], params['phi'], params['PA_IBO_dB'],
params['PA_rho'], params['LNA_IBO_dB'], params['LNA_rho'], h_si,
params['h_si_len'], h_s, params['h_s_len'], params['TX_IQI'],
params['PA'], params['LNA'], params['RX_IQI'])
system_model.set_lna_a_sat(
params['lin_n'],
params['LNA_IBO_dB'],
)
system_model.transceive_si(params['lin_n'])
h_lin = fd.ls_estimation(system_model.x[0:int(params['lin_n'] / 2)],
system_model.y, params['h_si_len'])
system_model.transceive_si_s(params['lin_n'], )
yCanc = fd.si_cancellation_linear(
system_model.x[0:int(params['lin_n'] / 2)], h_lin)
cancelled_y = system_model.y - yCanc
s_hat = cancelled_y * h_s.conj() / (np.abs(h_s)**2)
d_hat = m.demodulate_qpsk(s_hat)
d_hat_len = d_hat.shape[0]
error = np.sum(system_model.d_s[0:d_hat_len] != d_hat)
return error
def non_cancel_simulation(param: dict, sigma, h_si, h_s) -> np.ndarray:
system_model = PreviousSystemModel(
sigma,
params['gamma'],
params['phi'],
params['PA_IBO_dB'],
params['PA_rho'],
params['LNA_IBO_dB'],
params['LNA_rho'],
h_si,
params['h_si_len'],
h_s,
params['h_s_len'],
)
system_model.set_lna_a_sat(
params['lin_n'],
params['LNA_IBO_dB'],
)
system_model.transceive_si_s(
params['lin_n'],
)
s_hat = system_model.y * h_s.conj() / (np.abs(h_s)**2)
d_hat = m.demodulate_qpsk(s_hat)
d_hat_len = d_hat.shape[0]
error = np.sum(system_model.d_s[0:d_hat_len] != d_hat)
return error
def previous_non_lin(params: dict, sigma, h_si, h_s) -> PreviousNNModel:
training_samples = int(np.floor(params['n'] * params['trainingRatio']))
test_n = params['n'] - training_samples
system_model = PreviousSystemModel(
sigma, params['gamma'], params['phi'], params['PA_IBO_dB'],
params['PA_rho'], params['LNA_IBO_dB'], params['LNA_rho'], h_si,
params['h_si_len'], h_s, params['h_s_len'], params['TX_IQI'],
params['PA'], params['LNA'], params['RX_IQI'])
system_model.set_lna_a_sat(
test_n,
params['LNA_IBO_dB'],
)
system_model.transceive_si(params['n'])
previous_nn_model = PreviousNNModel(params['h_si_len'],
params['p_nHidden'],
params['p_learningRate'])
previous_nn_model.learn(system_model.x[0:int(params['n'] / 2)],
system_model.y, params['p_trainingRatio'],
params['h_si_len'], params['p_nEpochs'],
params['p_batchSize'])
system_model.transceive_si_s(test_n, )
previous_nn_model.cancel(
system_model.x[0:int(test_n / 2)],
system_model.y,
params['h_si_len'],
)
s_hat = previous_nn_model.cancelled_y * h_s.conj() / (np.abs(h_s)**2)
d_hat = m.demodulate_qpsk(s_hat)
d_hat_len = d_hat.shape[0]
error = np.sum(system_model.d_s[0:d_hat_len] != d_hat)
previous_nn_model.error = error
return previous_nn_model
for trials_index in tqdm(range(params['SNR_AVERAGE'])):
h_si = m.channel(1, params['h_si_len'])
h_s = m.channel(1, params['h_s_len'])
for sigma_index, sigma in enumerate(sigmas):
logging.info("SNR_AVERAGE_index:" + str(trials_index))
logging.info("sigma_index:" + str(sigma_index))
system_model = PreviousSystemModel(
sigma,
params['gamma'],
params['phi'],
params['PA_IBO_dB'],
params['PA_rho'],
params['LNA_IBO_dB'],
params['LNA_rho'],
h_si,
params['h_si_len'],
h_s,
params['h_s_len'],
)
system_model.transceive_si(params['n'])
previous_nn_model = PreviousNNModel(params['h_si_len'],
params['nHidden'],
params['learningRate'])
previous_nn_model.learn(system_model.x[0:int(params['n'] / 2)],
system_model.y, params['trainingRatio'],
params['h_si_len'], params['nEpochs'],
"n_hidden": 17,
"learning_rate": 0.004,
"training_ratio": 0.8,
"batch_size": 32,
"nEpochs": 20
}
# データを生成する
snrs_db = np.linspace(params['SNR_MIN'], params['SNR_MAX'],
params['SNR_NUM'])
sigmas = m.sigmas(snrs_db) # SNR(dB)を元に雑音電力を導出
h_si = m.channel(1, params['h_si_len'])
for sigma_index, sigma in enumerate(sigmas):
system_model = PreviousSystemModel()
system_model.transceive_si(
params['n'],
0,
params['gamma'],
params['phi'],
params['PA_IBO_dB'],
params['PA_rho'],
params['LNA_IBO_dB'],
params['LNA_rho'],
h_si,
params['h_si_len'],
)
plt.figure()
plt.scatter(system_model.y.real,
