def proposal(params: dict, sigma, h_si, h_s, receive_antenna_count) -> NNModel:
system_model = SystemModel(params['n'], sigma, params['gamma'],
params['phi'], params['PA_IBO_dB'],
params['PA_rho'], params['LNA_IBO_dB'],
params['LNA_rho'], h_si, h_s,
params['h_si_len'], params['h_s_len'],
receive_antenna_count, params['TX_IQI'],
params['PA'], params['LNA'], params['RX_IQI'])
# NNを生成
nn_model = NNModel(params['nHidden'], params['optimizer'],
params['learningRate'], params['h_si_len'],
params['h_s_len'], receive_antenna_count,
params['momentum'])
nn_model.learn(system_model, params['trainingRatio'], params['nEpochs'],
params['batchSize'], params['h_si_len'], params['h_s_len'],
receive_antenna_count, params['delay'],
params['standardization'])
return nn_model
logging.info('random channel')
logging.info('h_si:{0.real}+{0.imag}i'.format(h_si))
logging.info('h_s:{0.real}+{0.imag}i'.format(h_s))
for IBO_index, IBO_dB in enumerate(params['IBO_dB']):
for index, sigma in enumerate(sigmas):
logging.info("IBO_dB_index:" + str(IBO_index))
logging.info("SNR_AVERAGE_index:" + str(snr_index))
logging.info("sigma_index:" + str(index))
logging.info("time: %d[sec]" % int(time.time() - start))
system_model = SystemModel(
params['n'],
sigma,
params['gamma'],
params['phi'],
IBO_dB,
params['rho'],
IBO_dB,
params['rho'],
h_si,
h_s,
)
demodulate = m.demodulate_qpsk(system_model.y.squeeze())
error = np.sum(system_model.d_s != demodulate)
errors[IBO_index][index][snr_index] = error
logging.info("learn_end_time: %d[sec]" % int(time.time() - start))
# 結果をdumpしておく
result = Result(params, errors, losss, val_losss)
h_si = m.channel()
h_s = m.channel()
logging.info('random channel')
logging.info('h_si:{0.real}+{0.imag}i'.format(h_si))
logging.info('h_s:{0.real}+{0.imag}i'.format(h_s))
for index, sigma in enumerate(sigmas):
logging.info("SNR_AVERAGE_index:" + str(snr_index))
logging.info("sigma_index:" + str(index))
logging.info("time: %d[sec]" % int(time.time() - start))
system_model = SystemModel(
params['n'],
sigma,
params['gamma'],
params['phi'],
params['PA_IBO_dB'],
params['PA_rho'],
params['LNA_IBO_dB'],
params['LNA_rho'],
h_si,
h_s,
)
# NNを生成
model = NNModel(params['nHidden'])
model.learn(system_model, params['trainingRatio'], params['nEpochs'], params['batchSize'])
errors[index][snr_index] = model.error
losss[index][snr_index][:] = model.history.history['loss']
val_losss[index][snr_index][:] = model.history.history['val_loss']
logging.info("learn_end_time: %d[sec]" % int(time.time() - start))
'nEpochs': 20,
'learningRate': 0.004,
'trainingRatio': 0.8, # 全体のデータ数に対するトレーニングデータの割合
'batchSize': 32,
}
h_si = m.channel()
h_s = m.channel()
system_model = SystemModel(
params['n'],
0,
params['gamma'],
params['phi'],
params['PA_IBO_dB'],
params['PA_rho'],
params['LNA_IBO_dB'],
params['LNA_rho'],
h_si,
h_s,
params['h_si_len'],
params['h_s_len'],
)
model = NNModel(
params['nHidden'],
params['learningRate'],
params['h_si_len'],
params['h_s_len'],
)
model.learn(
system_model,
'phi': 3.0,
'PA_IBO_dB': 1000,
'PA_rho': 1000,
'LNA_IBO_dB': 7,
'LNA_rho': 2,
'h_si_len': 1,
'h_s_len': 1,
"receive_antenna": 1
}
h_si = m.channel(1, params['h_si_len'])
h_s = m.channel(1, params['h_s_len'])
system_model = SystemModel(params['n'], 0, params['gamma'], params['phi'],
params['PA_IBO_dB'], params['PA_rho'],
params['LNA_IBO_dB'], params['LNA_rho'], h_si,
h_s, params['h_si_len'], params['h_s_len'],
params['receive_antenna'], True, False, False,
False)
d = m.compensate_iqi(system_model.x_iq, params['gamma'], params['phi'])
plt.figure()
plt.scatter(system_model.x.real,
system_model.x.imag,
color="red",
label="x")
plt.scatter(system_model.x_iq.real,
system_model.x_iq.imag,
color="blue",
label="x_iq")
plt.scatter(d.real, d.imag, color="green", label="x_compensate_iq")
h_si.append(m.channel(1, params['h_si_len']))
h_s.append(m.channel(1, params['h_s_len']))
for IBO_index, IBO_dB in enumerate(params['IBO_dB']):
for sigma_index, sigma in enumerate(sigmas):
logging.info("IBO_dB_index:" + str(IBO_index))
logging.info("SNR_AVERAGE_index:" + str(trials_index))
logging.info("sigma_index:" + str(sigma_index))
system_model = SystemModel(
params['n'],
sigma,
params['gamma'],
params['phi'],
IBO_dB,
params['rho'],
params['LNA_IBO_dB'],
params['LNA_rho'],
h_si,
h_s,
params['h_si_len'],
params['h_s_len'],
params['receive_antenna'],
)
# NNを生成
nn_model = NNModel(
params['nHidden'],
params['optimizer'],
params['learningRate'],
params['h_si_len'],
params['h_s_len'],