def __init__(self, params, errors, losss, val_losss):
self.params = params
self.errors = errors
self.losss = losss
self.val_losss = val_losss
if __name__ == '__main__':
SIMULATIONS_NAME = 'previous_research'
params, output_dir = settings.init_simulation(SIMULATIONS_NAME)
# 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
loss_array = np.zeros(
(params['SNR_NUM'], params['SNR_AVERAGE'], params['nEpochs']))
val_loss_array = np.zeros(
(params['SNR_NUM'], params['SNR_AVERAGE'], params['nEpochs']))
error_array = np.zeros((len(snrs_db), params['SNR_AVERAGE']))
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))
val_losss: np.ndarray
if __name__ == '__main__':
SIMULATIONS_NAME = 'ofdm_fde_system_model_delay'
params_dict, output_dir = settings.init_simulation(SIMULATIONS_NAME,
ofdm=True)
params = Params.from_params_dict(params_dict)
delay_array = np.linspace(params.delay_min,
params.delay_max,
params.delay_num,
dtype=int)
sigma = m.sigmas(params.SNR)
sigma = sigma * np.sqrt(params.receive_antenna)
errors = np.zeros((params.delay_num, params.trials))
losss = np.zeros((params.delay_num, params.trials, params.nEpochs))
val_losss = np.zeros((params.delay_num, params.trials, params.nEpochs))
for trials_index in tqdm(range(params.trials)):
h_si = []
h_s = []
for i in range(params.receive_antenna):
h_si.append(m.exponential_decay_channel(1, params.h_si_len))
h_s.append(m.exponential_decay_channel(1, params.h_s_len))
for delay_array_index, delay in enumerate(delay_array):
nn_model = simulation(
previous_errors = np.zeros((params.graph_x_num, params.trials))
previous_losss = np.zeros(
(params.graph_x_num, params.trials, params.p_nEpochs))
previous_val_losss = np.zeros(
(params.graph_x_num, params.trials, params.p_nEpochs))
for trials_index in tqdm(range(params.trials)):
h_si = []
h_s = []
for i in range(params.receive_antenna):
h_si.append(m.exponential_decay_channel(1, params.h_si_len))
h_s.append(m.exponential_decay_channel(1, params.h_s_len))
for graph_x_index, SNR in enumerate(graph_x_array):
sigma = m.sigmas(SNR)
# previous_sigma = sigma
previous_sigma = sigma * np.sqrt(params.p_receive_antenna)
proposal_sigma = sigma * np.sqrt(params.receive_antenna)
non_cancell_error_array[
graph_x_index][trials_index] = noncancel_simulation(
params.block, params.subcarrier, params.CP, previous_sigma,
params.gamma, params.phi, params.PA_IBO, params.PA_rho,
params.LNA_alpha_1, params.LNA_alpha_2, h_si, h_s,
params.h_si_len, params.h_s_len, params.TX_IQI, params.PA,
params.LNA, params.RX_IQI, params.trainingRatio,
params.compensate_iqi, params.p_receive_antenna,
params.equalizer)
previous_nn_model = previous_simulation(
class Result:
params: Params
errors: np.ndarray
losss: np.ndarray
val_losss: np.ndarray
if __name__ == '__main__':
SIMULATIONS_NAME = 'ofdm_fde_system_model'
params_dict, output_dir = settings.init_simulation(SIMULATIONS_NAME,
ofdm=True)
params = Params.from_params_dict(params_dict)
snrs_db = m.snr_db(params.SNR_MIN, params.SNR_MAX, params.SNR_NUM)
sigmas = m.sigmas(snrs_db)
sigmas = sigmas * np.sqrt(params.receive_antenna)
errors = np.zeros((params.SNR_NUM, params.SNR_AVERAGE))
losss = np.zeros((params.SNR_NUM, params.SNR_AVERAGE, params.nEpochs))
val_losss = np.zeros((params.SNR_NUM, params.SNR_AVERAGE, params.nEpochs))
for trials_index in tqdm(range(params.SNR_AVERAGE)):
h_si = []
h_s = []
for i in range(params.receive_antenna):
h_si.append(m.exponential_decay_channel(1, params.h_si_len))
h_s.append(m.exponential_decay_channel(1, params.h_s_len))
for sigma_index, sigma in enumerate(sigmas):
nn_model = simulation(
val_losss_list.append(result.val_losss)
errors = np.concatenate(errors_list, 1)
losss = np.concatenate(losss_list, 1)
val_losss = np.concatenate(val_losss_list, 1)
logging.info('chainload')
logging.info(len(results))
params = results[0].params
logging.info('params')
logging.info(params)
# データを生成する
snr = params['snr']
sigma = m.sigmas(snr) # SNR(dB)を元に雑音電力を導出
var_si_var_s_db = np.linspace(params['var_min'], params['var_max'],
params['var_num'])
var_si_var_s = m.to_exact_number(var_si_var_s_db)
var_si = 1
var_s_list = var_si / var_si_var_s
non_cancell_error = np.zeros(
(params['var_num'], params['number_of_trials']))
for trials_index in tqdm(range(params['number_of_trials'])):
for var_s_index, var_s in enumerate(var_s_list):
# 通信路は毎回生成する
h_si = m.channel(1, params['h_si_len'], var_si)
h_s = m.channel(1, params['h_s_len'], var_s)
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))
