y_vec = np.array(
[cancelled_y[i:i + size - L_w] for i in range(L_w + 1)])
z = np.matmul(W.conj().T, y_vec)
d_hat = m.demodulate_qpsk(z)
d_hat_len = d_hat.shape[0]
error = np.sum(system_model.d_s[0:d_hat_len] != d_hat)
error_array[sigma_index][trials_index] = error
result = Result(params, error_array, loss_array, val_loss_array)
with open(output_dir + '/result.pkl', 'wb') as f:
pickle.dump(result, f)
ber_fig, ber_ax = graph.new_snr_ber_canvas(params['SNR_MIN'],
params['SNR_MAX'])
n_sum = d_hat_len * params['SNR_AVERAGE']
errors_sum = np.sum(error_array, axis=1)
bers = errors_sum / n_sum
ber_ax.plot(snrs_db,
bers,
color="k",
marker='o',
linestyle='--',
label="Previous research + MMSE")
ber_ax.legend()
plt.savefig(output_dir + '/SNR_BER.pdf', bbox_inches='tight')
output_png_path = output_dir + '/SNR_BER.png'
plt.savefig(output_png_path, bbox_inches='tight')
params.PA, params.LNA, params.RX_IQI, params.nHidden,
params.optimizer, params.learningRate, params.momentum,
params.trainingRatio, params.nEpochs, params.batchSize,
params.delay, params.standardization)
errors[sigma_index][trials_index] = nn_model.error
losss[sigma_index][trials_index][:] = nn_model.history.history[
'loss']
val_losss[sigma_index][trials_index][:] = nn_model.history.history[
'val_loss']
result = Result(params, errors, losss, val_losss)
with open(output_dir + '/result.pkl', 'wb') as f:
pickle.dump(result, f)
ber_fig, ber_ax = graph.new_snr_ber_canvas(params.SNR_MIN, params.SNR_MAX)
n_sum = params.test_bits * params.SNR_AVERAGE
errors_sum = np.sum(errors, axis=1)
bers = errors_sum / n_sum
ber_ax.plot(snrs_db,
bers,
color="k",
marker='o',
linestyle='--',
label="OFDM")
plt.tight_layout()
plt.savefig(output_dir + '/SNR_BER.pdf', bbox_inches='tight')
# slack通知用
output_png_path = output_dir + '/SNR_BER.png'
if __name__ == '__main__':
SIMULATIONS_NAME = os.path.basename(__file__).split('.')[0]
load_files = 1 # 同じ条件で読み込む数
dirname = settings.dirname_current_datetime(SIMULATIONS_NAME)
# dirname = "../results/keep/ofdm_fde_system_model_chain_load"
settings.init_output(dirname)
param_path = "../results/keep/fig031921/ber_3/params.json"
params = settings.load_param(param_path)
# n_sum = params["test_bits"] * params['SNR_AVERAGE'] * load_files
n_sum = params['test_bits'] * params['trials']
snrs_db = np.linspace(params['graph_x_min'], params['graph_x_max'], params['graph_x_num'])
fig, ax = graph.new_snr_ber_canvas(params['graph_x_min'], params['graph_x_max'], -4)
ax.set_yticks([10**0, 10**-1, 10**-2, 10**-3, 10**-4])
previous_n_sum = params['previous_test_bits'] * params['trials']
pkl_path = "../results/keep/fig031921/ber_3/result.pkl"
result = load_pkl_file(pkl_path)
errors_sum = np.sum(result.non_cancell_error_array, axis=1)
bers = errors_sum / previous_n_sum
np.place(bers, bers == 0, None)
ax.plot(snrs_db, bers, color='k', marker='x', linestyle=':', label="w/o canceller " + r"$(I=1)$", ms=10)
errors_sum = np.sum(result.previous_errors, axis=1)
bers = errors_sum / previous_n_sum
np.place(bers, bers == 0, None)
ax.plot(snrs_db, bers, color='k', marker='o', linestyle='--', label="Conventional " + r"$(I=1)$" + " [5]" , ms=10, markerfacecolor='None')
params.nHidden, params.optimizer, params.learningRate,
params.momentum, params.trainingRatio, params.nEpochs,
params.batchSize, params.delay, params.standardization)
errors[graph_x_index][trials_index] = nn_model.error
losss[graph_x_index][trials_index][:] = nn_model.history.history[
'loss']
val_losss[graph_x_index][
trials_index][:] = nn_model.history.history['val_loss']
result = Result(params, errors, losss, val_losss, non_cancell_error_array,
previous_errors, previous_losss, previous_val_losss)
with open(output_dir + '/result.pkl', 'wb') as f:
pickle.dump(result, f)
ber_fig, ber_ax = graph.new_snr_ber_canvas(params.graph_x_min,
params.graph_x_max, -6)
n_sum = params.previous_test_bits * params.trials
errors_sum = np.sum(non_cancell_error_array, axis=1)
bers = errors_sum / n_sum
ber_ax.plot(graph_x_array,
bers,
color="k",
marker='x',
linestyle='--',
label="w/o canceller")
n_sum = params.previous_test_bits * params.trials
errors_sum = np.sum(previous_errors, axis=1)
bers = errors_sum / n_sum
ber_ax.plot(graph_x_array,
]) # [[x[n], x[n-1]], x[x-1], x[n-1]]のように通信路の数に合わせる
chanels_s = h_s * chanels_s
y_s = np.sum(chanels_s, axis=1)
r = y_s + m.awgn(y_s.shape, sigma)
size = r.shape[0]
r_vec = np.array([r[i:i + size - L_w] for i in range(L_w + 1)])
W = mmse(H, noise_var)
z = np.matmul(W.conj().T, r_vec)
d_hat = m.demodulate_qpsk(z)
error = np.sum(d_s[0:d_hat.shape[0]] != d_hat)
error_array[sigma_index][trials_index] = error
ber_fig, ber_ax = graph.new_snr_ber_canvas(snr_min, snr_max)
n_sum = (n - 2 * h_s_len) * ave
errors_sum = np.sum(error_array, axis=1)
bers = errors_sum / n_sum
ber_ax.plot(snrs_db,
bers,
color="k",
marker='o',
linestyle='--',
label="MMSE")
ber_ax.legend()
plt.show()