def transceive_s(self, n):
offset_n = n + 2 * (self.h_s_len - 1) # 遅延を取る為に多く作っておく
# 希望信号
self.d_s = np.random.choice([0, 1], offset_n)
self.s = m.modulate_qpsk(self.d_s)
# [[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 = np.array([
self.s[i:i + self.h_s_len]
for i in range(self.s.size - self.h_s_len + 1)
])
chanels_s = self.h_s * chanels_s
y_s = np.sum(chanels_s, axis=1)
r = y_s + m.awgn(y_s.shape, self.sigma)
# 受信側非線形
if self.lna == True:
y_lna = m.sspa_rapp(r, self.a_sat, self.LNA_rho).squeeze()
else:
y_lna = r
if self.rx_iqi == True:
y_iq = m.iq_imbalance(y_lna, self.gamma, self.phi)
else:
y_iq = y_lna
self.y = y_iq
def transceive_s(self):
# 送信信号
self.d = np.random.choice([0, 1],
(self.subcarrier * 2 * self.block, 1))
x_n = m.modulate_qpsk(self.d)
x_p = x_n.reshape((self.subcarrier, self.block), order='F')
x_idft = np.matmul(self.idft_mat, x_p)
x_cp = ofdm.add_cp(x_idft, self.CP)
x = x_cp
self.x = x_cp.flatten(order='F')
tx_x = x
# 送信側非線形
if self.tx_iqi == True:
tx_x = m.iq_imbalance(tx_x, self.gamma, self.phi)
if self.pa == True:
tx_x = m.sspa_rapp_ibo(tx_x,
self.PA_IBO_dB,
self.PA_rho,
ofdm=True)
x_rx = tx_x
if self.h_si_len > 1:
x_rx = np.zeros((self.h_si_len - 1 + tx_x.shape[0], tx_x.shape[1]),
dtype=complex)
x_rx[:(self.h_si_len - 1), 1:] = tx_x[-(self.h_si_len - 1):, :-1]
x_rx[(self.h_si_len - 1):, :] = tx_x
self.y = np.zeros(
(self.subcarrier_CP * self.block, self.receive_antenna),
dtype=complex)
for receive_antenna_i in range(self.receive_antenna):
h_si = self.h_si_list[receive_antenna_i]
toeplitz_h_si = ofdm.toeplitz_channel(h_si.T, self.h_si_len,
self.subcarrier, self.CP)
r = np.matmul(toeplitz_h_si, x_rx) + m.awgn(
(self.subcarrier + self.CP, self.block), self.sigma)
# 受信側非線形
if self.lna == True:
r = m.polynomial_amplifier(r, self.LNA_alpha_1,
self.LNA_alpha_2)
if self.rx_iqi == True:
r = m.iq_imbalance(r, self.gamma, self.phi)
y = r.flatten(order='F')
self.y[:, receive_antenna_i] = y
def transceive_si(self, n):
offset_n = n + 2 * (self.h_si_len - 1) # 遅延を取る為に多く作っておく
# 送信信号
self.d = np.random.choice([0, 1], offset_n)
self.x = m.modulate_qpsk(self.d)
# 送信側非線形
if self.tx_iqi == True:
self.x_iq = m.iq_imbalance(self.x, self.gamma, self.phi)
else:
self.x_iq = self.x
if self.pa == True:
self.x_pa = m.sspa_rapp_ibo(self.x_iq, self.PA_IBO_dB, self.PA_rho)
else:
self.x_pa = self.x_iq
# 通信路
# [[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_x_pa = np.array([
self.x_pa[i:i + self.h_si_len]
for i in range(self.x_pa.size - self.h_si_len + 1)
])
chanels_y_si = self.h_si * chanels_x_pa
y_si = np.sum(chanels_y_si, axis=1)
r = y_si + m.awgn(y_si.shape, self.sigma)
# 受信側非線形
if self.lna == True:
y_lna = m.sspa_rapp(r, self.a_sat, self.LNA_rho).squeeze()
else:
y_lna = r
if self.rx_iqi == True:
y_iq = m.iq_imbalance(y_lna, self.gamma, self.phi)
else:
y_iq = y_lna
self.y = y_iq
def set_lna_a_sat(self, n, LNA_IBO_dB):
# TODO 調整する
offset_n = n + 2 * (self.h_si_len - 1) # 遅延を取る為に多く作っておく
# 送信信号
d = np.random.choice([0, 1], offset_n)
x = m.modulate_qpsk(d)
# 希望信号
d_s = np.random.choice([0, 1], offset_n)
s = m.modulate_qpsk(d_s)
# 送信側非線形
if self.tx_iqi == True:
x_iq = m.iq_imbalance(x, self.gamma, self.phi)
else:
x_iq = x
if self.pa == True:
x_pa = m.sspa_rapp_ibo(x_iq, self.PA_IBO_dB, self.PA_rho)
else:
x_pa = x_iq
# 通信路
# [[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_x_pa = np.array([
x_pa[i:i + self.h_si_len]
for i in range(x_pa.size - self.h_si_len + 1)
])
chanels_y_si = self.h_si * chanels_x_pa
y_si = np.sum(chanels_y_si, axis=1)
# [[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 = np.array(
[s[i:i + self.h_s_len] for i in range(s.size - self.h_s_len + 1)])
chanels_s = self.h_s * chanels_s
y_s = np.sum(chanels_s, axis=1)
r = y_si + y_s + m.awgn(y_si.shape, self.sigma)
self.a_sat = m.a_sat(r, LNA_IBO_dB)
def __init__(self,
block: int,
subcarrier: int,
CP: int,
sigma: float,
gamma=0.0,
phi=0.0,
PA_IBO_dB=5,
PA_rho=2,
LNA_alpha_1=5,
LNA_alpha_2=2,
h_si_list=None,
h_s_list=None,
h_si_len=1,
h_s_len=1,
receive_antenna=1,
tx_iqi=True,
pa=True,
lna=True,
rx_iqi=True):
self.block = block
self.subcarrier = subcarrier
self.CP = CP
self.subcarrier_CP = subcarrier + CP
# 必要な行列を生成する
dft_mat = dft(subcarrier, scale="sqrtn")
self.dft_mat = dft_mat
self.idft_mat = dft_mat.conj().T
self.cp_zero = np.hstack((np.zeros(
(subcarrier, CP)), np.eye(subcarrier)))
# ここまで
# 送信信号
self.d = np.random.choice([0, 1], (subcarrier * 2 * block, 1))
x_n = m.modulate_qpsk(self.d)
x_p = x_n.reshape((subcarrier, block), order='F')
x_idft = np.matmul(self.idft_mat, x_p)
x_cp = ofdm.add_cp(x_idft, CP)
x = x_cp
self.x = x_cp.flatten(order='F')
tx_x = x
# 送信側非線形
if tx_iqi == True:
tx_x = m.iq_imbalance(tx_x, gamma, phi)
if pa == True:
tx_x = m.sspa_rapp_ibo(tx_x, PA_IBO_dB, PA_rho, ofdm=True)
# 希望信号
self.d_s = np.random.choice([0, 1], (subcarrier * 2 * block, 1))
s_n = m.modulate_qpsk(self.d_s)
# self.s = s_n # シリアルの状態を保持する
s_p = s_n.reshape((subcarrier, block), order='F')
s_idft = np.matmul(self.idft_mat, s_p)
s_cp = ofdm.add_cp(s_idft, CP)
self.tilde_s = s_cp
x_rx = tx_x
if h_si_len > 1:
x_rx = np.zeros((h_si_len - 1 + tx_x.shape[0], tx_x.shape[1]),
dtype=complex)
x_rx[:(h_si_len - 1), 1:] = tx_x[-(h_si_len - 1):, :-1]
x_rx[(h_si_len - 1):, :] = tx_x
s_rx = s_cp
if h_s_len > 1:
s_rx = np.zeros((h_s_len - 1 + s_cp.shape[0], s_cp.shape[1]),
dtype=complex)
s_rx[:(h_s_len - 1), 1:] = s_cp[-(h_s_len - 1):, :-1]
s_rx[(h_s_len - 1):, :] = s_cp
self.y = np.zeros((self.subcarrier_CP * block, receive_antenna),
dtype=complex)
for receive_antenna_i in range(receive_antenna):
h_si = h_si_list[receive_antenna_i]
h_s = h_s_list[receive_antenna_i]
toeplitz_h_si = ofdm.toeplitz_channel(h_si.T, h_si_len, subcarrier,
CP)
toeplitz_h_s = ofdm.toeplitz_channel(h_s.T, h_s_len, subcarrier,
CP)
r = np.matmul(toeplitz_h_si, x_rx) + np.matmul(
toeplitz_h_s, s_rx) + m.awgn((subcarrier + CP, block), sigma)
# 受信側非線形
if lna == True:
r = m.polynomial_amplifier(r, LNA_alpha_1, LNA_alpha_2)
if rx_iqi == True:
r = m.iq_imbalance(r, gamma, phi)
y = r.flatten(order='F')
self.y[:, receive_antenna_i] = y
for sigma_index, sigma in enumerate(sigmas):
noise_var = sigma**2
d_s = np.random.choice([0, 1], n)
s = m.modulate_qpsk(d_s)
H = toeplitz_chanel(h_s.T, h_s_len, L_w)
chanels_s = np.array([
s[i:i + h_s_len] for i in range(s.size - h_s_len + 1)
]) # [[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)
D_1 = np.linalg.inv(D)
for sigma_index, sigma in enumerate(sigmas):
d = np.random.choice([0, 1], (params['subcarrier'] * 2 * params['block'], 1))
s_n = m.modulate_qpsk(d)
s = s_n.reshape(params['subcarrier'], params['block'])
x = np.matmul(FH, s)
x_cp = ofdm.add_cp(x, params['CP'])
x_receive = x_cp
if params["chanel_len"] > 1:
x_receive = np.zeros((params['chanel_len'] - 1 + x_cp.shape[0], x_cp.shape[1]), dtype=complex)
x_receive[:(params["chanel_len"] - 1), 1:] = x_cp[-(params["chanel_len"] - 1):, :-1]
x_receive[(params["chanel_len"] - 1):, :] = x_cp
noise = m.awgn((params['subcarrier'] + params['CP'], params['block']), sigma)
r = np.matmul(H, x_receive) + noise
r_s = r.flatten()
y_p = r_s.reshape((params['subcarrier'] + params['CP'], params['block']))
y_remove_cp = np.matmul(ofdm_zero, y_p)
y = np.matmul(F, y_remove_cp)
s_hat = np.matmul(D_1, y)
s_n_hat = s_hat.reshape(params['subcarrier'] * params['block'])
d_hat = m.demodulate_qpsk(s_n_hat).reshape((params['subcarrier'] * 2 * params['block'], 1))
error = np.sum(d != d_hat)
errors[sigma_index][trials_index] = error
for trials_index in range(params['SNR_AVERAGE']):
h_si = m.channel(1, params['chanel_len'])
Hc = ofdm.circulant_channel(h_si.T, params['chanel_len'],
params['subcarrier'])
D = F @ Hc @ FH
D_1 = np.linalg.inv(D)
for sigma_index, sigma in enumerate(sigmas):
d = np.random.choice([0, 1],
(params['subcarrier'] * 2 * params['block'], 1))
s_n = m.modulate_qpsk(d)
s = s_n.reshape(params['subcarrier'], params['block'])
x = np.matmul(FH, s)
noise = m.awgn((params['subcarrier'], params['block']), sigma)
r = np.matmul(Hc, x) + noise
y = np.matmul(F, r)
s_hat = np.matmul(D_1, y)
s_n_hat = s_hat.reshape(params['subcarrier'] * params['block'])
d_hat = m.demodulate_qpsk(s_n_hat).reshape(
(params['subcarrier'] * 2 * params['block'], 1))
error = np.sum(d != d_hat)
errors[sigma_index][trials_index] = error
### QPSK
q_r = (h_si * s_n) + m.awgn(
(params['subcarrier'] * params['block'], 1), sigma)
def __init__(self,
n,
sigma,
gamma=0.0,
phi=0.0,
PA_IBO_dB=5,
PA_rho=2,
LNA_IBO_dB=5,
LNA_rho=2,
h_si_list=None,
h_s_list=None,
h_si_len=1,
h_s_len=1,
receive_antenna=1,
tx_iqi=True,
pa=True,
lna=True,
rx_iqi=True):
# 送信信号
self.d = np.random.choice([0, 1], n)
self.x = m.modulate_qpsk(self.d)
# 希望信号
self.d_s = np.random.choice([0, 1], n)
self.s = m.modulate_qpsk(self.d_s)
# 送信側非線形
if tx_iqi == True:
self.x_iq = m.iq_imbalance(self.x, gamma, phi)
else:
self.x_iq = self.x
if pa == True:
self.x_pa = m.sspa_rapp_ibo(self.x_iq, PA_IBO_dB, PA_rho)
else:
self.x_pa = self.x_iq
# 通信路
# 通信路がランダムの場合
if h_si_list is None:
h_si_list = [m.channel(size=self.x_pa.size)]
if h_s_list is None:
h_s_list = [m.channel(size=self.s.size)]
self.h_si_list = h_si_list
self.h_s_list = h_s_list
x_len = self.x_pa.size - h_si_len + 1 # 周波数選択性の場合，時間のずれを考慮すると長さがnではなくなる
self.y = np.zeros((x_len, receive_antenna), dtype=complex)
for i, (h_si, h_s) in enumerate(zip(h_si_list, h_s_list)):
chanels_x_pa = np.array([
self.x_pa[i:i + h_si_len]
for i in range(self.x_pa.size - h_si_len + 1)
]) # [[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_y_si = h_si * chanels_x_pa
y_si = np.sum(chanels_y_si, axis=1)
chanels_s = np.array([
self.s[i:i + h_s_len] for i in range(self.s.size - h_s_len + 1)
]) # [[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_si + y_s + m.awgn(y_s.shape, sigma)
# 受信側非線形
if lna == True:
y_lna = m.sspa_rapp_ibo(r, LNA_IBO_dB, LNA_rho).squeeze()
else:
y_lna = r
if rx_iqi == True:
y_iq = m.iq_imbalance(y_lna, gamma, phi)
else:
y_iq = y_lna
self.y[:, i] = y_iq
