def test_seq(signal, N):
snrindB_range = range(1, 11)
itr = len(snrindB_range)
ber = [None] * itr
for n in range(0, itr):
snrindB = snrindB_range[n]
snr = 10.0**(snrindB / 10.0)
# sigma^2 = N0/2
noise_std = 1 / sqrt(2 * snr)
noise_mean = 0
no_errors = 0
# ВЫПОЛНЯЕМ N испітаний по отправке сигнала размером bit_length
for m in range(0, N):
noise = generate_noise(noise_mean, noise_std, len(signal))
rx_sig = received_sig(signal, noise)
det_sig = detected_sig(rx_sig)
no_errors += err(det_sig, signal)
ber[n] = no_errors / (N * len(signal))
print_info(snrindB, no_errors, ber[n])
print(ber)
plot.build_BER(snrindB_range, ber)
def test_seq_symbol2(signal):
snrindB_range = range(0, 10)
itr = len(snrindB_range)
ber = [None] * itr
N = 20000
sig = signal
# cds = CDS(N)
# sig = cds.table[5]
for n in range(0, itr):
snrindB = snrindB_range[n]
# linear snr
snr = 10.0**(snrindB / 10.0)
noise_std = 1 / sqrt(2 * snr)
noise_mean = 0
no_errors = 0
# Передаем cигнал побитово
for m in range(0, N):
# transported 4symbol(frame) of signal
tx_sig = sig[(4 * m) % len(sig):(4 * (m + 1)) % len(sig)]
noise = generate_noise(noise_mean, noise_std, len(tx_sig))
# received symbol(applying noise to transported sig)
rx_sig = received_sig(tx_sig, noise)
# detecting signal
# if rx_symbol >=0 then det_symbol = 1
# else det_symbol = -1
det_sig = detected_sig(rx_sig)
# if detected symbols not the same as transmitted then +plus error
no_errors = err(det_sig, tx_sig)
ber[n] = no_errors / (N * len(tx_sig))
print_info(snrindB, no_errors, ber[n])
print(ber)
plot.build_BER(snrindB_range, ber)
def test_seq_symbol(signal, N):
snrindB_range = range(1, 11)
itr = len(snrindB_range)
ber = [None] * itr
sig = signal
# cds = CDS(N)
# sig = cds.table[5]
for n in range(0, itr):
snrindB = snrindB_range[n]
# linear snr
snr = 10.0**(snrindB / 10.0)
noise_std = 1 / sqrt(2 * snr)
noise_mean = 0
no_errors = 0
# Передаем cигнал побитово
for m in range(0, N):
# transported symbol of signal
tx_symbol = sig[m % len(sig)]
# generating noise
noise = random.gauss(noise_mean, noise_std)
# received symbol(applying noise to transported sig)
rx_symbol = tx_symbol + noise
# detecting signal
# if rx_symbol >=0 then det_symbol = 1
# else det_symbol = -1
det_symbol = 2 * (rx_symbol >= 0) - 1
# if detected symbols not the same as transmitted then +plus error
no_errors += 1 * (tx_symbol != det_symbol)
ber[n] = no_errors / N
print_info(snrindB, no_errors, ber[n])
print(ber)
plot.build_BER(snrindB_range, ber)
def test_agwn_analytical():
f_zero = 1
w_zero = 2 * math.pi * f_zero
V = 1
T = 1 / V
Pw = 1
k = 4
N = 20
M = 10000
a = math.sqrt((8 / 5) * Pw)
x = math.floor(np.random.randint(0, 2 ** k))
print('X: ', x)
x_high = math.floor(x / 2 ** 2)
x_low = round(math.fmod(x, 2 ** 2))
print('High: {0} Low: {1}'.format(x_high, x_low))
s = lambda t: a * ((3 / 2) - x_high) * np.sin(w_zero * t) - a * ((3 / 2) - x_low) * np.cos(w_zero * t)
p_sn = []
for s_n in range(1, N):
print(s_n)
count = 0
n_zero = 1 / s_n
for i in range(0, M):
r = np.random.normal(0, math.sqrt(n_zero / 4), 10)
# fourier
noise = lambda t: r[0] / 2 + np.sum(
[r[2 * j - 1] * np.sin((2 * math.pi * j / 2 * T) * t) + r[2 * j] * np.cos((2 * math.pi * j / 2 * T) * t)
for j in range(0, 4)])
# signal with noise
st = lambda t: s(t) + noise(t)
# demodulation
function_alpha = lambda t: st(t) * np.sin(w_zero * t)
alpha = quad(function_alpha, 0, 1)
res_alpha = (2 / (T * a)) * alpha[0]
function_beta = lambda t: st(t) * np.cos(w_zero * t)
beta = quad(function_beta, 0, 1)
res_beta = (2 / (T * a)) * beta[0]
high_1 = round((3 / 2) - res_alpha)
if high_1 < 0:
high_1 = 0
elif high_1 > (2 ** k) - 1:
high_1 = (2 ** k) - 1
else:
high_1 = high_1
low_1 = round((3 / 2) + res_beta)
if low_1 < 0:
low_1 = 0
elif low_1 > (2 ** k) - 1:
low_1 = (2 ** k) - 1
else:
low_1 = low_1
x1 = high_1 * (2 ** 2) + low_1
dif = diffrence(x, x1, k)
# print(x,x1,dif)
count += dif
p_sn.append(count / (k * M))
print_info(s_n, s_n, p_sn[s_n - 1])
print(p_sn)
snr_inDb = range(0,20)
build_BER(snr_inDb,p_sn)
high_1 = 0
elif high_1>(2**k)-1:
high_1 = (2**k)-1
else:
high_1 = high_1
low_1 = round((3 / 2) + res_beta)
if low_1 < 0:
low_1 = 0
elif low_1 > (2 ** k) - 1:
low_1 = (2 ** k) - 1
else:
low_1 = low_1
x1 = high_1*(2**2)+low_1
dif = diffrence(x,x1,k)
# print(x,x1,dif)
count+=dif
p_sn.append(count/(k*M))
print_info(s_n, count, p_sn[s_n - 1])
print(p_sn)
build_BER(snr_in_db_range,p_sn)