BPSK.py

#!/usr/bin/env python3

import matplotlib.pyplot as plt
import numpy as np

# from scipy.signal import periodogram
from scipy.stats import norm

# Carrier signal
# f_c = 100.0
# t_c = 1.0 / f_c

# Sampling rate
# f_s = 10000.0
# t_s = 1.0 / f_s

# BPSK Parameters
# Tb = 0.01
# Eb = 0.001


def modulate(msg, Eb, Tb, f_c, f_s):
    modulated_signal = []
    t = np.linspace(0, Tb, int(Tb * f_s))
    for i in msg:
        s = np.sqrt(2 * Eb / Tb) * np.sin(2 * np.pi * f_c * t)
        if i == 0:
            s = -s
        modulated_signal.extend(s)
    t = np.linspace(0, len(msg) * Tb, int(len(msg) * Tb * f_s))
    modulated_signal = np.array(modulated_signal)
    return modulated_signal


# def add_noise(signal, N0):
#     N0_unit_power = 0.0004
#     ns = len(signal)
#     noise = np.random.normal(size=ns)
#     noise *= np.sqrt(N0/N0_unit_power)
#     f, psd = periodogram(noise, f_s)
#     psd_av = np.mean(psd)
#     # N0 = 2*psd_av
#     signal_with_noise = signal + noise
#     return signal_with_noise


def demodulate(signal, Tb, f_c, f_s):
    t = np.linspace(0, Tb, int(Tb * f_s))
    phi = np.sqrt(2 / Tb) * np.sin(2 * np.pi * f_c * t)
    N = len(signal) // len(t)
    signal = np.array_split(signal, N)
    received_msg = []
    for i in signal:
        x = i * phi
        sm = x.sum() / f_s
        if sm > 0:
            received_msg.append(1)
        else:
            received_msg.append(0)
    return received_msg


def error_probabilities(msg, decoded_msg, Eb, N0):
    Pb = norm.sf(np.sqrt(2 * Eb / N0))
    # print('Theoretical Bit Error Probability:', Pb)
    Pb_pr = np.count_nonzero(np.array(msg) != np.array(decoded_msg)) / len(msg)
    # print('Practical Bit Error Probability:', Pb_pr)
    return Pb, Pb_pr