众所周知，matlab中信号处理的工具箱十分强大，但因为一些深度学习实时性的需求，博主不得不转至python，下面是用python编写的一个完整的测试；实现了BPSK的相干解调，门限使用积分判决。

源码：

# -*- coding:utf-8 -*-

import numpy as np

from math import pi

import matplotlib.pyplot as plt

import matplotlib

import scipy.signal as signal

import math

#码元数

size = 10

sampling_t = 0.01

t = np.arange(0, size, sampling_t)

# 随机生成信号序列

a = np.random.randint(0, 2, size)

m = np.zeros(len(t), dtype=np.float32)

for i in range(len(t)):

m[i] = a[math.floor(t[i])]

fig = plt.figure()

ax1 = fig.add_subplot(3, 1, 1)

# 解决set_title中文乱码

zhfont1 = matplotlib.font_manager.FontProperties(fname = 'C:\Windows\Fonts\simsun.ttc')

ax1.set_title('产生随机n位二进制信号', fontproperties = zhfont1, fontsize = 20)

plt.axis([0, size, -0.5, 1.5])

plt.plot(t, m, 'b')

fc = 4000

fs = 20 * fc # 采样频率

ts = np.arange(0, (100 * size) / fs, 1 / fs)

coherent_carrier = np.cos(np.dot(2 * pi * fc, ts))

bpsk = np.cos(np.dot(2 * pi * fc, ts) + pi * (m - 1) + pi / 4)

# BPSK调制信号波形

ax2 = fig.add_subplot(3, 1, 2)

ax2.set_title('BPSK调制信号', fontproperties=zhfont1, fontsize=20)

plt.axis([0,size,-1.5, 1.5])

plt.plot(t, bpsk, 'r')

# 定义加性高斯白噪声

def awgn(y, snr):

snr = 10 ** (snr / 10.0)

xpower = np.sum(y ** 2) / len(y)

npower = xpower / snr

return np.random.randn(len(y)) * np.sqrt(npower) + y

# 加AWGN噪声

noise_bpsk = awgn(bpsk, 5)

# BPSK调制信号叠加噪声波形

ax3 = fig.add_subplot(3, 1, 3)

ax3.set_title('BPSK调制信号叠加噪声波形', fontproperties = zhfont1, fontsize = 20)

plt.axis([0, size, -1.5, 1.5])

plt.plot(t, noise_bpsk, 'r')

# 带通椭圆滤波器设计，通带为[2000，6000]

[b11,a11] = signal.ellip(5, 0.5, 60, [2000 * 2 / 80000, 6000 * 2 / 80000], btype = 'bandpass', analog = False, output = 'ba')

# 低通滤波器设计，通带截止频率为2000Hz

[b12,a12] = signal.ellip(5, 0.5, 60, (2000 * 2 / 80000), btype = 'lowpass', analog = False, output = 'ba')

# 通过带通滤波器滤除带外噪声

bandpass_out = signal.filtfilt(b11, a11, noise_bpsk)

# 相干解调,乘以同频同相的相干载波

coherent_demod = bandpass_out * (coherent_carrier * 2)

# 通过低通滤波器

lowpass_out = signal.filtfilt(b12, a12, coherent_demod)

fig2 = plt.figure()

bx1 = fig2.add_subplot(3, 1, 1)

bx1.set_title('本地载波下变频，经低通滤波器后', fontproperties = zhfont1, fontsize=20)

plt.axis([0, size, -1.5, 1.5])

plt.plot(t, lowpass_out, 'r')

#抽样判决

detection_bpsk = np.zeros(len(t), dtype=np.float32)

flag = np.zeros(size, dtype=np.float32)

for i in range(10):

tempF = 0

for j in range(100):

tempF = tempF + lowpass_out[i * 100 + j]

if tempF > 0:

flag[i] = 1

else:

flag[i] = 0

for i in range(size):

if flag[i] == 0:

for j in range(100):

detection_bpsk[i * 100 + j] = 0

else:

for j in range(100):

detection_bpsk[i * 100 + j] = 1

bx2 = fig2.add_subplot(3, 1, 2)

bx2.set_title('BPSK信号抽样判决后的信号', fontproperties = zhfont1, fontsize=20)

plt.axis([0, size, -0.5, 1.5])

plt.plot(t, detection_bpsk, 'r')

plt.show()

显示结果：