一、代码部分

1.2ASK调制解调

代码如下（示例）：

# -*- 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(5, 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))

ook = m * np.cos(np.dot(2 * pi * fc, ts))
# 2ASK调制信号波形
ax2 = fig.add_subplot(5, 1, 3)
ax2.set_title('2ASK调制信号', fontproperties=zhfont1, fontsize=20)
plt.axis([0, size, -1.5, 1.5])
plt.plot(t, ook, '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_ook = awgn(ook, 5)

#2ASK调制信号叠加噪声波形
ax3 = fig.add_subplot(5, 1, 5)
ax3.set_title('2ASK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_ook, '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_ook)

# 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('2ASK信号相干解调后、抽样判决前的信号', 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 > 5:
        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, 3)
bx2.set_title('2ASK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()

2.2FSK调制解调

代码如下（示例）：

# -*- 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(5, 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')

fc1 = 4000
fc2 = 10000
fs = 160000  # 采样频率
ts = np.arange(0, (100 * size) / fs, 1 / fs)
coherent_carrier1 = np.cos(np.dot(2 * pi * fc1, ts))
coherent_carrier2 = np.cos(np.dot(2 * pi * fc2, ts))

bfsk = m * coherent_carrier1 + (1 - m) * coherent_carrier2

# 2FSK调制信号波形
ax2 = fig.add_subplot(5, 1, 3)
ax2.set_title('2FSK调制信号', fontproperties=zhfont1, fontsize=20)
plt.axis([0, size, -1.5, 1.5])
plt.plot(t, bfsk, '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(bfsk, 5)

# 2FSK调制信号叠加噪声波形
ax3 = fig.add_subplot(5, 1, 5)
ax3.set_title('2FSK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 / 160000, 6000 * 2 / 160000], 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 / 160000), btype='lowpass', analog=False, output='ba')

# 通过带通滤波器滤除带外噪声
bandpass_out1 = signal.filtfilt(b11, a11, noise_bpsk)

# 相干解调,乘以同频同相的相干载波
coherent_demod1 = bandpass_out1 * (coherent_carrier1 * 2)

# 通过低通滤波器
lowpass_out1 = signal.filtfilt(b12, a12, coherent_demod1)

# 带通椭圆滤波器设计2，通带为[8000，12000]
[b21, a21] = signal.ellip(5, 0.5, 60, [8000 * 2 / 160000, 12000 * 2 / 160000], btype='bandpass', analog=False,
                          output='ba')

# 通过带通滤波器滤除带外噪声
bandpass_out2 = signal.filtfilt(b21, a21, noise_bpsk)

# 相干解调,乘以同频同相的相干载波
coherent_demod2 = bandpass_out2 * (coherent_carrier2 * 2)

# 通过低通滤波器
lowpass_out2 = signal.filtfilt(b12, a12, coherent_demod2)

fig2 = plt.figure()
bx1 = fig2.add_subplot(3, 1, 1)
bx1.set_title('2FSK信号相干解调后、抽样判决前的信号', fontproperties=zhfont1, fontsize=20)
plt.axis([0, size, -1.5, 1.5])
plt.plot(t, lowpass_out1, 'r')

# 抽样判决
detection_2fsk = 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_out1[i * 100 + j]
    if tempF > 50:
        flag[i] = 1
    else:
        flag[i] = 0
for i in range(size):
    if flag[i] == 0:
        for j in range(100):
            detection_2fsk[i * 100 + j] = 0
    else:
        for j in range(100):
            detection_2fsk[i * 100 + j] = 1

bx2 = fig2.add_subplot(3, 1, 3)
bx2.set_title('2FSK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_2fsk, 'r')
plt.show()

3.2PSK调制解调

代码如下（示例）：

# -*- 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(5, 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)

# 2PSK调制信号波形

ax2 = fig.add_subplot(5, 1, 3)
ax2.set_title('2PSK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_2psk = awgn(bpsk, 5)

# 2PSK调制信号叠加噪声波形
ax3 = fig.add_subplot(5, 1, 5)
ax3.set_title('2PSK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_2psk, '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_2psk)

# 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_2psk = 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_2psk[i * 100 + j] = 0
    else:
        for j in range(100):
            detection_2psk[i * 100 + j] = 1
bx2 = fig2.add_subplot(3, 1, 3)
bx2.set_title('2PSK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_2psk, 'r')
plt.show()

二、运行结果






总结

基于python实现了ASK,FSK和PSK的调制解调仿真。

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