【信号处理】python按原理实现BPSK、QPSK、QAM信号调制

迷南。 2022-11-11 13:27 1070阅读 0赞

# 完整的使用不涉及原理的包的使用请转到我的另一篇文章 #

[【信号处理】Python实现2PSK、QPSK、8PSK、N-QAM的调制和解调][Python_2PSK_QPSK_8PSK_N-QAM]

# 1 BPSK调制 #

# -*- coding:utf-8 -*-
     
    import numpy as np
    from math import pi
    import matplotlib.pyplot as plt
    import matplotlib
    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(2, 1, 1)
     
    ax1.set_title('generate Random Binary signal', 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(2, 1, 2)
    ax2.set_title('BPSK Modulation', fontsize=20)#, fontproperties=zhfont1
    plt.axis([0,size,-1.5, 1.5])
    plt.plot(t, bpsk, 'r')
    plt.show()
    print()

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# 2 QPSK #

from scipy import signal, special
    import numpy as np
    import matplotlib.pyplot as plt
    import matplotlib.ticker as mticker 
    from matplotlib.font_manager import FontProperties
    
    
    t = np.arange(0,8.5,0.5)
    # input
    plt.subplot(4,1,1)
    y1 = [0,1,1,0,1,1,0,0,0,1,1,0,1,1,0,0,0]
    plt.plot(t,y1,drawstyle='steps-post')
    plt.xlim(0,8)
    plt.ylim(-0.5,1.5)
    plt.title('Input Signal')
    
    # I Signal
    plt.subplot(4,1,2)
    a = 1/np.sqrt(2)
    tI = np.arange(0,9,1)
    yI = [-a,a,-a,a,-a,a,-a,a,a]
    plt.plot(tI,yI,drawstyle='steps-post')
    plt.xlim(0,8)
    plt.ylim(-2,2)
    plt.title('I signal')
    
    # Q signal
    plt.subplot(4,1,3)
    yQ = [a,-a,-a,a,a,-a,-a,a,a]
    plt.plot(tI,yQ,drawstyle='steps-post')
    plt.xlim(0,8)
    plt.ylim(-1,1)
    plt.title('Q Signal')
    
    # QPSK signal
    plt.subplot(4,1,4)
    t = np.arange(0,9.,0.01)
    def outputwave(I,Q,t):
        rectwav = []
        for i in range(len(I)):
            t_tmp = t[((i)*100):((i+1)*100)]
            yI_tmp = yI[i]*np.ones(100)
            yQ_tmp = yQ[i]*np.ones(100)
            wav_tmp = yI_tmp*np.cos(2*np.pi*5*t_tmp)-yQ_tmp*np.sin(2*np.pi*5*t_tmp)
            rectwav.append(wav_tmp)
        return rectwav
    rectwav = outputwave(yI,yQ,t)
    plt.plot(t,np.array(rectwav).flatten(),'r')
    plt.xlim(0,8)
    plt.ylim(-2,2)
    plt.title('QPSK Signal')
    
    plt.tight_layout()
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzkzNTY5Ng_size_16_color_FFFFFF_t_70_pic_center 1]

# 3 QAM #

# 4 参考资料 #

1.  [python实现BPSK调制信号解调][python_BPSK]
2.  [深入浅出通信原理Python代码版][Python]
3.  [Python实现M-QAM][Python_M-QAM]

[Python_2PSK_QPSK_8PSK_N-QAM]: https://betterbench.blog.csdn.net/article/details/120596442
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzkzNTY5Ng_size_16_color_FFFFFF_t_70_pic_center]: /images/20221022/925014117c6f46728975b4962fcd4e21.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MzkzNTY5Ng_size_16_color_FFFFFF_t_70_pic_center 1]: /images/20221022/70061783859b43798334914a3c51f09e.png
[python_BPSK]: https://www.pianshen.com/article/796926952/
[Python]: https://github.com/weidongz/Rubbish_Code/blob/master/Understanding_Communication_Theroy/Blog_22-40.md
[Python_M-QAM]: https://blog.csdn.net/qq_35154082/article/details/106933760