81 lines
2.0 KiB
Python
81 lines
2.0 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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import scipy as sp
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def add_noise_snr_db(s, SNR):
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var_s = np.cov(s)
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var_noise = var_s/(10**(SNR/10))
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noise = var_noise**0.5 * np.random.randn(len(s))
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return s + noise
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def add_noise_db(s, noise_db):
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var_noise = 10**(noise_db/10)
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noise = var_noise**0.5 * np.random.randn(len(s))
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return s + noise
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def lowpass(data, f_cutoff, f_s):
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nyq = 0.5*f_s
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normal_cutoff = f_cutoff/nyq
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b, a = sp.signal.butter(1, normal_cutoff, btype="low", analog=False)
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y = sp.signal.lfilter(b, a, data)
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return y
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def dsb_am(m, f_c, t):
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omega_c = 2*np.pi*f_c
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return (1 + m)*np.cos(omega_c*t)
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def dsb_sc(m, f_c, t):
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omega_c = 2*np.pi*f_c
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c = np.cos(omega_c*t)
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return m*c
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def product_demod(s, f_baseband, f_c, f_s, t):
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omega_c = 2*np.pi*f_c
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mixed = s*np.cos(omega_c*t)
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return lowpass(mixed, f_baseband, f_s)
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def main():
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T_s = 0.0005
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f_s = 1/T_s
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f_m = 10
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omega_m = 2*np.pi*f_m
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f_c = 100
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t = np.arange(0,1,T_s)
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f = np.linspace(0,f_s,len(t))
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m = 0.5*(np.sin(omega_m*t) + np.sin(omega_m/2*t))
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s_am = dsb_am(m, f_c, t)
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s_sc = dsb_sc(m, f_c, t)
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plt.subplot(311)
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plt.plot(t, m, label="Message Signal")
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plt.subplot(312)
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plt.plot(t, s_am, label="DSB-AM Modulated Signal")
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plt.subplot(313)
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plt.plot(t, s_sc, label="DSB-SC Modulated Signal")
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plt.show()
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m_am_demod = product_demod(s_am, f_m, f_c, f_s, t)
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m_sc_demod = product_demod(s_sc, f_m, f_c, f_s, t)
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plt.plot(t, m, label="Original Message Signal")
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plt.plot(t, m_am_demod, label="Demodulated DSB-AM Signal")
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plt.plot(t, m_sc_demod, label="Demodulated DSB-SC Signal")
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plt.legend(loc="upper right")
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plt.show()
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m_am_noisy_demod = product_demod(add_noise_snr_db(s_am, 3), f_m, f_c, f_s, t)
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m_sc_noisy_demod = product_demod(add_noise_snr_db(s_sc, 3), f_m, f_c, f_s, t)
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plt.plot(t, m)
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plt.plot(t, m_am_noisy_demod)
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plt.plot(t, m_sc_noisy_demod)
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plt.show()
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if __name__ == "__main__":
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main()
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