AM synthesis code

This commit is contained in:
Adog64 2024-11-20 14:49:04 -05:00
parent e97b5244ef
commit a00dde9ad4

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@ -2,6 +2,17 @@ import numpy as np
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import scipy as sp import scipy as sp
def add_noise_snr_db(s, SNR):
var_s = np.cov(s)
var_noise = var_s/(10**(SNR/10))
noise = var_noise**0.5 * np.random.randn(len(s))
return s + noise
def add_noise_db(s, noise_db):
var_noise = 10**(noise_db/10)
noise = var_noise**0.5 * np.random.randn(len(s))
return s + noise
def lowpass(data, f_cutoff, f_s): def lowpass(data, f_cutoff, f_s):
nyq = 0.5*f_s nyq = 0.5*f_s
@ -12,13 +23,12 @@ def lowpass(data, f_cutoff, f_s):
def dsb_am(m, f_c, t): def dsb_am(m, f_c, t):
omega_c = 2*np.pi*f_c omega_c = 2*np.pi*f_c
c = np.cos(omega_c*t) return (1 + m)*np.cos(omega_c*t)
return 0.5*m*c + c
def dsb_sc(m, f_c, t): def dsb_sc(m, f_c, t):
omega_c = 2*np.pi*f_c omega_c = 2*np.pi*f_c
c = np.cos(omega_c*t) c = np.cos(omega_c*t)
return 0.5*m*c return m*c
def product_demod(s, f_baseband, f_c, f_s, t): def product_demod(s, f_baseband, f_c, f_s, t):
omega_c = 2*np.pi*f_c omega_c = 2*np.pi*f_c
@ -37,9 +47,18 @@ def main():
t = np.arange(0,1,T_s) t = np.arange(0,1,T_s)
f = np.linspace(0,f_s,len(t)) f = np.linspace(0,f_s,len(t))
m = np.sin(omega_m*t) + np.sin(omega_m/2*t) m = 0.5*(np.sin(omega_m*t) + np.sin(omega_m/2*t))
s_am = dsb_am(m, f_c, t) s_am = dsb_am(m, f_c, t)
s_sc = dsb_sc(m, f_c, t) s_sc = dsb_sc(m, f_c, t)
plt.subplot(311)
plt.plot(t, m, label="Message Signal")
plt.subplot(312)
plt.plot(t, s_am, label="DSB-AM Modulated Signal")
plt.subplot(313)
plt.plot(t, s_sc, label="DSB-SC Modulated Signal")
plt.show()
m_am_demod = product_demod(s_am, f_m, f_c, f_s, t) m_am_demod = product_demod(s_am, f_m, f_c, f_s, t)
m_sc_demod = product_demod(s_sc, f_m, f_c, f_s, t) m_sc_demod = product_demod(s_sc, f_m, f_c, f_s, t)
@ -49,5 +68,13 @@ def main():
plt.legend(loc="upper right") plt.legend(loc="upper right")
plt.show() plt.show()
m_am_noisy_demod = product_demod(add_noise_snr_db(s_am, 3), f_m, f_c, f_s, t)
m_sc_noisy_demod = product_demod(add_noise_snr_db(s_sc, 3), f_m, f_c, f_s, t)
plt.plot(t, m)
plt.plot(t, m_am_noisy_demod)
plt.plot(t, m_sc_noisy_demod)
plt.show()
if __name__ == "__main__": if __name__ == "__main__":
main() main()