End sem 7; week 2 winter session 2025

7th-Semester-Fall-2024/ECOMMS/labs/lab2/am.py

@@ -2,7 +2,7 @@ import numpy as np
 import matplotlib.pyplot as plt
 import scipy as sp
 
-def add_noise_snr_db(s, SNR):
+def add_noise(s, SNR):
     var_s = np.cov(s)
     var_noise = var_s/(10**(SNR/10))
     noise = var_noise**0.5 * np.random.randn(len(s))
@@ -51,14 +51,6 @@ def main():
     s_am = dsb_am(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_sc_demod = product_demod(s_sc, f_m, f_c, f_s, t)
 
@@ -66,14 +58,17 @@ def main():
     plt.plot(t, m_am_demod, label="Demodulated DSB-AM Signal")
     plt.plot(t, m_sc_demod, label="Demodulated DSB-SC Signal")
     plt.legend(loc="upper right")
+    plt.savefig("demodulation-clean.png")
     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.plot(t, m, label="Original Message Signal")
+    plt.plot(t, m_am_noisy_demod, label="Demodulated DSB-AM Signal With Noise")
+    plt.plot(t, m_sc_noisy_demod, label="Demodulated DSB-SC Signal With Noise")
+    plt.legend(loc="upper right")
+    plt.savefig("demodulation-noisy.png")
     plt.show()
 
 if __name__ == "__main__":

7th-Semester-Fall-2024/ECOMMS/labs/lab3/lab-3.py

@@ -0,0 +1,140 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import scipy as sp
+
+def add_noise(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):
+    nyq = 0.5*f_s
+    normal_cutoff = f_cutoff/nyq
+    b, a = sp.signal.butter(2, normal_cutoff, btype="low", analog=False)
+    y = sp.signal.lfilter(b, a, data)
+    return y
+
+def bandpass(data, f_pass, f_cutoff, f_s):
+    nyq = 0.5*f_s
+
+    normal_pass = f_pass/nyq
+    normal_cutoff = f_cutoff/nyq
+
+    b, a = sp.signal.butter(2, (normal_pass, normal_cutoff), btype="bandpass", analog=False)
+    y = sp.signal.lfilter(b, a, data)
+    return y
+
+def fm(m, f_c, beta_f, t):
+    omega_c = 2*np.pi*f_c
+    return np.cos(omega_c*t + beta_f*m)
+
+def product_demod(s, f_baseband, f_c, f_s, t):
+    omega_c = 2*np.pi*f_c
+    mixed = s*np.cos(omega_c*t)
+    return lowpass(mixed, f_baseband, f_s)
+
+
+def fm_demod(s_fm, f_c, f_s, beta_f, t):
+    diff_t = np.gradient(s_fm, t)
+    envelope = np.abs(sp.signal.hilbert(diff_t))
+
+    omega_c = 2*np.pi*f_c
+    dt = 1/f_s
+    m_demod = np.empty_like(envelope)
+
+    #err1 = dt*(envelope[0] + envelope[1] - 2*omega_c)/beta_f**2
+    #err2 = dt*(dm_dt[0] + dm_dt[1])/beta_f
+
+    for i in range(len(envelope)):
+        m_demod[i] = np.sum((envelope[:i+1] - omega_c)/(beta_f*f_s))
+
+    return m_demod
+
+def quad_demod(s_fm, bandwidth, f_s, f_c, t):
+    iq = real_to_iq(s_fm, f_c, bandwidth, f_s, t)
+    iq = np.conj(iq)
+    return 0.5*np.angle(iq)
+
+def real_to_iq(s_fm, f_c, bandwidth, f_s, t):
+    i = lowpass(s_fm * np.cos(2*np.pi*f_c*t), bandwidth, f_s)
+    q = lowpass(s_fm * np.sin(2*np.pi*f_c*t), bandwidth, f_s)
+    return i + 1j*q
+
+
+def m(t):
+    f_m = 10
+    omega_m = 2*np.pi*f_m
+    return 0.1*np.sin(omega_m*t)
+
+def dm_dt(t):
+    f_m = 10
+    omega_m = 2*np.pi*f_m
+    return 0.1*omega_m*np.cos(omega_m*t)
+
+def db(x):
+    return 10*np.log10(x)
+
+def main():
+    T_s = 0.0001
+    f_s = 1/T_s
+    f_m = 20
+    omega_m = 2*np.pi*f_m
+
+    f_c = 500
+
+    beta_f = 2.40483
+    
+    t = np.arange(0,1,T_s)
+    f = np.linspace(-f_s/2, f_s/2, len(t))
+
+    #m = 0.5*(np.sin(omega_m*t) + np.sin(omega_m/2*t))
+    m = np.sin(omega_m*t)
+    s_fm = fm(m, f_c, beta_f, t)
+
+    omega_c = 2*np.pi*f_c
+    
+    #plt.plot(t, m, label="Original Message Signal")
+    #plt.plot(t, quad_demod(s_fm, 2*f_m, f_s, f_c, t), label="Demodulated Message")
+    #plt.legend()
+    #plt.savefig("message-signals")
+    #plt.show()
+
+    #plt.plot(t, s_fm, label="Frequency Modulated Message")
+    #plt.legend()
+    #plt.savefig("fm-signal")
+    #plt.show()
+
+
+    spectrum = np.abs(sp.fft.fftshift(sp.fft.fft(s_fm)))**2
+    #spectrum_db = db(spectrum)
+
+    plt.subplot(121)
+    plt.plot(f, spectrum)
+    #plt.vlines(f_c, -250, 100, color='r')
+    plt.xlim(0,2*f_c)
+    plt.title("Null Carrier Spectrum")
+    plt.xlabel("Frequency [Hz]")
+
+    beta_f = 5
+    m = np.sin(omega_m*t)
+    s_fm = fm(m, f_c, beta_f, t)
+    spectrum = np.abs(sp.fft.fftshift(sp.fft.fft(s_fm)))**2
+
+    plt.subplot(122)
+    plt.title("Non-null Carrier Spectrum")
+    plt.plot(f, spectrum)
+    plt.xlim(0,2*f_c)
+    plt.xlabel("Frequency [Hz]")
+    plt.savefig("null-carrier-comp")
+
+    plt.show()
+
+if __name__ == "__main__":
+    main()