From a00dde9ad487333d9ad8097f3daa615d583cc165 Mon Sep 17 00:00:00 2001
From: Adog64 <amsharpe102@pm.me>
Date: Wed, 20 Nov 2024 14:49:04 -0500
Subject: [PATCH] AM synthesis code

---
 7th-Semester-Fall-2024/ECOMMS/labs/lab2/am.py | 35 ++++++++++++++++---
 1 file changed, 31 insertions(+), 4 deletions(-)

diff --git a/7th-Semester-Fall-2024/ECOMMS/labs/lab2/am.py b/7th-Semester-Fall-2024/ECOMMS/labs/lab2/am.py
index dcabfc9..8ecd351 100644
--- a/7th-Semester-Fall-2024/ECOMMS/labs/lab2/am.py
+++ b/7th-Semester-Fall-2024/ECOMMS/labs/lab2/am.py
@@ -2,6 +2,17 @@ import numpy as np
 import matplotlib.pyplot as plt
 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):
     nyq = 0.5*f_s
@@ -12,13 +23,12 @@ def lowpass(data, f_cutoff, f_s):
 
 def dsb_am(m, f_c, t):
     omega_c = 2*np.pi*f_c
-    c = np.cos(omega_c*t)
-    return 0.5*m*c + c
+    return (1 + m)*np.cos(omega_c*t)
 
 def dsb_sc(m, f_c, t):
     omega_c = 2*np.pi*f_c
     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):
     omega_c = 2*np.pi*f_c
@@ -37,9 +47,18 @@ def main():
     t = np.arange(0,1,T_s)
     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_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)
 
@@ -49,5 +68,13 @@ def main():
     plt.legend(loc="upper right")
     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__":
     main()