Rician fading channel Simulation Python code

This fading channel python code script simulates rician channel coefficients. The rician python code output plots are also mentioned.

Introduction : The term fading refers to variation in the received signal strength due to various parameters between transmitter and receiver as well as their movements. These factors or parameters include path loss, multipaths, doppler spread, shadowing effect, mobility etc. The rayleigh channel model simulates both NLOS (Non Line of Sight) and LOS (Line of Sight) components between transmitter and receiver. Refer article on fading basics and fading types >>.

Rician Channel Python code

# This python script is used to generate rician channel coefficients
import math
import matplotlib.pyplot as plt
import numpy as np
from numpy.random import standard_normal

# Sinusoidal waveform generation
t = np.linspace(1, 100, 750)
x_volts = 20*np.sin(t/(2*np.pi))
x_watts = x_volts ** 2
x_db = 10 * np.log10(x_watts)

# Rician channel coefficients

N = 1000 # Number of samples to generate
K_dB = 10 # K factor in dB
K = 10**(K_dB/10) # K factor in linear scale
mu = math.sqrt(K/(2*(K+1))) # mean
sigma = math.sqrt(1/(2*(K+1))) # sigma
h = (sigma*standard_normal(N)+mu)+1j*(sigma*standard_normal(N)+mu)
h_mag = np.abs(h)
h_mag_dB = 10*np.log10(h_mag) # convert channel response in dB

# Convolve rician channel response with sinusoidal waveform
Y4 = np.convolve(h, x_volts)

# Plots
figure, axis = plt.subplots(2, 2)
axis[0, 0].plot(x_volts)
axis[0, 0].set_title("Pure sine wave signal")
axis[0, 1].plot(h)
axis[0, 1].set_title("Rician Channel response")
axis[1, 0].plot(h_mag_dB)
axis[1, 0].set_title("Rician Channel response in dB")
axis[1, 1].plot(Y4)
axis[1, 1].set_title("Convolved sine wave signal")

Rician fading channel output plots

Following are the plots of above Rician fading channel python code.

Rician Fading Channel Python Plots

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