Hi Adriane,
To implement Monte Carlo (MC) method, an outer loop for fixed number of ‘MonteCarloTrials’ can be applied over the provided code.
The outer loop on ‘MonteCarloTrails’ is used for the following reasons:
- For each trail, the MC method generates random bits and noise transmitted and received.
- At the end, for all the trails, error is averaged out for reliable estimation of ‘BER (Bit Error Rate)’.
Also, to store the errors for each value ‘nErr’ is utilized.
Refer to the implementation below for better understanding. The ‘nErr’ variable is used to store the errors for each iteration:
nErr = zeros(length(Eb_N0_dB), 1);
for trial = 1:MonteCarloTrials
% Generate random bits (matrix of binary variables)
Bits_ale = randi([0 M-1], 1, N);
% BPSK modulation: 0 -> -1, 1 -> 1
Bits_bpsk = 2 * Bits_ale - 1;
% Generate random watermark bits
Bit_wat = randi([0 M-1], 1, N);
Theta = pi/8;
b = 1/sqrt(2)*(randn(1, N) + 1i*randn(1, N));
% Preallocate transmitted bits
Bit_enviado = zeros(1, N);
% Apply watermark modulation
for k = 1:N
%similar to original code
end
% Loop over each Eb/N0 value
for iter = 1:length(Eb_N0_dB)
%similar to original code
end
end
% Calculate the average error rate over all trials
simBer = nErr / (N * MonteCarloTrials);
% Theoretical BER for BPSK (without watermarking)
theoryBer = 0.5 * erfc(sqrt(10.^(Eb_N0_dB/10)));
Refer to the attached output graph for reference.
I hope this works.
