BPSK.zip_climatebgn_coherent_matlab_non coherent_telecom

function [Pe, SNR] = fading_error_nc(SNR_min,SNR_max,SNR_step,errorCount) % simulates noncoherent detection for orthogonal signaling over a flat % Rayleigh fading channel % The error probability Pe is evaluated using Monte Carlo simulation for % several SNR values between SNR_min and SNR_max, with stepsize SNR_step; % SNR is measured in dB. % For each SNR value, as many transmissions are simulated as needed to % obtain at least errorCount errors. blocklength = 1000; % number of simultaneous transmissions counter = 0; % for matrix indexing for snr = SNR_min:SNR_step:SNR_max % for evey SNR value counter = counter + 1; snrLin = 10^(snr/10); % linear SNR symbolCount = 0; % number of transmitted symbols errors = 0; % number of errors so far while errors < errorCount % check number of errors symbolCount = symbolCount + blocklength; % the symbols are the rows of this matrix, each row has exactly one % "0" and one "1" sig = randerr(blocklength,2); % the noise power is normalized to one, hence the signal power is % equal to 2*snrLin, because we need to average over two time slots txSig = sqrt(2*snrLin)*sig; % the channel, normalized to unit power, i.e., no path loss h = sqrt(1/2)*(randn(blocklength, 2)+ i*randn(blocklength,2)); % complex Gaussian noise, unit variance w = 1/sqrt(2)*(randn(blocklength, 2) + i*randn(blocklength,2)); % modulate with the channel, add noise rxSig = h.* txSig + w ; % square law receiver rxStat = abs(rxSig).^2; % the noncoherent detector: check which of the two time slots % contains more energy decision = rxStat(:,1) >= rxStat(:,2); errors = errors + sum(abs(sig(:,1) - decision)); % count errors end Pe(counter) = errors / symbolCount; % estimate error probability SNR(counter) = snr; end