Code-Final.zip_QPSK SNR matlab_ber vs snr_ber vs snr for qpsk_q

% Lyceum-friendly function for plotting the BER vs SNR curve of BPSK modulation in an AWGN channel. % This function will attempt to simulate the SNRs (in dB) from the set SNR_start:SNR_delta:SNR_stop. % It will stop if the BER target BER_stop is reached or if the job is killed. % On Lyceum, this will happen if you run out of time or if you use the qdel command. % On a PC, you can kill the job by pressing Ctrl-C. % The function is written so that the results obtained so far are not lost when the job is killed. % Copyright (C) 2009 Robert G. Maunder % This program is free software: you can redistribute it and/or modify it % under the terms of the GNU General Public License as published by the % Free Software Foundation, either version 3 of the License, or (at your % option) any later version. % This program is distributed in the hope that it will be useful, but % WITHOUT ANY WARRANTY; without even the implied warranty of % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General % Public License for more details. % The GNU General Public License can be seen at http://www.gnu.org/licenses/. function main_qpsk(SNR_start, SNR_delta, SNR_stop, BER_stop) % Choose a file to save the results into. filename = ['results_',num2str(SNR_start),'.mat']; % Doing the simulation frame-by-frame rather than bit-by-bit speeds it up. This is because we can use vector operations instead of longer loops. frame_length = 10000; % Setup the SNR for the first iteration of the loop. SNR_count = 1; SNR = SNR_start; BER = 1; % Loop until the job is killed or until the SNR or BER target is reached. while SNR <= SNR_stop && BER >= BER_stop % Convert from SNR (in dB) to noise power spectral density. N0 = 1/(10^(SNR/10)); % Counters to store the number of errors and bits simulated so far. error_count=0; bit_count=0; % Keep going until enough errors have been observed. This runs the simulation only as long as is required to keep the BER vs SNR curve smooth. while error_count < 100 % Generate some random bits. bits = round(rand(1,frame_length)); b_1 = bits(1:2:frame_length); b_2 = bits(2:2:frame_length); j=sqrt(-1); a = 1/sqrt(2); % amplitude symbols=a*(-2*(b_1-0.5)+j*-2*(b_2-0.5)); % QPSK symbols % Add some complex Gaussian distributed noise. received_symbols = symbols + sqrt(N0/2)*(randn(1,length(symbols))+j*randn(1,length(symbols))); % Perform BPSK demodulation. recovered_bits_r = real(received_symbols)<0; recovered_bits_i = imag(received_symbols)<0; % Accumulate the number of errors and bits that have been simulated so far. error_count = error_count + sum(recovered_bits_r ~= b_1)+sum(recovered_bits_i ~= b_2); bit_count = bit_count + frame_length; end % Calculate the BER. BER = error_count/bit_count; % Store the SNR and BER in a matrix and display it. results(SNR_count,1) = SNR; results(SNR_count,2) = BER % Save the results into binary files. This avoids the loss of precision that is associated with ASCII files. save(filename, 'results', '-MAT'); % Plot the results. This will be ignored on Lyceum. semilogy(results(:,1),results(:,2)); title('BPSK modulation in an AWGN channel'); ylabel('BER'); xlabel('SNR (in dB)'); if BER_stop ~= 0 ylim([BER_stop,1]); end if SNR_stop ~= inf xlim([SNR_start, SNR_stop]); end % Setup the SNR for the next iteration of the loop. SNR = SNR + SNR_delta; SNR_count = SNR_count + 1; end end % input : main_qpsk(2,2,12,0.00001)