simleofd_hwsnr.m

基于matlab实现OFDM系统 包括信号时域频域仿真 误码率分析

yifft=[];
xifft=[];
%ranges of Variables
num_symbol=100;%number of symbol,1k OFDM
N_OFDM=1024;%set 1K OFDM
nsamp=1;
M_print=4;%while ((M==M_print)*(EbNo==EbNo_print)) print some produre curve 
EbNo_print=5;%while ((M==M_print)*(EbNo==EbNo_print)) print some produre curve
Mvec=[4 8 16];%Values of M PSK
EbNovec=[0:10];%Values of EbNo to consider
%Preallocate space for results
number_of_errors=zeros(length(Mvec),length(EbNovec));
sym_error_rate=zeros(length(Mvec),length(EbNovec));

for idxM=1:length(Mvec)
    for idxEbNo=1:length(EbNovec)
      M=Mvec(idxM);%size of signal constellation,QPSK
      EbNo=EbNovec(idxEbNo);%set SNR; dB
      K=log2(M);%number of bits per symbol
      data_0=(N_OFDM*K)*num_symbol; %number of total bits for processing
      %signal source,first create a binary data stream with 8000bits as 
      % colomn vector.
      x=randint(data_0,1);%random binary data stream
      %mapping into I-Q constellation
      data_1= bi2de(reshape(x,K,length(x)/K).','left-msb');%produce 2bit symbol for modulating
      data_21=pskmod(data_1,M,pi/M);%qpsk modulating,initial phase is pi/4
      data_2=reshape(data_21,N_OFDM,100);%scatter the modulated signal vector to 1024rows and 100colomns matrix
      %serial to parallel
      data_3=data_2;
      %IFFT
      data_4=ifft(data_3);
      %GI add
      data_5=[data_4(N_OFDM,:);data_4];
      %Add noise
      ytx=data_5;% ytx is the transmitted signal;
      %send signal over an AWGN channel
      snr=EbNo+10*log10(M)-10*log10(nsamp);%convert EbNo value to the corresponding signal-to noise ratio(SNR)
      ynoisy=awgn(ytx,snr,'measured');
      yrx=ynoisy;%yrx is the received signal;
      %GI remove
      data_6=yrx([2:(N_OFDM+1)],:);
      %FFT
      data_7=fft(data_6);
      %received singal demodulate
      data_8=reshape(data_7,N_OFDM*num_symbol,1);
      data_demod=pskdemod(data_8,M,pi/M);
        while (M==4)&&(EbNo==5)
            %figure(1);
            %stem(x(1:40),'filled','--');
            %title('Random Bits transmitted');
           % xlabel('Bit Index'); ylabel('Binary Value');

            %figure(2);
            %stem(data_1(10240:10260),'filled','--');
            %title('Random symbols transmitted');
            %xlabel('Bit Index'); ylabel('Binary Value');

          figure(3);
            map=scatterplot(data_21);%scatter and display in constellation
            axis([-3 3 -3 3]);
            title('I-Q constellation');
            xlabel('I'); ylabel('Q');

            figure(4);
             %for j=1:100
             for i=1:N_OFDM
               yifft=[yifft abs(data_4(i,1))];
               xifft=[xifft i];
             end
            %end
            plot(xifft,yifft,'--');

            %figure(5);
            map1=scatterplot(data_8);
            axis([-3 3 -3 3]);
            title('I-Q constellation');
            xlabel('I'); ylabel('Q');

            figure(6);
            stem(data_demod(10240:10260),'filled','--');
            title('Random symbols demodulated');
            xlabel('Bit Index'); ylabel('Binary Value');
            break, 
        end
      
      [number_of_errors(idxM,idxEbNo),sym_error_rate(idxM,idxEbNo)]=symerr(data_1,data_demod);
    end% End of loop over EbNo values
   %% Plot a Curve.
   figure(7);
   markerchoice = '.xo*';
   plotsym = [markerchoice(idxM) '-']; % Plotting style for this curve
   semilogy(EbNovec,sym_error_rate(idxM,:),plotsym); % Plot one curve.
   drawnow; % Update the plot instead of waiting until the end.
   hold on; % Make sure next iteration does not remove this curve.

end% End of loop over M values
%% Complete the plot.
title('Performance of M-PSK for Varying M');
xlabel('EbNo (dB)'); ylabel('BER');
legend('M = 4','M = 8','M = 16','M = 32',...
   'Location','SouthWest');