OFDM.zip_16QAM matlab_16qam_OFDM BER_OFDM信号_OFDM信号产生‘

% OFDM Code % Author: Ihsan Ullah, % Ms-55 Electrical, % College of EME, % NUST Pakistan % No.of Carriers: 64 % coding used: Convolutional coding % Single frame size: 96 bits % Total no. of Frames: 100 % Modulation: 16-QAM % No. of Pilots: 4 % Cylic Extension: 25%(16) close all clear all clc %% % Generating and coding data t_data=randint(9600,1)'; x=1; si=1; %for BER rows %% for d=1:100; data=t_data(x:x+95); x=x+96; k=3; n=6; s1=size(data,2); % Size of input matrix j=s1/k; %% % Convolutionally encoding data constlen=7; codegen = [171 133]; % Polynomial trellis = poly2trellis(constlen, codegen); codedata = convenc(data, trellis); %% %Interleaving coded data s2=size(codedata,2); j=s2/4; matrix=reshape(codedata,j,4); intlvddata = matintrlv(matrix',2,2)'; % Interleave. intlvddata=intlvddata'; %% % Binary to decimal conversion dec=bi2de(intlvddata','left-msb'); %% %16-QAM Modulation M=64; y = qammod(dec,M); % scatterplot(y); %% % Pilot insertion lendata=length(y); pilt=3+3j; nofpits=4; k=1; for i=(1:13:52) pilt_data1(i)=pilt; for j=(i+1:i+12); pilt_data1(j)=y(k); k=k+1; end end pilt_data1=pilt_data1'; % size of pilt_data =52 pilt_data(1:52)=pilt_data1(1:52); % upsizing to 64 pilt_data(13:64)=pilt_data1(1:52); % upsizing to 64 for i=1:52 pilt_data(i+6)=pilt_data1(i); end %% % IFFT ifft_sig=ifft(pilt_data',64); %% % Adding Cyclic Extension cext_data=zeros(80,1); cext_data(1:16)=ifft_sig(49:64); for i=1:64 cext_data(i+16)=ifft_sig(i); end %% % Channel % SNR o=1; for snr=0:2:50 ofdm_sig=awgn(cext_data,snr,'measured'); % Adding white Gaussian Noise % figure; % index=1:80; % plot(index,cext_data,'b',index,ofdm_sig,'r'); %plot both signals % legend('Original Signal to be Transmitted','Signal with AWGN'); %% % RECEIVER %% %Removing Cyclic Extension for i=1:64 rxed_sig(i)=ofdm_sig(i+16); end %% % FFT ff_sig=fft(rxed_sig,64); %% % Pilot Synch%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% for i=1:52 synched_sig1(i)=ff_sig(i+6); end k=1; for i=(1:13:52) for j=(i+1:i+12); synched_sig(k)=synched_sig1(j); k=k+1; end end % scatterplot(synched_sig) %% % Demodulation dem_data= qamdemod(synched_sig,16); %% % Decimal to binary conversion bin=de2bi(dem_data','left-msb'); bin=bin'; %% % De-Interleaving deintlvddata = matdeintrlv(bin,2,2); % De-Interleave deintlvddata=deintlvddata'; deintlvddata=deintlvddata(:)'; %% %Decoding data n=6; k=3; decodedata =vitdec(deintlvddata,trellis,5,'trunc','hard'); % decoding datausing veterbi decoder rxed_data=decodedata; %% % Calculating BER rxed_data=rxed_data(:)'; errors=0; c=xor(data,rxed_data); errors=nnz(c); % for i=1:length(data) % % % if rxed_data(i)~=data(i); % errors=errors+1; % % end % end BER(si,o)=errors/length(data); o=o+1; end % SNR loop ends here si=si+1; end % main data loop %% % Time averaging for optimum results for col=1:25; %%%change if SNR loop Changed ber(1,col)=0; for row=1:100; ber(1,col)=ber(1,col)+BER(row,col); end end ber=ber./100; %% figure i=0:2:48; semilogy(i,ber); title('BER vs SNR'); ylabel('BER'); xlabel('SNR (dB)'); grid on