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;
x
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=16;
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
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