Question

I am supposed to do a communication systems project including MATLAB. Topic maybe something related to for example OFDM system, AMR reading using gprs etc. Or any communication systems related topic.

Can some one provide with the brief project description and MATLAB code?

Answer 1

% OFDM Code
% 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=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

% OFDM Code
% 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=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