qdpsk.m

QDPSK Communication Performance simulation

% MATLAB script for QDPSK System
clear all;
SNRindB1=0:2:12;
for k=1:length(SNRindB1)
	snr_in_dB = SNRindB1(k);
	N=10000;
	Es=1;				  	% energy per symbol
	SNR=10^(snr_in_dB/10);	 	  	% signal to noise ratio
	sgma=sqrt(Es/(4*SNR));	  	  	% noise variance
	% generation of the data source follows

	source = randint(2,N);
	dsource = reshape(source,1,2*N);

	% Differential encoding of the data source follows
	mapping=[0 1 3 2];
	M=4;
	[diff_enc_output] = cm_dpske(Es,M,mapping,dsource).';

	% received signals
	n = sgma*[randn(1,N); randn(1,N)];
	r = diff_enc_output + n;
	
	% detection and the probability of error calculation
	numoferr=0;
	prev_theta=0;
	for i=1:N,
	  theta=angle(r(1,i)+j*r(2,i));
	  delta_theta=mod(theta-prev_theta,2*pi);
	  if ((delta_theta<pi/4) | (delta_theta>7*pi/4)),
		decis(:,i)=[0 0].';
	  elseif (delta_theta<3*pi/4),
		decis(:,i)=[0 1].';
	  elseif (delta_theta<5*pi/4)
		decis(:,i)=[1 1].';
	  else
		decis(:,i)=[1 0].';
	  end;
	  prev_theta=theta;
	end

	% calculate the symbol error rate
	numoferr = sum(sign(sum(decis~=source)));
	smld_err_prb(k)=numoferr/N;

end


SNRindB2=0:0.1:12;
for i=1:length(SNRindB2)
  SNR=exp(SNRindB2(i)*log(10)/10);   	% signal to noise ratio
  theo_err_prb(i)=2*Qfunct(sqrt(SNR)); 	% theoretical symbol error rate
end		
% Plotting commands follow
semilogy(SNRindB1,smld_err_prb,'*');
hold
semilogy(SNRindB2,theo_err_prb);