smldp510.m

contemporarycommunicatonsystem－using MATLAB经典书目源程序！

function [p]=smldP510(snr_in_dB)
% [p]=smldP510(snr_in_dB)
%		SMLDP510  simulates the probability of error for the given
%   		snr_in_dB, signal to noise ratio in dB.
M=4;				   	% quarternary orthogonal signalling
E=1;
SNR=exp(snr_in_dB*log(10)/10);	   	% signal to noise ratio per bit
sgma=sqrt(E^2/(4*SNR));  	   	% sigma, standard deviation of noise
N=10000;	 		   	% number of symbols being simulated
% generation of the quarternary data source
for i=1:N,
  temp=rand;   	      			% a uniform random variable over (0,1)
  if (temp<0.25),
    dsource1(i)=0; 
    dsource2(i)=0;	      	
  elseif (temp<0.5),
    dsource1(i)=0; 
    dsource2(i)=1;	      	
  elseif (temp<0.75),
    dsource1(i)=1; 
    dsource2(i)=0;	      	
  else
    dsource1(i)=1; 
    dsource2(i)=1;
  end
end;
% detection, and probability of error calculation
numoferr=0;
for i=1:N,
  % matched filter outputs
  if ((dsource1(i)==0) & (dsource2(i)==0)),
    r0=sqrt(E)+gngauss(sgma);
    r1=gngauss(sgma);
    r2=gngauss(sgma);
    r3=gngauss(sgma);
  elseif ((dsource1(i)==0) & (dsource2(i)==1)),
    r0=gngauss(sgma);
    r1=sqrt(E)+gngauss(sgma);
    r2=gngauss(sgma);
    r3=gngauss(sgma);
  elseif ((dsource1(i)==1) & (dsource2(i)==0)),
    r0=gngauss(sgma);
    r1=gngauss(sgma);
    r2=sqrt(E)+gngauss(sgma);
    r3=gngauss(sgma);
  else
    r0=gngauss(sgma);
    r1=gngauss(sgma);
    r2=gngauss(sgma);
    r3=sqrt(E)+gngauss(sgma);
  end;
  % the detector
  max_r=max([r0 r1 r2 r3]);
  if (r0==max_r),
    decis1=0;
    decis2=0;
  elseif (r1==max_r),
    decis1=0;
    decis2=1;
  elseif (r2==max_r),
    decis1=1;
    decis2=0;
  else
    decis1=1;
    decis2=1;
  end;
  % count the number of bit errors made in this decision
  if (decis1~=dsource1(i)),   		% if it is an error, increase the error counter
    numoferr=numoferr+1;
  end;
  if (decis2~=dsource2(i)),   		% if it is an error, increase the error counter
    numoferr=numoferr+1;
  end;
end;
p=numoferr/(2*N);	       		% bit error probability estimate