www.pudn.com > dss.zip > ss_pe94.m

function [p]=ss_Pe94(snr_in_dB, Lc, A, w0) 
% [p]=ss_Pe94(snr_in_dB, Lc, A, w0) 
%		SS_PE94  finds the measured error rate. The function  
%   		that returns the measured probability of error for the given value of  
%   		the snr_in_dB, Lc, A and w0. 
snr=10^(snr_in_dB/10); 
sgma=1;		      			% Noise standard deviation is fixed. 
Eb=2*sgma^2*snr;      			% signal level required to achieve the given  
		      			% signal-to-noise ratio 
E_chip=Eb/Lc;	      			% energy per chip 
N=10000;	      			% number of bits transmitted 
% The generation of the data, noise, interference, decoding process and error 
% counting is performed all together in order to decrease the run time of the  
% program. This is accomplished by avoiding very large sized vectors. 
num_of_err=0;		 
for i=1:N, 
  % Generate the next data bit. 
  temp=rand;	 
  if (temp<0.5), 
    data=-1; 
  else 
    data=1; 
  end; 
  % Repeat it Lc times, i.e. divide it into chips. 
  for j=1:Lc, 
    repeated_data(j)=data; 
  end; 
  % pn sequence for the duration of the bit is generated next 
  for j=1:Lc, 
    temp=rand; 
    if (temp<0.5), 
      pn_seq(j)=-1; 
    else 
      pn_seq(j)=1; 
    end; 
  end; 
  % the transmitted signal is 
  trans_sig=sqrt(E_chip)*repeated_data.*pn_seq; 
  % AWGN with variance sgma^2 
  noise=sgma*randn(1,Lc); 
  % interference  
  n=(i-1)*Lc+1:i*Lc; 
  interference=A*sin(w0*n); 
  % received signal 
  rec_sig=trans_sig+noise+interference; 
  % Determine the decision variable from the received signal. 
  temp=rec_sig.*pn_seq; 
  decision_variable=sum(temp); 
  % making decision 
  if (decision_variable<0), 
    decision=-1; 
  else 
    decision=1; 
  end; 
  % If it is an error, increment the error counter. 
  if (decision~=data), 
    num_of_err=num_of_err+1; 
  end; 
end; 
% then the measured error probability is 
p=num_of_err/N;