简单的盲信道估计基于QPSK源码程序

% CHANNEL EQUALIZATION USING LMS 
 clc;
 clear all;
 close all;
 M=3000;    % number of data samples
 T=2000;    % number of training symbols
 dB=25;     % SNR in dB valueL=20; % length for smoothing(L+1)
 ChL=5;  % length of the channel(ChL+1)
 EqD=round((L+ChL)/2);  %delay for equalizationCh=randn(1,ChL+1)+sqrt(-1)*randn(1,ChL+1);   % complex channel
 Ch=Ch/norm(Ch);                     % scale the channel with normTxS=round(rand(1,M))*2-1;  % QPSK transmitted sequence
 TxS=TxS+sqrt(-1)*(round(rand(1,M))*2-1); x=filter(Ch,1,TxS);  %channel distortion
 n=randn(1,M);  %+sqrt(-1)*randn(1,M);   %Additive white gaussian noise
  n=n/norm(n)*10^(-dB/20)*norm(x);  % scale the noise power in accordance with SNR
 x=x+n;                           % received noisy signalK=M-L;   %% Discarding several starting samples for avoiding 0's and negative
 X=zeros(L+1,K);  % each vector column is a sample
 for i=1:K
     X(:,i)=x(i+L:-1:i).';
 end%adaptive LMS Equalizer
 e=zeros(1,T-10);  % initial error
 c=zeros(L+1,1);   % initial condition
 mu=0.001;        % step size
 for i=1:T-10
     e(i)=TxS(i+10+L-EqD)-c'*X(:,i+10);   % instant error
     c=c+mu*conj(e(i))*X(:,i+10);           % update filter or equalizer coefficient
 endsb=c'*X;   % recieved symbol estimation
%SER(decision part)
 sb1=sb/norm(c);  % normalize the output
 sb1=sign(real(sb1))+sqrt(-1)*sign(imag(sb1));  %symbol detection
 start=7;  
 sb2=sb1-TxS(start+1:start+length(sb1));  % error detection
 SER=length(find(sb2~=0))/length(sb2); %  SER calculation
 disp(SER);% plot of transmitted symbols
     subplot(2,2,1), 
     plot(TxS,'*');   
     grid,title('Input symbols');  xlabel('real part'),ylabel('imaginary part')
     axis([-2 2 -2 2])
     
 % plot of received symbols
     subplot(2,2,2),
     plot(x,'o');
     grid, title('Received samples');  xlabel('real part'), ylabel('imaginary part')% plots of the equalized symbols    
     subplot(2,2,3),
     plot(sb,'o');   
     grid, title('Equalized symbols'), xlabel('real part'), ylabel('imaginary part')% convergence
     subplot(2,2,4),
     plot(abs(e));   
     grid, title('Convergence'), xlabel('n'), ylabel('error signal')
     %%
     
     %IMPLEMENTATION OF BLIND CHANNEL USING CMA OR GODARD ALGORITHM IMPLEMENTEDclc;
 clear all;
 close all;
 N=3000;    % number of sample data
 dB=25;     % Signal to noise ratio(dB)L=20; % smoothing length L+1
 ChL=1;  % length of the channel= ChL+1
 EqD=round((L+ChL)/2);  %  channel equalization delayi=sqrt(-1);
 %Ch=randn(1,ChL+1)+sqrt(-1)*randn(1,ChL+1);   % complex channel%Ch=[0.0545+j*0.05 .2832-.1197*j -.7676+.2788*j -.0641-.0576*j .0566-.2275*j .4063-.0739*j];
 Ch=[0.8+i*0.1 .9-i*0.2]; %complex channel
     Ch=Ch/norm(Ch);% normalize
 TxS=round(rand(1,N))*2-1;  % QPSK symbols are transmitted symbols
 TxS=TxS+sqrt(-1)*(round(rand(1,N))*2-1);
 x=filter(Ch,1,TxS); %channel distortionn=randn(1,N)+sqrt(-1)*randn(1,N);   % additive white gaussian noise (complex)
  n=n/norm(n)*10^(-dB/20)*norm(x);  % scale noise power
 x1=x+n;  % received noisy signal%estimation using CMA
 K=N-L;   %% Discard initial samples for avoiding 0's and negative
 X=zeros(L+1,K);  %each vector
 for i=1:K
     X(:,i)=x1(i+L:-1:i).';
 ende=zeros(1,K);  % to store the error signal
 c=zeros(L+1,1); c(EqD)=1;    % initial condition
 R2=2;                  % constant modulous of QPSK symbols
 mu=0.001;      % step size
 for i=1:K
    e(i)=abs(c'*X(:,i))^2-R2;                  % initial error
    c=c-mu*2*e(i)*X(:,i)*X(:,i)'*c;     % update equalizer co-efficients
    c(EqD)=1;
 end   
    
 sym=c'*X;   % symbol estimation
 %calculate SER
 H=zeros(L+1,L+ChL+1); for i=1:L+1, H(i,i:i+ChL)=Ch; end  % channel matrix
 fh=c'*H; % channel equalizer
 temp=find(abs(fh)==max(abs(fh))); %find maximumsb1=sym/(fh(temp));  % normalize the output
  sb1=sign(real(sb1))+sqrt(-1)*sign(imag(sb1));  % perform symbol detection
 strt=6;  
 sb2=sb1-TxS(strt+1:strt+length(sb1));  % detecting error symbols
 SER=length(find(sb2~=0))/length(sb2);% SER calculations
 disp(SER);% plot of transmitted bits
     subplot(2,2,1), 
     plot(TxS,'*');
     grid on,title('Transmitted bits');  xlabel('real'),ylabel('imaginary')
     axis([-3 3 -3 3])
     
 % plot of received symbols  
     subplot(2,2,2),
     plot(x1,'o');
     grid on, title('Received symbols');  xlabel('real'), ylabel('imaginary') % plot of the equalized symbols
     subplot(2,2,3),
     plot(sym,'o');
     grid on, title('After Equalization'), xlabel('real'), ylabel('imaginary')% convergence of algorithm
     subplot(2,2,4),
     plot(abs(e));   
     grid on, title('Convergence'), xlabel('n'), ylabel('error signal');
     axis([0 2000 0 4]);