blind3sub.m

这是一个基于二阶统计特性的MIMO系统盲辩识!大家多多的互相交流哦!

% subspace method for blind channel estimation
%
%
echoF=1;   % turn on/off output display
dB=15;
T=1000;    %  sample amount
L=4; M=4; N=5;  % L: antenna #. M: channel length. N: smoothing. 
d=M+N;          % d: equalization delay
j=sqrt(-1);           
mh=[-0.049+j*0.359 0.482-j*0.569 -0.556+j*0.587 1 -0.171+j*0.061; % channel
    0.443-j*0.0364 1 0.921-j*0.194 0.189-j*0.208 -0.087-j*0.054;
    -0.221-j*0.322 -0.199+j*0.918 1 -0.284-j*0.524 0.136-j*0.19;
    0.417+j*0.030 1 0.873+j*0.145 0.285+j*0.309 -0.049+j*0.161];
h=[mh(1,:) mh(2,:) mh(3,:) mh(4,:)].';


s=sign(rand(1,T)-0.5);         
s=s+sqrt(-1)*(sign(rand(1,T)-0.5));   %transmitted signals;


TN=T-N+1; X=zeros(L*N,TN); SNR=[];    %received signals
for i=1:L
  x=filter(h((i-1)*(M+1)+1:i*(M+1)),1,s);
  n=randn(size(x))+sqrt(-1)*randn(size(x));
  n=n/norm(n)*10^(-dB/20)*norm(x);
  x=x+n;  
  for j=1:TN,
    X((i-1)*N+1:i*N, j)=x(j+N-1:-1:j).';
  end
end 



  
%%%%%%%%%%%%%% subspace method 
Rx=X*X'/TN;                 % calculate correlation matrix
[U0,S0,V0]=svd(Rx);         % SVD to find null subspace

Q=zeros(L*(M+1), L*(M+1));             % Construct matrix Q
for i=d+1:L*N,
  Vm=zeros(L*(M+1), M+N);
  for j=1:(M+1),
    for k=1:L,
      Vm((k-1)*(M+1)+j, j:(j+N-1))=U0((k-1)*N+1:k*N, i).';
    end
  end
  Q=Q+Vm*Vm';
end

[U1,S1,V1]=svd(Q);          % solve equation Ah=0 by SVD

hb=U1(:,L*(M+1));           % channel estimation



%%%%%%% Compare channel estimation MSE
hb_h=mean(hb./h); 
hb_h
hb1=hb/hb_h;
squ_err_h=sqrt((h-hb1)'*(h-hb1))/sqrt(h'*h)



if echoF,          % plot channels
  subplot(211), te=length(h); 
  plot(1:te,real(hb1),'bo-',1:te,real(h),'r+'), grid
  legend('Estimated','Accurate')
  title('Real part of Channel');
  
  subplot(212),
  plot(1:te,imag(hb1),'bo-',1:te,imag(h),'r+'); grid, 
  legend('Estimated','Accurate')
  title('Imag Part of Channel')
end