codeg4.m

自适应滤波原理_西蒙.赫金_Matlab_源代码 希望能够对学习自适应滤波技术的朋友有所帮助

function [err]=codeG4(index,snr,nsymb,Mary,modtype,nRx,err);
%Routine that preforms simulations based on QAM Mary modulation
%and using coding scheme G3.
%G4 : |  x1   x2   x3   x4 |
%     | -x2   x1  -x4   x3 |
%     | -x3   x4   x1  -x2 |
%     | -x4  -x3   x2   x1 |
%     |  x1*  x2*  x3*  x4*|
%     | -x2*  x1* -x4*  x3*|
%     | -x3*  x4*  x1* -x2*|
%     | -x4* -x3*  x2*  x1*|

nTx = 4;
delay = 8;
%Generate Data
%Possible Symbol Set
Set=[0:Mary-1]';  
Smap=dmodce(Set,1,1,modtype,Mary);
%Generate Symbols
symb=randsrc(nsymb,1,[0:Mary-1]);
%Encode 
msg=dmodce(symb,1,1,modtype,Mary);
%Noise Stats
Eav=Smap'*Smap/Mary;
NF=10^(snr/10);
S=sqrt(nTx*Eav/(2*NF));
%Operate on a block by block itteration
for k=1:4:nsymb,
    Sv=msg(k:k+3);  %vector of next nTx bits
    %Make next block
    G4_1 = [Sv(1) Sv(2) Sv(3) Sv(4);
           -Sv(2) Sv(1) -Sv(4) Sv(3);
           -Sv(3) Sv(4) Sv(1) -Sv(2);
           -Sv(4) -Sv(3) Sv(2) Sv(1)];
    G4= [G4_1; conj(G4_1)];      
    noise=S*(randn(8,nRx) + i*randn(8,nRx));  %Dimension (Blocksize , nRx)
    H=(randn(nTx,nRx) + i*randn(nTx,nRx))/sqrt(2);  %Channel Gain Matrix  
    rx=G4*H + noise;  
    
    %Modification due to symbol uncertainty
    %dBdec = 10^(25/10);  %25 dB
    %H = H + sqrt(nTx*Eav/(2*dBdec))*(randn(nTx,nRx) + i*randn(nTx,nRx))/sqrt(2);
    
    %Demodulate
    S1=0; S2=0; S3=0; S4=0; Hnorm=0;
    for j=1:nRx,
        
        S1 = S1 + rx(1,j)*H(1,j)' + rx(2,j)*H(2,j)' + rx(3,j)*H(3,j)' + rx(4,j)*H(4,j)' ...
                 +rx(5,j)'*H(1,j) + rx(6,j)'*H(2,j) + rx(7,j)'*H(3,j) + rx(8,j)'*H(4,j);
        
        S2 = S2 + rx(1,j)*H(2,j)' - rx(2,j)*H(1,j)' - rx(3,j)*H(4,j)' + rx(4,j)*H(3,j)' ...
                 +rx(5,j)'*H(2,j) - rx(6,j)'*H(1,j) - rx(7,j)'*H(4,j) + rx(8,j)'*H(3,j);
        
        S3 = S3 + rx(1,j)*H(3,j)' + rx(2,j)*H(4,j)' - rx(3,j)*H(1,j)' - rx(4,j)*H(2,j)' ...
                 +rx(5,j)'*H(3,j) + rx(6,j)'*H(4,j) - rx(7,j)'*H(1,j) - rx(8,j)'*H(2,j);
        
        S4 = S4 + rx(1,j)*H(4,j)' - rx(2,j)*H(3,j)' + rx(3,j)*H(2,j)' - rx(4,j)*H(1,j)' ...
                 +rx(5,j)'*H(4,j) - rx(6,j)'*H(3,j) + rx(7,j)'*H(2,j) - rx(8,j)'*H(1,j);
             
        Hnorm = Hnorm + H(:,j)'*H(:,j);
    end
    for L=1:Mary
        Con = (-1 + 2*Hnorm) * Smap(L) * Smap(L)';
        
        %For s1
        est_s1(L)= real( (S1 - Smap(L))*(S1 - Smap(L))' + Con);
        
        %For s2
        est_s2(L)= real( (S2 - Smap(L))*(S2 - Smap(L))' + Con);
        
        %For s3
        est_s3(L)= real( (S3 - Smap(L))*(S3 - Smap(L))' + Con);
        
        %For s4
        est_s4(L)= real( (S4 - Smap(L))*(S4 - Smap(L))' + Con);   
    end
    %Choose symbols that minimize the est_* vectors
    %Decide in favour of min value 
    [A,B]=min(est_s1);
    shat(k)=B-1;      %-1 b/c index starts at 0 and not 1
    [A,B]=min(est_s2);
    shat(k+1)=B-1;    %-1 b/c index starts at 0 and not 1
    [A,B]=min(est_s3);
    shat(k+2)=B-1;      %-1 b/c index starts at 0 and not 1
    [A,B]=min(est_s4);
    shat(k+3)=B-1;    %-1 b/c index starts at 0 and not 1
end
%Compute symbol errors
err(index)=sum(shat~=symb');