jh_lms.m

LMS及RLS算法

function [Pe1,Pe2,Pe3]=jh_LMS(snr_in_dB)
snr_in_dB=30;
N=10000;
d=1;
Eav=10*d^2;
snr=10^(snr_in_dB/10);
sgma=sqrt(Eav/(8*snr));
M=16;
for k=1:N
    temp=rand;
    dsource(k)=1+floor(M*temp);
end
mapping=[-3*d, 3*d;
         -d,   3*d;
          d,   3*d;
         3*d,  3*d;
        -3*d,   d ;
          -d,   d ;
           d,   d ;
         3*d,   d ;
        -3*d,  -d ;
          -d,  -d ;
           d,  -d ;
         3*d,  -d ;
        -3*d, -3*d;
          -d, -3*d;
           d, -3*d;
         3*d, -3*d];
for k=1:N
    qam_sig(k,:)=mapping(dsource(k),:);
end
qam_sig_r=qam_sig(:,1);
qam_sig_i=qam_sig(:,2);
qam=qam_sig_r+i*qam_sig_i;
for k=3:N;
    qam_gr(k)=0.3*qam(k)+1*qam(k-1)+0.3*qam(k-2);
end

for k=1:N
    [noise0(1) noise0(2)]=gngauss(sgma);
    noise=noise0(1)+i*noise0(2);
    r(k)=qam_gr(k)+noise;
    r0(k)=qam(k)+noise;                 %无码间干扰时的输出
end

r1=real(r);
r2=imag(r);
order=11;                                         %均衡器阶数
error_s=zeros(1,N);    
w=0.001*(1+i)*ones(1,order)';
w1=real(w);
w2=imag(w);
h=cov(r.');
u=1/(10*trace(h));
 %isi=[0.3,0.9,0.3];                             %信道特性3
 %M0=conv(x,isi);
 %M=M0(1:N); 

for k=order:N;
    d0(k)=qam(k-order+1);                          %均衡器理想输出    
end
d1=real(d0);
d2=imag(d0);

for k=order:N
    r11=r1(k:-1:(k-order+1))';
    y1(k)=w1'*r11;
    e1(k)=d1(k)-y1(k);
    w1=w1+u*r11.*conj(e1(k)); 
    r22=r2(k:-1:(k-order+1))';
    y2(k)=w2'*r22;
    e2(k)=d2(k)-y2(k);
    w2=w2+u*r22.*conj(e2(k)); 
    e(k)=e1(k)+i*e2(k);
    y(k)=y1(k)+i*y2(k);
end

Pe1=0;
numoferr=0;
for k=1:N
    for l=1:M
        metrics(l)=(real(r0(k))-mapping(l,1))^2+(imag(r0(k))-mapping(l,2))^2;
    end
    [min_metric decis]=min(metrics);
    if decis~=dsource(k)
       numoferr=numoferr+1;
    end
end
Pe1=numoferr/N;         %%无码间干扰时的误码率

Pe2=0;
numoferr=0;
for k=2:N
    for l=1:M
        metrics(l)=(real(r(k))-mapping(l,1))^2+(imag(r(k))-mapping(l,2))^2;;
    end
    [min_metric decis]=min(metrics);
    if decis~=dsource(k-1)
       numoferr=numoferr+1;
    end
end
Pe2=numoferr/N;           %%均衡前误码率

Pe3=0;
numoferr=0;
for k=order:N
    for l=1:M
        metrics(l)=(y1(k)-mapping(l,1))^2+(y2(k)-mapping(l,2))^2;
    end
    [min_metric decis]=min(metrics);
    if decis~=dsource(k-order+1)
       numoferr=numoferr+1;
    end
end
Pe3=numoferr/N;             %%均衡后误码率