【用户检测】基于matlab最小均方算法LMS和递推最小二乘算法RLS盲多用户检测【含Matlab源码 3543期】.zip

clc clear all N=31;%扩频增益% step=2000; K=10; S1=[1 1 1 1 1 -1 -1 -1 1 1 -1 1 1 1 -1 1 -1 1 -1 -1 -1 -1 1 -1 -1 1 -1 1 1 -1 -1];%a(n)=a(2)+a(-1) S2=[1 -1 -1 -1 -1 1 -1 1 1 -1 1 -1 1 -1 -1 -1 1 1 1 -1 1 1 1 1 1 -1 -1 1 -1 -1 1];%a(n)=a(3)+a(2)+a(1)+a(-1) for i=1:N SS(i,:)=(S1~=[S2(i:N),S2(1:i-1)]); end SS=SS*2-1; S=SS(1:K,:); C(1,:)=SS(1,:); C(2:N,:)=rand(N-1,N)*2-1; for i=2:N rou=C(i,:)*C(1:i-1,:).'./dot(C(1:i-1,:),C(1:i-1,:),2).'; C(i,:)=C(i,:)-rou*C(1:i-1,:); end for i=1:N C(i,:)=C(i,:)/sqrt(dot(C(i,:),C(i,:))); end C_null=C(2:N,:); SNR_db=[20,30,30,30,30,30,40,40,40,50]; %SNR (in dB)% SNR=10.^(SNR_db./10); A=sqrt(SNR./SNR(1)); %lms盲自适应多用户检测% uplms=zeros(1,step); downlms=zeros(1,step); repeat_time=60; %实验重复次数% for repeat=1:repeat_time repeat; b=zeros(K,step); b=sign(rand(K,step)-0.5); noise=randn(N,step); noise=sqrt(10./SNR(1))*noise;% Noise% for i=1:K Ab(i,:)=A(i)*b(i,:); end ystep=(Ab.'*S).'; ystep=ystep+noise; x=zeros(1,N); mu=1e-6; for i=1:step Z=(S(1,:)+x)*ystep(:,i); ZMF=S(1,:)*ystep(:,i); x=x-mu*Z*(ystep(:,i).'-ZMF*S(1,:)); xx(i,:)=x; c=S(1,:)+x; up1(i)=(c*S(1,:).')^2; down1(i)=( c*(ystep(:,i)-b(1,i)*S(1,:).') )^2; %SINR(i)=up1/down1; y(i)=sign(c*ystep(:,i)); end uplms=uplms+up1; downlms=downlms+down1; end SINRlms=uplms./downlms; SINR_dblms=10*log10(SINRlms); plot(SINR_dblms,'r'); hold on %rls盲自适应多用户检测% clear N=31; step=2000; K=10; S1=[1 1 1 1 1 -1 -1 -1 1 1 -1 1 1 1 -1 1 -1 1 -1 -1 -1 -1 1 -1 -1 1 -1 1 1 -1 -1];%a(n)=a(2)+a(-1) S2=[1 -1 -1 -1 -1 1 -1 1 1 -1 1 -1 1 -1 -1 -1 1 1 1 -1 1 1 1 1 1 -1 -1 1 -1 -1 1];%a(n)=a(3)+a(2)+a(1)+a(-1) for i=1:N SS(i,:)=(S1~=[S2(i:N),S2(1:i-1)]); end SS=SS*2-1; S=SS(1:K,:); C(1,:)=SS(1,:); C(2:N,:)=rand(N-1,N)*2-1; for i=2:N rou=C(i,:)*C(1:i-1,:).'./dot(C(1:i-1,:),C(1:i-1,:),2).'; C(i,:)=C(i,:)-rou*C(1:i-1,:); end for i=1:N C(i,:)=C(i,:)/sqrt(dot(C(i,:),C(i,:))); end C_null=C(2:N,:); SNR_db=[20,30,30,30,30,30,40,40,40,50]; %SNR (in dB)% SNR=10.^(SNR_db./10); A=sqrt(SNR./SNR(1)); uprls=zeros(1,step); downrls=zeros(1,step); repeat_time=60; for repeat=1:repeat_time repeat; b=zeros(K,step); b=sign(rand(K,step)-0.5); noise=randn(N,step); noise=sqrt(10./SNR(1))*noise;% Noise% for i=1:K Ab(i,:)=A(i)*b(i,:); end ystep=(Ab.'*S).'; ystep=ystep+noise; x=zeros(1,N); sigma=1e-2; R1=1/sigma*eye(N); lamda=0.997; for i=1:step K1=R1*ystep(:,i)/(lamda+ystep(:,i)'*R1*ystep(:,i)); h=R1*S(1,:).'; c=h/(S(1,:)*h); R1=1/lamda*(R1-K1*ystep(:,i)'*R1); up2(i)=abs( (c.'*S(1,:).')^2 ); down2(i)=abs( ( c.'*(ystep(:,i)-b(1,i)*S(1,:).') )^2 ); %SINR(i)=up2/down2; y(i)=sign(real(c.'*ystep(:,i))); end uprls=uprls+up2; downrls=downrls+down2; end SINRrls=uprls./downrls; SINR_dbrls=10*log10(SINRrls); plot(SINR_dbrls,'g'); ylabel('SIR/db'); xlabel('迭代步数'); title('时间平均信干比');