matlab_OFDM系统中，基于LS信道估计算法和MMSE信道估计算法的MSE分析和BER分析

clc; clear all; %%产生训练序列，采用BPSK调制，符号：+1/-1 X=zeros(64,64); d=rand(64,1); for i=1:64 if(d(i)>=0.5) d(i)=+1; else d(i)=-1; end end for i=1:64 X(i,i)=d(i); end %%计算信道矩阵 tau=[0.5 3.5]; for k=1:64 s=0; for m=1:2 s=s+(exp(-j*pi*(1/64)*(k+63*tau(m))) * (( sin(pi*tau(m)) / sin(pi*(1/64)*(tau(m)-k))))); %Go through the above cited paper for the theory behind the formula end g(k)=s/sqrt(64); end G=g'; H=fft(G); XFG=X*H; n1=ones(64,1); n1=n1*0.000000000000000001i; noise=awgn(n1,8); variance=var(noise); N=fft(noise); Y=XFG+N; %%计算信道矩阵G的自协方差矩阵Ggg gg=zeros(64,64); for i=1:64 gg(i,i)=G(i); end gg_myu = sum(gg, 1)/64; gg_mid = gg - gg_myu(ones(64,1),:); sum_gg_mid= sum(gg_mid, 1); Rgg = (gg_mid' * gg_mid- (sum_gg_mid' * sum_gg_mid) / 64) / (64 - 1); %LS估计的信道矩阵 %Hls=inv(X)*Y; H_ls=(inv(X)) * Y; Hls=zeros(64,64); for i=1:64 Hls(i,i)=H_ls(i); end %MMSE估计的信道矩阵 u=rand(64,64); F=fft(u)*inv(u);%The 64 X 64 twiddle factor matrix.. I=eye(64,64); Rgy=Rgg * F'* X'; Ryy=X * F * Rgg * F' *X' + variance * I; Gmmse=Rgy * inv(Ryy)* Y; H_mmse=fft(Gmmse); for i=1:64 Hmmse(i,i)=H_mmse(i); end %%仿真 for n=1:6 SNR_send=5*n;%SNR=5,10,15,20,25,30 error_count_ls=0;%记录错误个数 error_count_mmse=0; %每个SNR下用发1000次64个符号序列仿真 for c=1:1000 %Generate Random Data[i/p matrix..] X=zeros(64,64); d=rand(64,1); for i=1:64 if(d(i)>=0.5) d(i)=+1; else d(i)=-1; end end for i=1:64 X(i,i)=d(i); end XFG=X*H;%符号通过实际信道 n1=ones(64,1); n1=n1*0.000000000000000001i;%Just to ensure that the function awgn adds 'complex gaussian noise'.. noise=awgn(n1,SNR_send); variance=var(noise); N=fft(noise); Y=XFG+N;%叠加噪声 %接收端： %I(k)表示判决矩阵 %I:LS估计 I=inv(Hls)* Y; for k=1:64 if(real(I(k))>0)%Putting it through a slicer I(k)=1; else I(k)=-1; end end for k=1:64 if(I(k)~=d(k)) error_count_ls=error_count_ls+1; end end %II:MMSE估计 I=inv(Hmmse)* Y; for k=1:64 if(real(I(k))>0)%Putting it through a slicer I(k)=1; else I(k)=-1; end end for k=1:64 if(I(k)~=d(k)) error_count_mmse=error_count_mmse+1; end end end%1000次序列估计结束 ser_ls(n)=error_count_ls/64000;%共发1000*64个符号 ser_mmse(n)=error_count_mmse/64000; ser_ls ser_mmse SNR(n)=SNR_send; end; %绘图，LS用蓝线，MMSE用黑线 semilogy(SNR,ser_mmse,'k-'); grid on; xlabel('SNR（dB)'); ylabel('SER'); title('基于SER的MMSE/LS估计的性能比较'); hold on; semilogy(SNR,ser_ls,'b*'); semilogy(SNR,ser_ls,'b-'); semilogy(SNR,ser_mmse,'kv'); grid on;