MIMO_OFDM.rar_MIMO-OFDM估计_MIMO信道_MIMO信道估计_OFDM信道_mimo

%------------------------------------------ % EE359 final project, Fall 2002 % Channel estimation for a MIMO-OFDM system % By Shahriyar Matloub %------------------------------------------ clear all; %close all; i=sqrt(-1); Rayleigh=1; AWGN=0; % for AWGN channel MMSE=0; % estimation technique Nsc=64; % Number of subcarriers Ng=16; % Cyclic prefix length SNR_dB=[0 5 10 15 20 25 30 35 40]; % Signal to noise ratio Mt=2; % Number of Tx antennas Mr=2; % Number of Rx antennas pilots=[1:Nsc/Ng:Nsc]; % pilot subcarriers 【1:4:64】 DS=5; % Delay spread of channel iteration_max=200; %%%%%%%%%%%%%%%%%%%%%%%%%%%% % Channel impulse response % %%%%%%%%%%%%%%%%%%%%%%%%%%%% if (Rayleigh) N=50; fm=100;%fm=v/r=v*fc/c B=20e3; fd=(rand(1,N)-0.5)*2*fm; theta=randn(1,N)*2*pi; c=randn(1,N); c=c/sum(c.^2); t=0:fm/B:10000*fm/B; Tc=zeros(size(t)); Ts=zeros(size(t)); for k=1:N Tc=c(k)*cos(2*pi*fd(k)*t+theta(k))+Tc; Ts=c(k)*sin(2*pi*fd(k)*t+theta(k))+Ts; end r=ones(Mt*Mr,1)*(Tc.^2+Ts.^2).^0.5; index=floor(rand(Mt*Mr,DS)*5000+1); end MEE1=zeros(1,length(SNR_dB)); MEE2=zeros(1,length(SNR_dB)); for snrl=1:length(SNR_dB) %snrl estimation_error1=zeros(Mt*Mr,Nsc); estimation_error2=zeros(Mt*Mr,Nsc); R1=besselj(0,2*pi*fm*(Nsc+Ng)/B);%这里是Jakes模型h（n）的自相关函数 sigma2=10^(-SNR_dB(snrl)/10); aa=(1-R1^2)/(1-R1^2+sigma2); bb=sigma2*R1/(1-R1^2+sigma2); for iteration=1:iteration_max %iteration if AWGN==1 h=ones(Mt*Mr,1); else phi=rand*2*pi; h=r(index+iteration)*exp(j*phi); %h=rand(Mt*Mr,DS); h=h.*(ones(Mt*Mr,1)*(exp(-0.5).^[1:DS])); h=h./(sqrt(sum(abs(h).^2,2))*ones(1,DS)); end CL=size(h,2); % channel length data_time=zeros(Mt,Nsc+Ng); data_qam=zeros(Mt,Nsc); data_out=zeros(Mr,Nsc); output=zeros(Mr,Nsc); for tx=1:Mt data_b=round(rand(4,Nsc)); %原式data_b=0*round(rand(4,Nsc));但是我认为是数据应该是0或1 所以改了 % data data_qam(tx,:)=j*(2*(mod(data_b(1,:)+data_b(2,:),2)+2*data_b(1,:))-3)+... 2*(mod(data_b(3,:)+data_b(4,:),2)+2*data_b(3,:))-3; for loop=1:Mt data_qam(tx,pilots+loop-1)=(1+j)*(loop==tx); % pilots end data_time_temp=ifft(data_qam(tx,:)); data_time(tx,:)=[data_time_temp(end-Ng+1:end) data_time_temp]; end for rx=1:Mr for tx=1:Mt output_temp=conv(data_time(tx,:),h((rx-1)*Mt+tx,:));%卷积 output(rx,:)=output_temp(Ng+1:Ng+Nsc)+output(rx,:);%去掉循环前缀 end np=(sum(abs(output(rx,:)).^2)/length(output(rx,:)))*sigma2; noise=(randn(size(output(rx,:)))+i*randn(size(output(rx,:))))*sqrt(np); output(rx,:)=output(rx,:)+noise; data_out(rx,:)=fft(output(rx,:)); end %%%%%%%%%%%%%%%%%%%%%% % Channel estimation % %%%%%%%%%%%%%%%%%%%%%% H_act=zeros(Mt*Mr,Nsc); H_est1=zeros(Mt*Mr,Nsc); H_est2=zeros(Mt*Mr,Nsc); i=1; for tx=1:Mt for rx=1:Mr H_est_temp=data_out(rx,pilots+tx-1)./data_qam(tx,pilots+tx-1); %H_est_temp2=aa*abs(H_est_temp1)+bb*abs(H_est2((rx-1)*Mt+tx,:)); h_time=ifft(H_est_temp); h_time=[h_time zeros(1,Nsc-length(h_time))]; H_est1((rx-1)*Mt+tx,:)=fft(h_time); H_est2((rx-1)*Mt+tx,:)=((aa*abs(H_est1((rx-1)*Mt+tx,:))+bb*abs(H_est2((rx-1)*Mt+tx,:)))... .*H_est1((rx-1)*Mt+tx,:))./abs(H_est1((rx-1)*Mt+tx,:)); if (tx>1) H_est1((rx-1)*Mt+tx,:)=[H_est1((rx-1)*Mt+tx,Nsc-tx+2:Nsc) H_est1((rx-1)*Mt+tx,1:Nsc-tx+1)]; H_est2((rx-1)*Mt+tx,:)=[H_est2((rx-1)*Mt+tx,Nsc-tx+2:Nsc) H_est2((rx-1)*Mt+tx,1:Nsc-tx+1)]; end H_act((rx-1)*Mt+tx,:)=fft([h((rx-1)*Mt+tx,:) zeros(1,Nsc-CL)]); error1=(abs(H_act((rx-1)*Mt+tx,:)-H_est1((rx-1)*Mt+tx,:)).^2); error2=(abs(H_act((rx-1)*Mt+tx,:)-H_est2((rx-1)*Mt+tx,:)).^2); %error=(abs(H_act((rx-1)*Mt+tx,:)-H_est((rx-1)*Mt+tx,:)).^2)./(abs(H_act((rx-1)*Mt+tx,:)).^2); estimation_error1((rx-1)*Mt+tx,:)=estimation_error1((rx-1)*Mt+tx,:)+error1; estimation_error2((rx-1)*Mt+tx,:)=estimation_error2((rx-1)*Mt+tx,:)+error2; %subplot(Mt*Mr,3,i),plot([0:Nsc-1],abs(H_act((rx-1)*Mt+tx,:))); i=i+1; %subplot(Mt*Mr,3,i),plot([0:Nsc-1],abs(H_est((rx-1)*Mt+tx,:))); i=i+1; %subplot(Mt*Mr,3,i),plot([0:Nsc-1],abs(error)); i=i+1; end end end estimation_error1=estimation_error1/iteration_max; estimation_error2=estimation_error2/iteration_max; %estimation_error=min(estimation_error,10*iteration_max*ones(size(estimation_error))); %for i=1:Mt*Mr % subplot(Mt*Mr,2,2*i-1),plot([0:Nsc-1],estimation_error1(i,:)); % subplot(Mt*Mr,2,2*i),plot([0:Nsc-1],estimation_error2(i,:)); %end MEE1(snrl)=sum(sum(estimation_error1))/(Mt*Mr*Nsc); MEE2(snrl)=sum(sum(estimation_error2))/(Mt*Mr*Nsc); end plot(SNR_dB,10*log10(MEE1)); hold on; plot(SNR_dB,10*log10(MEE2),'r'); %H_act=fft([h_zeros(1,Nsc-CL)]).'; error1=(abs(H_act-H_est1).^2)./(abs(H_act).^2); error2=(abs(H_act-H_est2).^2)./(abs(H_act).^2); %%%%%%%%% % Plots % %%%%%%%%% fig=5; i=1; plot([0:length(H_act)-1],abs(H_act)); figure plot([0:length(H_est1)-1],abs(H_est1)); figure plot([0:length(H_est2)-1],abs(H_est2)); figure plot([0:length(error1)-1],error1); figure plot([0:length(error2)-1],error2); figure subplot(fig,1,i),plot([0:length(H_act)-1],abs(H_act)); i=i+1; subplot(fig,1,i),plot([0:length(H_est1)-1],abs(H_est1)); i=i+1; subplot(fig,1,i),plot([0:length(H_est2)-1],abs(H_est2)); i=i+1; subplot(fig,1,i),plot([0:length(error1)-1],error1); i=i+1; subplot(fig,1,i),plot([0:length(error2)-1],error2);