?? mimo_ofdm.m
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%------------------------------------------% Channel estimation for a MIMO-OFDM system% 訓練序列做導頻時的狀況%------------------------------------------clear all;%close all;Rayleigh=1;AWGN=0; % for AWGN channel 加性白高斯噪聲LMMSE=0; % estimation techniqueNsc=64; % Number of subcarriersNg=16; % Cyclic prefix lengthSNR_dB=[0 5 10 15 20 25 30 35 40]; % Signal to noise ratioMt=2; % Number of Tx antennasMr=2; % Number of Rx antennaspilots=1:Nsc/Ng:Nsc; % pilot subcarriers DS=15; % Delay spread of channeliteration_max=200;%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Channel impulse response %%%%%%%%%%%%%%%%%%%%%%%%%%%%%if (Rayleigh) N=50; fm=100; B=20e3; fd=(rand(1,N)-0.5)*2*fm; % -fm<=fd<=fm; -100<=fd<=100 theta=randn(1,N)*2*pi; % 正態隨機分布 theta=[0,2pi] c=randn(1,N); % 正態隨機分布 [0,1] c=1*50 c=c/sum(c.^2); % t=[0:fm/B:10000*fm/B]; % t=[0:0.005:50] Tc=zeros(size(t)); % Tc是1行,10001列 全0 cos信號 Ts=zeros(size(t)); % Ts是1行,10001列 全0 sin信號 for k=1:N Tc=c(k)*cos(2*pi*fd(k)*t+theta(k))+Tc; % 小括號表示的是下標 調制方法 Ts=c(k)*sin(2*pi*fd(k)*t+theta(k))+Ts; end r=ones(Mt*Mr,1)*(Tc.^2+Ts.^2).^0.5; % (4*1)*(1*10001),后面是均方值,r=(4*10001) 前40有數據 index=floor(rand(Mt*Mr,DS)*5000+1); % 取整 index=[1,5001] (4*15)endMEE1=zeros(1,length(SNR_dB)); % (1*9)MEE2=zeros(1,length(SNR_dB));for snrl=1:length(SNR_dB) % 循環九次 snrl estimation_error1=zeros(Mt*Mr,Nsc); % estimation_error1=(4*64) estimation_error2=zeros(Mt*Mr,Nsc); R1=besselj(0,2*pi*fm*(Nsc+Ng)/B); % R1=besselj(0,0.8pi)~=-0.0550 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 % 1-->200 %iteration if AWGN==1 h=ones(Mt*Mr,1); else phi=rand*2*pi; % 這里的rand就是一個任意數[0,1] h=r(index+iteration)*exp(i*phi); % %%%%%%%%%%%%h=rand(Mt*Mr,DS); h=h.*(ones(Mt*Mr,1)*(exp(-0.5).^[1:DS])); % [exp(-0.5) exp(-0.5)^2 exp(-0.5)^3 ...] h=h./(sqrt(sum(abs(h).^2,2))*ones(1,DS)); % sum 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=00*round(rand(4,Nsc)); % data data_qam(tx,:)=i*(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+i)*(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,:)); output(rx,:)=output_temp(Ng+1:Ng+Nsc)+output(rx,:); 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))]; %過采樣 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'); xlabel('信噪比/dB') ylabel('誤碼率/dB')%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=4;%i=1;%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);
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