实现信道估计算法的MATLAB仿真.doc

实现信道估计算法的matlab仿真clear all;%close all;i=sqrt(-1);Rayleigh=1;AWGN=0; % for AWGN channel MMSE=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=5; % 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; 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);endMEE1=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); sigma2=10(-SNR_dB(snrl)/10); aa=(1-R12)/(1-R12+sigma2); bb=sigma2*R1/(1-R12+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:)S); 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=0*round(rand(4,Nsc); % 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,:); 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); 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 (tx1) 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);endplot(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=4;i=1;subplot(fig,1,i),plot(0:length(H_act)-1,abs(H_act); i=i+1;subplot(fig,