# ls-mse.rar

• wangkedelh
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• matlab
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• 2020-04-26 14:22
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MATLAB程序基于LS和MMSE算法的MSE比较-mse比较，希望可以给大家点帮助。
ls-mse.rar
• ls-mse
• MMSE_MSE_calc.m
569B
• LS_MSE_calc.m
423B
• MSE_compare.m
3.6KB

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Author: Vinay Uday Prabhu % E-mail: vinay_u_prabhu@yahoo.co.uk % Function: Comparison of the performances of the LS and the MMSE channel estimators % for a 64 sub carrier OFDM system based on the parameter of Mean square error % Assumptions: The channel is assumed to be g(t)=delta(t-0.5 Ts)+delta(t-3.5 Ts) % {Fractionally spaced} %For more information on the theory and formulae used , please do refer to the paper On %"Channel Estimation In OFDM systems" By Jan-Jaap van de Beek, Ove Edfors, Magnus Sandell % Sarah Kate wilson and Petr Ola Borjesson In proceedings Of VTC'95 Vol 2 pg.815-819 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% clc; clear all; %Generation of a naive training sequence.. %Assuming BPSK modulation ...symbols:+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 %Calculation of G[The channel Matrix] %The channnel is... tau=[0.5 3.5];%The fractionally spaced taps.. %Generation of the G matrix... 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';%Thus, the channel vector is evaluated.. H=fft(G)% In the freq domain.. u=rand(64,64); F=fft(u)*inv(u);% 'F' is the twiddle factor matrix.. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Evaluation of the autocovariance matrix of G-Rgg %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 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); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %Running for a dozen trials to try and average out the results.. for m=1:12 for n=1:5 SNR_send=5*n; XFG=X*H; 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; %Evaluating the mean squared error for the LS estimator.. mean_squared_error_ls=LS_MSE_calc(X,H,Y); %Evaluating the mean squared error for the MMSE estimator.. mean_squared_error_mmse=MMSE_MSE_calc(X,H,Y,Rgg,variance); SNR(n)=SNR_send; mmse_mse(m,n)=mean_squared_error_mmse; ls_mse(m,n)=mean_squared_error_ls; end; end; ls_mse mmse_mse mmse_mse_ave=mean(mmse_mse); ls_mse_ave=mean(ls_mse); %Now just the display part..... %semilogy(SNR,mmse_mse_ave,'k-'); %grid on; %xlabel('SNR in DB'); %ylabel('mean squared error'); %title('PLOT OF SNR V/S MSE FOR AN OFDM SYSTEM WITH MMSE/LS ESTIMATOR BASED RECEIVERS'); %hold on; semilogy(SNR,ls_mse_ave,'b*'); semilogy(SNR,ls_mse_ave,'b-'); %semilogy(SNR,mmse_mse_ave,'kv'); grid on; xlabel('SNR in DB'); ylabel('mean squared error'); title('PLOT OF SNR V/S MSE FOR AN OFDM SYSTEM WITH MMSE/LS ESTIMATOR BASED RECEIVERS');

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