压缩感知信道估计算法（Matlab 代码）

function [cs_mse_ave,ls_mse_ave,mmse_mse_ave]=MSE_com(N,L,K,h,N1) W_h=1/sqrt(N)*fft(eye(N,L)); H=W_h*h; H1=H(1:N1,:); H2=H((N1+1):N,:); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %-----------------------------training sequence----------------------% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% d=randn(1,N); d=d/std(d); d=d-mean(d); X=diag(d); X1=X(1:N1,1:N1); X2=X((N1+1):N,(N1+1):N); XH=X*H; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % --------------------求h的自协方差矩阵-Rhh-------------------------% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% gg=diag(h); gg_myu = sum(gg, 1)/L; gg_mid = gg - gg_myu(ones(L,1),:); sum_gg_mid= sum(gg_mid, 1); Rgg = (gg_mid' * gg_mid- (sum_gg_mid' * sum_gg_mid) / L) / (L- 1); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %---------------------------添加高斯白噪声，得Y-----------------------% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% n1=ones(N,1); for m=1:20%多组实验取平均 for n=0:6 SNR(n+1)=5*n;%比较不同SNR clear j; n1=n1*0.01j;%保证下面的awgn函数输入的是复高斯噪声 No=awgn(n1,SNR(n+1));%white Gaussian noise %variance=var(noise); SNR_log=10^(SNR(n+1)/10); variance=var(XH)/SNR_log; No=variance/var(No)*No; var_No=var(No); %No=fft(noise); %Y = AWGN(X,SNR) adds to X. The SNR is in dB.The power of X is assumed to be 0 dBW. If X is complex, then AWGN adds complex noise. %No=fft(noise); Y=XH+No; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %-----------------------LS/MMSE信道估计，得MSE-------------------------% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% mean_squared_error_ls=LS_MSE_calc(X,H,Y,N); %Evaluating the mean squared error for the MMSE estimator.. mean_squared_error_mmse=MMSE_MSE_calc(X,H,Y,Rgg,var_No,N,L); mmse_mse(m,n+1)=mean_squared_error_mmse; ls_mse(m,n+1)=mean_squared_error_ls; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %--------------------------CS信道估计H，得MSE--------------------------% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %CS evaluate H s=Y; Phi=X; T=Phi*W_h; % 恢复矩阵(测量矩阵*正交反变换矩阵 re_H=zeros(1,N); % 待重构的谱域(变换域)向量 re_y=zeros(1,L); [pos_arry,aug_y]=omp(K,s,T); % pos_arry:最大投影系数对应的位置, [cos_pos_arry,aug_y]=omp(K,s,T); % pos_arry:最大投影系数对应的位置, re_y(pos_arry)=aug_y; re_H=W_h*re_y.'; % 做傅里叶变换重构得到原信号 diff_value=abs((re_H) -(H)); re_error=mean((diff_value./abs(H)).^2); cs_mse(m,n+1)=re_error; end end mmse_mse_ave=mean(mmse_mse); ls_mse_ave=mean(ls_mse); cs_mse_ave=mean(cs_mse);