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,:);
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%-----------------------------training sequence----------------------%
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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;
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% --------------------求h的自协方差矩阵-Rhh-------------------------%
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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);
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%---------------------------添加高斯白噪声，得Y-----------------------%
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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;
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%-----------------------LS/MMSE信道估计，得MSE-------------------------%
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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;
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%--------------------------CS信道估计H，得MSE--------------------------%
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%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);
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