mse_compare.m

ofdm信道估计仿真（ls算法

clc;
clear all;
%生成训练序列
%用BPSK调制
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
%计算出信道向量G
 %信道特性
  tau=[0.5 3.5];
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)))));
      end
g(k)=s/sqrt(64);
end
G=g';%信道向量
H=fft(G);% 频域
u=rand(64,64);
F=fft(u)*inv(u);% DFT矩阵

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% 求出G的自相关矩阵Rgg
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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);

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%多次计算，求平均
for m=1:12
    
for n=1:6

SNR_send=3*n;
XFG=X*H;
n1=ones(64,1);
n1=n1*0.000000000000000001i;%加入复高斯白噪声
noise=awgn(n1,SNR_send);
variance=var(noise);
N=fft(noise);
Y=XFG+N;
%得出LS的均方误差
mean_squared_error_ls=LS_MSE_calc(X,H,Y);
%得出MMSE的均方误差
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);
%作图
semilogy(SNR,mmse_mse_ave,'-vk');
grid on;
xlabel('SNR in DB');
ylabel('mean squared error');
title('OFDM系统MMSE和LS算法的比较（MSE）');
hold on;
semilogy(SNR,ls_mse_ave,'-*b');
grid on;
xlabel('SNR in DB');
ylabel('mean squared error');
title('OFDM系统MMSE和LS算法的比较（MSE）');
legend('MMSE算法','LS算法');
hold off