高斯信道衰减-MATLAB程序.doc

clearall;
closeall;

echoon;
display('Beginning');
echooff;

N=50;
SNRindB=-5;
snr=10.^(SNRindB./10);
a=[0.0050.010.0250.050.10.150.20.30.40.50.60.70.80.90.950.98];
CV=[4.5593.8573.0702.4921.9331.6101.4051.1260.9240.7730.6500.5450.4480.3460.2820.227];
%a=[0.980.960.930.90.80.60.50.40.30.20.10.050.010.0050.001];
exp_time=10000;
%%%%%%%%%%%%%%%%%M是高斯信号%%%%%%%%%%%%%%%%%%%%%%%
fori=1:length(a);
display(i);
count=[0000000000000000];
ave_segma_MLE=0;
fork=1:exp_time
%display(k);V
%%%%产生高斯随机数，并进行处理%%%%%%%%
R1=randn(1,N);
R2=randn(1,N);
R=(sqrt(snr).*R1+R2);
Y=sort(R);
%%%%%%%%%%%%%进行极大似然估计%%%%%%%%%%%
%R3=sum(R)/N;
%forw=1:N;
%A(w)=(R(w)-R3).^2;
%end
%segma_MLE=sqrt(sum(A)/N);
segma_MLE=1;
temp2=0;
forj=1:1:N

%%%%假设的高斯分布函数F（x）
%%%Z_i
h1=@(t)((1/(sqrt(2*pi).*segma_MLE)).*exp((t.^2)/(-2*(segma_MLE.^2))));
F_Y1=quadl(h1,-10000,Y(j));
Z_1=F_Y1;

%%%%Z_(N-i+1)
h2=@(t)((1/(sqrt(2*pi).*segma_MLE)).*exp((t.^2)/(-2*(segma_MLE.^2))));
F_Y2=quadl(h2,-10000,Y(N-j+1));
Z_2=F_Y2;

%%%%计算差值
temp2=temp2+(2*j-1)*(log(Z_1)+log(1-Z_2));
%temp2=temp2+(2*j-1)*(log(Z_1))+(2*N+1-2*j)*(log(1-Z_1));
end
%A_n2=-N-temp2;
A_n2=-N-(1/N)*temp2;
%display(A_n2);

ifA_n2>=CV(i)
count(i)=count(i)+1;
end
count1(i)=count(i);
end
P1(i)=count1(i)/exp_time;
display(P1(i));
%ave_segma_MLE=ave_segma_MLE/exp_time;
%display(ave_segma_MLE);
end
%%%%%%%%%%%%%%%%%%%%M是正弦信号%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
fori=1:length(a);
display(i);
count=[0000000000000000];
ave_segma_MLE=0;
fork=1:exp_time
%display(k);
%%%%产生正弦信号随机数，并进行处理%%%%%%%%
R=linspace(0,2*pi,N);
forf=1:N;
Y1(f)=sqrt(2)*sin((pi/3)*R(f)+(pi/3));
end
%%%%产生高斯分布随机数，并进行排序处理
%X=raylrnd(segma_MLE,[1N]);
%Y=sort(X);
R1=randn(1,N);
Y2=(sqrt(snr).*Y1+R1);
Y=sort(Y2);
%%%%%%%%%%%%%进行极大似然估计%%%%%%%%%%%
%R2=sum(Y)/N;
%forw=1:N;
%A(w)=(Y(w)-R2).^2;
%end
%segma_MLE=sqrt(sum(A)/N);
segma_MLE=1;
temp2=0;
forj=1:1:N

%%%%假设的高斯分布函数F（x）
%%%Z_i
h1=@(t)((1/(sqrt(2*pi).*segma_MLE)).*exp((t.^2)/(-2*(segma_MLE.^2))));
F_Y1=quadl(h1,-10000,Y(j));
Z_1=F_Y1;

%%%%Z_(N-i+1)
h2=@(t)((1/(s