高斯信道衰减-MATLAB程序.doc

clear all;close all;echo on;display(Beginning);echo off;N=50;SNRindB=-5;snr=10.(SNRindB./10);a=0.005 0.01 0.025 0.05 0.1 0.15 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0.95 0.98;CV=4.559 3.857 3.070 2.492 1.933 1.610 1.405 1.126 0.924 0.773 0.650 0.545 0.448 0.346 0.282 0.227;% a=0.98 0.96 0.93 0.9 0.8 0.6 0.5 0.4 0.3 0.2 0.1 0.05 0.01 0.005 0.001;exp_time=10000;%M是高斯信号%for i=1:length(a); display(i); count=0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;ave_segma_MLE=0;for k=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;% for w=1:N;% A(w)=(R(w)-R3).2;% end% segma_MLE=sqrt(sum(A)/N);segma_MLE=1; temp2=0;for j=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); if A_n2=CV(i) count(i)=count(i)+1; end count1(i)=count(i);endP1(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%for i=1:length(a); display(i); count=0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;ave_segma_MLE=0;for k=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);for f=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,1 N);%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;% for w=1:N;% A(w)=(Y(w)-R2).2;% end% segma_MLE=sqrt(sum(A)/N);segma_MLE=1; temp2=0;for j=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); if A_n2=CV(i) count(i)=count(i)+1; end count1(i)=count(i);endP2(i)=count1(i)/exp_time;display(P2(i);% ave_segma_MLE=ave_segma_MLE/exp_time;% display(ave_segma_MLE);end%M是1%555for i=1:length(a); display(i); count=0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0;ave_segma_MLE=0;for k=1:exp_time% display(k); %产生高斯随机数，并进行处理%R1=randn(1,N);%m=1%R2=ones(1,N);R=(sqrt(snr).*R2+R1);Y=sort(R);%进行极大似然估计% R3=sum(R)/N;% for w=1:N;% A(w)=(R(w)-R3).2;% end% segma_MLE=sqrt(sum(A)/N);segma_MLE=1; temp2=0;for j=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); if A_n2=CV(i) count(i)=count(i)+1; end count1(i)=count(i);endP3(i)=count1(i)/exp_time;display(P3(i);% ave_segma_MLE=ave_segma_MLE/exp_time;% display(ave_segma_MLE); endfor i=1:length(a), if a(i)0.1, aa=N/2; else aa=N; end threshold(i)=2*fzero(x)threshold_pf(x,N/2,a(i),aa)/N; thr=threshold(i)*N; sr=snr*N; Pd_AWGN(i)=marcumq(sqrt(sr),sqrt(thr),N/2);end% display(a);% display(P1,P2,P3); plot(a,Pd_AWGN,b-o,a,P1,b*-,a,P2,ks-,a,P3,ro-); axis(1e-3 1 1e-3 1); xlabel(a:false alarm Probability); ylabel(P:Probability of detection); temp3=ED N=50 SNR=-5 ; temp4=AD m=awgn N=50 SNR=-5 ; temp5=AD m=sin N=50 SNR=-5 ; temp6=AD m=1 N=50 SNR=-5 ; legend(temp3,temp4,temp5,temp6); grid;% loglog(a,P,b-);% axis(1e-3 1 1e-3 1);% %temp0=AWGN local detection, N=1;% % %temp4=AWGN AND rule, K=,num2str(N), theory;%