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From: Mandana on 3 Aug 2010 19:13
Hi
I am working in cyclostationary detection in cognitive radio, I was wondering is any body can help me in this area , I can draw SCF of every signal but I could not calculate Pdetection VS SNR, Is any body here can helo me I don't have enough time.
Have good times
Mandana



"Quan " <alex-hjgc(a)163.com> wrote in message <hmacq9$2th$1(a)fred.mathworks.com>...
> hello,every one!
> As a novice of cognitive radio, I am now very interesting in the topic of energy detection (ED), especially the performance of ED over different fading channels.
> Accoring to some papers, like On the energy detection of unknown signals over fading channels (which was included in IEEE Xplore, 2003). I have done some simulation about this in Matlab 2009b: Firstly, produce a real signal(single tone or BPSK signal), and then detect them after transmission over different channels with ED theory. However, the problem is: the complementary ROC curve (Pm-Pf) obtained by this simulation is very different from the theory results and analysis given in your papers. I have also referred some other high cited papers but their results are the same with yours, so I am very eager to wonder where the problem is? It seems that I have some misunderstanding of the detection process.
> I haven&#8217;t found any solution for this problem after a long-time serious check. So I&#8217;d like to resort to your help. Part of my Mathlab script is attached as follows. Did you ever do such a simulation? Or If you have any program codes related to ED Simulation, could you please send me some for study?
>
> Matlab Simulation Code: (Is there any problem ??)
> close all;
> clear all;
> clc;
>
> m=5;
> N =2*m; %sample points N=T/Ts=T/1/2W=2TW
> Base= 0.01:0.02:1;
> Pf =Base.^2; %False alarm
> snr_avgdB =5; %SNR=Ps/(N0*W)
> snr_avg = power(10,snr_avgdB/10); %db to linear snr
> for i=1:length(Pf)
> Over_Num = 0;
> for kk = 1:1000 %Monter-Carlo times
> %If Transimmting Single tone
> t = 1:N; %samples
> x = sin(pi*t/2); %single tone, Fs=2F0
> amp = sqrt(1/2/snr_avg); %amp. for noise, avg power of signal=1/2
> noise = amp*randn(1,N); %Noise production
> pn = (std(noise)).^2; %Noise power average
>
> % %If trans. BPSK singnal
> % Ns=10;%bits Number
> % M=2;
> % Fd=1;%code rate
> % Fs=1;%sample freq.
> % a=randint(1,Ns,M); %source
> % x=dmodce(a,Fd,Fs,'psk',M); %BPSK baseband singal
> % amp = sqrt(1/snr_avg); %amp. for noise,, avg power of signal=1
> % noise = amp*randn(1,N); %Noise production
> % pn = (std(noise)).^2; %Noise power average
>
> signal = x(1:N);
> ps = mean(abs(signal).^2); %signal average power
>
> %awgn channels
> Rev_sig = signal + noise; %received signal for detection
>
> Th(i) = gaminv(1-Pf(i),m,1)*2; %Threshold for given Pf(i) according to Digham 2003
> accum_power(i) = sum(abs(Rev_sig.^2))/pn; %accumulated received power(normalized to noise power)
>
> if accum_power(i)>Th(i)
> Over_Num = Over_Num +1; %decide 1 or 0, present or absent
> end
> end
> Pd_sim(i) = Over_Num / kk; %Detection probability computation
> Pd_theory(i) = marcumq(sqrt(snr_avg*2*m),sqrt(Th(i)),m);
> %Theory results according Digham 2003 in awgn channel
> Pd_appm(i) = 0.5*erfc((sqrt(2)*erfcinv(2*Pf(i))-snr_avg*sqrt(m))/sqrt(2+4*snr_avg));
> %approximation when m>100, chi-squre appoximate gaussion
> end
>
> Pm_sim =1-Pd_sim;
> Pm_theory = 1- Pd_theory;
> Pm_appm =1-Pd_appm;
>
> figure
> loglog(Pf,Pm_sim,'*r',Pf,Pm_theory,'-.k',Pf,Pm_appm,'--b');%Complementary ROC Curve
>
> title('Complementary ROC of ED under AWGN')
> grid on
> axis([0.0001,1,0.0001,1]);
> xlabel('Pf');
> ylabel('Pm');
> legend('Simulation','Theory','Arrpoximation');
>
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