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Hi all,

i´m actually trying to compute a novel energy detection algorithm based on the algorithm of the paper "An enhanced energy detection algorithm in cognitive radio" from Mr.Hao and Mr.Zu.

Here is my matlab code:

clear all;clc;close all;

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% DVB-T Signal

load resampled_594.mat

rx_sig= iq_resp ;

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% intiating noise variables

noise_power = 1;

snr=-35:5:-25; % in dB

%n_test(1:length(snr)) = 1e3;

n_test =[100 200];% 300 400 500 600 700 800 900 1000 2000 3000 4000 10000];

sensing_len=457000;% 50ms "channel BW = 9.14 MHz"

pf=0.01;

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Taking sample of the captured signal

sig=sample_real(rx_sig,sensing_len);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Apply Rayleigh Fading Channel

sig_channel = fading(sig,'Rayleigh');

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% normalize signal power to 1 without Rayleigh Fading Channel

power_sig_before= POWER(sig);

scaled_sig1_before=sig./sqrt(power_sig_before);

power_scaledsig1_before=POWER(scaled_sig1_before);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% normalize signal power to 1 with Rayleigh Fading Channel

power_sig_after= POWER(sig_channel);

scaled_sig1_after=sig_channel./sqrt(power_sig_after);

power_scaledsig1_after=POWER(scaled_sig1_after);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Generated Normal Random Guassian Signal

v_scaled_before=(randn(1, sensing_len)+randn(1, sensing_len)*(1j))*sqrt(1/2);

% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Apply Rayeigh Fading

v_channel = fading(v_scaled_before,'Rayleigh');

v_channel_pwr=POWER(v_channel);

v_scaled_after=v_channel/ sqrt(v_channel_pwr);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

for jj=1:length(snr)

for ii=1:n_test(jj)

%%%% Generating NOISE

noise=(randn(1, sensing_len)+randn(1, sensing_len)*(1j))*sqrt(noise_power/2);

%%%% Test Statisitic for noise

noise_pwr(ii) = noise(1,:)*noise(1,:)' / sensing_len;

%%%% Scaling without and with Rayleigh Faidng According to diff. SNRs

p_targeted= noise_power.* (10.^(snr(jj)/10));

scaled_sig_before(1,:)= scaled_sig1_before .* sqrt(p_targeted);

scaled_sig_after(1,:)= scaled_sig1_after .* sqrt(p_targeted);

scaled_v_before(1,:)= v_scaled_before .* sqrt(p_targeted);

scaled_v_after(1,:)= v_scaled_after .* sqrt(p_targeted);

%%%% Adding Signal to noise

signal_noise_before(1,:)= scaled_sig_before(1,:)+noise(1,:);

signal_noise_after(1,:)= scaled_sig_after(1,:)+noise(1,:);

v_noise_before(1,:)= scaled_v_before+noise(1,:);

v_noise_after(1,:)= scaled_v_after+noise(1,:);

%%%% Test Statistic

power_before(ii+((jj-1)*n_test(jj)))=signal_noise_before(1,:)*signal_noise_before(1,:)'/length(signal_noise_before(1,:));

power_v_before(ii+((jj-1)*n_test(jj)))=v_noise_before(1,:)*v_noise_before(1,:)'/length(v_noise_before(1,:));

%%%% Test Statistic with Fading

power_after(ii+((jj-1)*n_test(jj)))=signal_noise_after(1,:)*signal_noise_after(1,:)'/length(signal_noise_after(1,:));

power_v_after(ii+((jj-1)*n_test(jj)))=v_noise_after(1,:)*v_noise_after(1,:)'/length(v_noise_after(1,:));

end

end

%%%%%% sim. thr

noise_pwr = sort(noise_pwr, 'descend');

display('For PFA=0.01')

thr_accurat = noise_pwr(n_test(end)*pf)

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Generating threshold using theoritical closed form

% display('For PFA=0.01')

% thr_accurat = chi2inv(1-pf,2*sensing_len)/(((2*sensing_len)/100)*100 )

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Generating misdetection Prob.

c=0;

for jj=1:length(snr)

cmd_accurat=0;

cmd_accurat_after=0;

cmd_v_accurat=0;

cmd_v_accurat_after=0;

for ii=1:n_test(jj)

if power_before(ii+(c*n_test(jj)))<thr_accurat

cmd_accurat=cmd_accurat+1;

end

if power_after(ii+(c*n_test(jj)))<thr_accurat

cmd_accurat_after=cmd_accurat_after+1;

end

if power_v_before(ii+(c*n_test(jj)))<thr_accurat

cmd_v_accurat=cmd_v_accurat+1;

end

if power_v_after(ii+(c*n_test(jj)))<thr_accurat

cmd_v_accurat_after=cmd_v_accurat_after+1;

end

end

pmd_accurat(1,jj)=cmd_accurat/n_test(jj);

pmd_accurat_after(1,jj)=cmd_accurat_after/n_test(jj);

pmd_v_accurat(1,jj)=cmd_v_accurat/n_test(jj);

pmd_v_accurat_after(1,jj)=cmd_v_accurat_after/n_test(jj);

c=c+1;

end

display('For PFA=0.01')

pmd_accurat

pmd_accurat_after

pmd_v_accurat

pmd_v_accurat_after

figure(1)

semilogy(snr,pmd_accurat,':rs')

hold on

semilogy(snr,pmd_accurat_after,'--b*')

semilogy(snr,pmd_v_accurat,'-gh')

semilogy(snr,pmd_v_accurat_after,'-mp')

title('SNR vs. Misdetection Probability')

xlabel('SNR')

ylabel('Misdetection Probability')

h=legend('Captured DVB-T "8k"mode','Captured DVB-T "8k"mode, Rayleigh','Random Guassian','Random Guassian, Rayleigh',2);

set(h,'Interpreter','none')

grid on

save Energy_det_with_without_fading_real.mat snr thr_accurat pmd_accurat pmd_accurat_after pmd_v_accurat pmd_v_accurat_after

How should i change it?

I´ve an idea about the series to parallel changement but my problem is how to find the T(duration time)and the W(bandwidth of the signal)because i need them to compute the threshold value!!!

If you´va any other idea about an enhanced energy detection algorithm let me know please,don´t hesitate if you´ve also questions about cognitive radio and energy detection.

Thank you very much

you have not given functions and the .mat files which are required to run this file.

"Samer " <samerclub@yahoo.fr> wrote in message <idqfnh$h39$1@fred.mathworks.com>...

> Hi all,

>

> i´m actually trying to compute a novel energy detection algorithm based on the algorithm of the paper "An enhanced energy detection algorithm in cognitive radio" from Mr.Hao and Mr.Zu.

>

> Here is my matlab code:

> clear all;clc;close all;

> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% DVB-T Signal

> load resampled_594.mat

> rx_sig= iq_resp ;

> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% intiating noise variables

> noise_power = 1;

> snr=-35:5:-25; % in dB

> %n_test(1:length(snr)) = 1e3;

> n_test =[100 200];% 300 400 500 600 700 800 900 1000 2000 3000 4000 10000];

> sensing_len=457000;% 50ms "channel BW = 9.14 MHz"

> pf=0.01;

> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Taking sample of the captured signal

> sig=sample_real(rx_sig,sensing_len);

> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Apply Rayleigh Fading Channel

> sig_channel = fading(sig,'Rayleigh');

> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% normalize signal power to 1 without Rayleigh Fading Channel

> power_sig_before= POWER(sig);

> scaled_sig1_before=sig./sqrt(power_sig_before);

> power_scaledsig1_before=POWER(scaled_sig1_before);

> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% normalize signal power to 1 with Rayleigh Fading Channel

> power_sig_after= POWER(sig_channel);

> scaled_sig1_after=sig_channel./sqrt(power_sig_after);

> power_scaledsig1_after=POWER(scaled_sig1_after);

> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Generated Normal Random Guassian Signal

> v_scaled_before=(randn(1, sensing_len)+randn(1, sensing_len)*(1j))*sqrt(1/2);

> % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Apply Rayeigh Fading

> v_channel = fading(v_scaled_before,'Rayleigh');

> v_channel_pwr=POWER(v_channel);

> v_scaled_after=v_channel/ sqrt(v_channel_pwr);

> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

> for jj=1:length(snr)

> for ii=1:n_test(jj)

> %%%% Generating NOISE

> noise=(randn(1, sensing_len)+randn(1, sensing_len)*(1j))*sqrt(noise_power/2);

> %%%% Test Statisitic for noise

> noise_pwr(ii) = noise(1,:)*noise(1,:)' / sensing_len;

> %%%% Scaling without and with Rayleigh Faidng According to diff. SNRs

> p_targeted= noise_power.* (10.^(snr(jj)/10));

> scaled_sig_before(1,:)= scaled_sig1_before .* sqrt(p_targeted);

> scaled_sig_after(1,:)= scaled_sig1_after .* sqrt(p_targeted);

> scaled_v_before(1,:)= v_scaled_before .* sqrt(p_targeted);

> scaled_v_after(1,:)= v_scaled_after .* sqrt(p_targeted);

> %%%% Adding Signal to noise

> signal_noise_before(1,:)= scaled_sig_before(1,:)+noise(1,:);

> signal_noise_after(1,:)= scaled_sig_after(1,:)+noise(1,:);

> v_noise_before(1,:)= scaled_v_before+noise(1,:);

> v_noise_after(1,:)= scaled_v_after+noise(1,:);

> %%%% Test Statistic

> power_before(ii+((jj-1)*n_test(jj)))=signal_noise_before(1,:)*signal_noise_before(1,:)'/length(signal_noise_before(1,:));

> power_v_before(ii+((jj-1)*n_test(jj)))=v_noise_before(1,:)*v_noise_before(1,:)'/length(v_noise_before(1,:));

> %%%% Test Statistic with Fading

> power_after(ii+((jj-1)*n_test(jj)))=signal_noise_after(1,:)*signal_noise_after(1,:)'/length(signal_noise_after(1,:));

> power_v_after(ii+((jj-1)*n_test(jj)))=v_noise_after(1,:)*v_noise_after(1,:)'/length(v_noise_after(1,:));

> end

> end

> %%%%%% sim. thr

> noise_pwr = sort(noise_pwr, 'descend');

> display('For PFA=0.01')

> thr_accurat = noise_pwr(n_test(end)*pf)

> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Generating threshold using theoritical closed form

> % display('For PFA=0.01')

> % thr_accurat = chi2inv(1-pf,2*sensing_len)/(((2*sensing_len)/100)*100 )

> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Generating misdetection Prob.

> c=0;

> for jj=1:length(snr)

> cmd_accurat=0;

> cmd_accurat_after=0;

> cmd_v_accurat=0;

> cmd_v_accurat_after=0;

> for ii=1:n_test(jj)

> if power_before(ii+(c*n_test(jj)))<thr_accurat

> cmd_accurat=cmd_accurat+1;

> end

> if power_after(ii+(c*n_test(jj)))<thr_accurat

> cmd_accurat_after=cmd_accurat_after+1;

> end

> if power_v_before(ii+(c*n_test(jj)))<thr_accurat

> cmd_v_accurat=cmd_v_accurat+1;

> end

> if power_v_after(ii+(c*n_test(jj)))<thr_accurat

> cmd_v_accurat_after=cmd_v_accurat_after+1;

> end

> end

>

> pmd_accurat(1,jj)=cmd_accurat/n_test(jj);

> pmd_accurat_after(1,jj)=cmd_accurat_after/n_test(jj);

> pmd_v_accurat(1,jj)=cmd_v_accurat/n_test(jj);

> pmd_v_accurat_after(1,jj)=cmd_v_accurat_after/n_test(jj);

> c=c+1;

> end

> display('For PFA=0.01')

> pmd_accurat

> pmd_accurat_after

> pmd_v_accurat

> pmd_v_accurat_after

> figure(1)

> semilogy(snr,pmd_accurat,':rs')

> hold on

> semilogy(snr,pmd_accurat_after,'--b*')

> semilogy(snr,pmd_v_accurat,'-gh')

> semilogy(snr,pmd_v_accurat_after,'-mp')

> title('SNR vs. Misdetection Probability')

> xlabel('SNR')

> ylabel('Misdetection Probability')

> h=legend('Captured DVB-T "8k"mode','Captured DVB-T "8k"mode, Rayleigh','Random Guassian','Random Guassian, Rayleigh',2);

> set(h,'Interpreter','none')

> grid on

> save Energy_det_with_without_fading_real.mat snr thr_accurat pmd_accurat pmd_accurat_after pmd_v_accurat pmd_v_accurat_after

>

> How should i change it?

> I´ve an idea about the series to parallel changement but my problem is how to find the T(duration time)and the W(bandwidth of the signal)because i need them to compute the threshold value!!!

> If you´va any other idea about an enhanced energy detection algorithm let me know please,don´t hesitate if you´ve also questions about cognitive radio and energy detection.

> Thank you very much

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