New folder (2).zip_cognitive code_cognitive radio_matlab

%for Energy detection %probabilty of false alarm Energy Detection N=5000; L=1000; pfc=0.01:0.01:1; pfe=1-sqrt(1-pfc); pfm=(pfc-pfe)./(1-pfe); %-----SNR in decibels-----% snr_dB = -20; snr_dB1 = -14; snr_dB2 = -12; snr_dB3 = -10; snr_dB4=-8; %-----Linear Value of SNR-----% snr = 10.^(snr_dB./10); snr1 = 10.^(snr_dB1./10); snr2 = 10.^(snr_dB2./10); snr3 = 10.^(snr_dB3./10); snr4 = 10.^(snr_dB4./10); %-----Probability of False Alarm-----% % Pf = 0.01:0.01:1; %% Simulation to plot Probability of Detection (Pd) vs. Probability of False Alarm (Pf) % for m = 1:length(pfe) Detect = 0; for kk=1:1000 % Number of Monte Carlo Simulations %-----AWGN noise with mean 0 and variance 1-----% Noise = randn(1,L); %-----Real valued Gaussian Primary User Signal------% Signal = sqrt(snr).*randn(1,L); Recv_Sig = Signal + Noise; % Received signal at SU Energy = abs(Recv_Sig).^2; % Energy of received signal over N samples %-----Computation of Test statistic for energy detection-----% Test_Statistic =(1/L).*sum(Energy); %-----Theoretical value of Threshold-----% Threshold(m) = (qfuncinv(pfe(m))./sqrt(L))+ 1; if(Test_Statistic >= Threshold(m)) % Check whether the received energy is greater than threshold, if so, increment Pd (Probability of detection) counter by 1 Detect = Detect+1; end end % Pd(m) = Detect/kk; end % plot(pfe, Pd) % hold on %% Theroretical expression of Probability of Detection Threshold =qfuncinv(pfe)./sqrt(L)+ 1; Pd_the = qfunc(((Threshold - (snr + 1)).*sqrt(L))./2*(sqrt(2).*(snr + 1))); Pd_the1 = qfunc(((Threshold - (snr1 + 1)).*sqrt(L))./2*(sqrt(2).*(snr1 + 1))); Pd_the2 = qfunc(((Threshold - (snr2 + 1)).*sqrt(L))./2*(sqrt(2).*(snr2 + 1))); Pd_the3 = qfunc(((Threshold - (snr3 + 1)).*sqrt(L))./2*(sqrt(2).*(snr3 + 1))); Pd_the4 = qfunc(((Threshold - (snr4 + 1)).*sqrt(L))./2*(sqrt(2).*(snr4+ 1))); % Pd_the5 = qfunc(((Threshold - (snr5 + 1)).*sqrt(L))./2*(sqrt(2).*(snr5+ 1))); figure(1) semilogx(pfe, Pd_the,'r','LineWidth',2) hold on semilogx(pfe, Pd_the1,'g','LineWidth',2) hold on semilogx(pfe, Pd_the2,'b','LineWidth',2) hold on semilogx(pfe, Pd_the3,'c','LineWidth',2) hold on semilogx(pfe, Pd_the4,'k','LineWidth',2) axis([0 1 0 1.1]) grid on title('ED ROC curves for Probability of False Alarm vs Probability of Detection N=5000 sample'); xlabel('Probability Of False Alarm'); ylabel('Probability Of Detection'); legend('SNR= -20dB','SNR= -14dB','SNR= -12dB','SNR= -10dB','SNR= -8dB'); %% Simulation to plot Probability of Detection (Pd) vs. Probability of False Alarm (Pf) % L=1000; pfc=0.01:0.01:1; pfe=1-sqrt(1-pfc); pfm=(pfc-pfe)./(1-pfe); i_pfa=1:length(pfm); for m = 1:length(pfm) Detect = 0; for kk=1:1000 % Number of Monte Carlo Simulations %-----AWGN noise with mean 0 and variance 1-----% Noise = randn(1,L); %-----Real valued Gaussian Primary User Signal------% Signal = sqrt(snr).*randn(1,L); Recv_Sig = Signal + Noise; % Received signal at SU Energy = abs(Recv_Sig).^2; % Energy of received signal over N samples %-----Computation of Test statistic for energy detection-----% Test_Statistic =(1/L).*sum(Energy); %-----Theoretical value of Threshold-----% % Threshold(m) = (qfuncinv(Pf(m))./sqrt(L))+ 1; Threshold(i_pfa)=(sqrt(N)+sqrt(L))^2/(sqrt(N)-sqrt(L))^2*(1+(sqrt(N)+sqrt(L))^(-2/3)/(N*L)^(1/6)*finv(1-pfm,1,1)); if(Test_Statistic >= Threshold(i_pfa)) % Check whether the received energy is greater than threshold, if so, increment Pd (Probability of detection) counter by 1 Detect = Detect+1; end end % Pd(m) = Detect/kk; end % plot(Pf, Pd) % hold on %% Theroretical expression of Probability of Detection Threshold = (qfuncinv(pfm)./sqrt(L))+ 1; Pd_them = qfunc(((Threshold - (snr + 1)).*sqrt(L))./2*(sqrt(2).*(snr + 1))); Pd_the1m = qfunc(((Threshold - (snr1 + 1)).*sqrt(L))./2*(sqrt(2).*(snr1 + 1))); Pd_the2m = qfunc(((Threshold - (snr2 + 1)).*sqrt(L))./2*(sqrt(2).*(snr2 + 1))); Pd_the3m = qfunc(((Threshold - (snr3 + 1)).*sqrt(L))./2*(sqrt(2).*(snr3 + 1))); Pd_the4m = qfunc(((Threshold - (snr4 + 1)).*sqrt(L))./2*(sqrt(2).*(snr4+ 1))); figure(2) semilogx(pfm, Pd_them,'r','LineWidth',2) hold on semilogx(pfm, Pd_the1m,'g','LineWidth',2) hold on semilogx(pfm, Pd_the2m,'b','LineWidth',2) hold on semilogx(pfm, Pd_the3m,'c','LineWidth',2) hold on semilogx(pfm, Pd_the4m,'k','LineWidth',2) axis([0 1 0 1.1]) grid on title('MME detector ROC curves for Probability of False Alarm vs Probability of Detection N=5000 sample'); xlabel('Probability Of False Alarm'); ylabel('Probability Of Detection'); legend('SNR= -20dB','SNR= -14dB','SNR= -12dB','SNR= -10dB','SNR= -8dB'); %for 2EMC %probabilty of false alarm Energy Detection Pdc=Pd_the+(1-Pd_the).*Pd_them; Pdc1=Pd_the1+(1-Pd_the1).*Pd_the1m; Pdc2=Pd_the2+(1-Pd_the2).*Pd_the2m; Pdc3=Pd_the3+(1-Pd_the3).*Pd_the3m; Pdc4=Pd_the4+(1-Pd_the4).*Pd_the4m; figure(3) semilogx(pfc,Pdc,'r','LineWidth',2) hold on semilogx(pfc, Pdc1,'g','LineWidth',2) hold on semilogx(pfc, Pdc2,'b','LineWidth',2) hold on semilogx(pfc, Pdc3,'c','LineWidth',2) hold on semilogx(pfc, Pdc4,'k','LineWidth',2) axis([0 1 0 1.1]) grid on title('2EMC ROC curves for Probability of False Alarm vs Probability of Detection N=5000 sample'); xlabel('Probability Of False Alarm'); ylabel('Probability Of Detection'); legend('SNR= -20dB','SNR= -14dB','SNR= -12dB','SNR= -10dB','SNR= -8dB'); %Compare Roc when SNR=-12dB figure(4) semilogx(pfe, Pd_the2,'b','LineWidth',2) hold on semilogx(pfm, Pd_the2m,'r','LineWidth',2) hold on semilogx(pfc, Pdc2,'g','LineWidth',2) grid on axis([0 1 0 1.1]) title('compare graph SNR=-12dB'); xlabel('Probability Of False Alarm'); ylabel('Probability Of Detection'); legend('ED','MME','2EMC');