Have a look at https://au.mathworks.com/matlabcentral/answers/840-how-to-plot-a-simple-curve

https://au.mathworks.com/matlabcentral/answers/475415-how-to-plot-the-curve

https://www.capterra.com/statistical-analysis-software/

If independent variable : X = [x1, x2, ..., xn], dependent variable Y = [y1, y2, ..., yn], then plot(X,Y). To create a plot with a linear scale on the x-axis and a log (base 10) scale on the y-axis you can use the function semilogy. To create a plot with a linear scale on the x-axis and a log (base 10) scale on the x-axis you can use the function semilogx.

Try this:

https://in.mathworks.com/help/phased/examples/detector-performance-analysis-using-roc-curves.html

Hi Anil Keshari, elaborate what exactly you are seeking.

If you want to understand how MATLAB code is written/working, write that particular code for Pd vs SNR (dB) here. I can include line by line comment to it for the sake of your understanding. Or if it is related to the theoretical derivation of the probability of detection, we can discuss the same.

I hope it will help you.

Cheers!

Hi, Prasanna Raut I have mentioned below. please look into the problem. The theoretical code is working perfectly fine.

clc; close all;

clear all;

SNRdB=-10:.2:20;

r = 2:1:8;

Pfa = 10.^(1-r);

for i=1:length(Pfa)

inv_q=qfuncinv(Pfa(i));

for k=1:length(SNRdB)

SNR=10^(SNRdB(k)/10);

sigma=1/sqrt(SNR);

gama=sigma*inv_q;

Pd(k)=qfunc((gama-1)/sigma);

end

plot(SNRdB,Pd)

hold on

axis tight

grid on

end

xlabel('SNR(dB)');

ylabel('Pd');

title('Pd VS SNR(dB)');

legend('Pfa = 10^-1','Pfa = 10^-2','Pfa = 10^-3','Pfa = 10^-4','Pfa = 10^-5','Pfa = 10^-6','Pfa = 10^-7','location', 'northwest' )

hold off

----------------------------------------------------------------------------------

above code is Pd vs SNRdB. but this is theoreticaly simulated i want to add lets say i transmitted the bits and recieved and then i want to make binary hypothesis to test that the recieved signal lies in which one of the hypothesis by considering threshold as deciding factor. then from there i can calculate ROC also by deciding PD vs PFAand plot Pd vs SNRdb also.

This code is valid when you are using Energy Detection

%% Probability of detection

clear % Clear all data stored in variables

Am = 1; % 1V Amplitude

SR = 48000; % 48 KHz Smapling Rate

f0 = 20e+3; % 20 KHz

duration = (127/SR); % ~ 2.6 ms

t = 0:1/SR:duration; % Time Vector

%%

sinusPulse = Am*sin(2*pi*f0*t);

mySNR = -30:1:30; % SNR buffer

threshold = 150;

detectProbability(sinusPulse,mySNR,threshold)

%%

function detectProbability(yourSignal,snr,threshold)

N = length(yourSignal);

for i = 1:length(snr)

Pd_buff(100) = 0;

for j = 1:100

a(N) = 100;

yourSignal = awgn(yourSignal,snr(i));

a(1) = abs(yourSignal(1)).^2;

for k = 2:N

a(k) = a(k-1) + abs(yourSignal(k)).^2;

end

Pd_buff(j) = sum(a > threshold)/N;

end

Pd = sum(Pd_buff)/100;

plot(snr(i),Pd_ED,'b+');

hold on

title('Energy Detection')

xlabel('SNR (db)')

ylabel('Probaility of Detection')

end

hold off

end

via Mathworks.com