clc
clear
close all;

SNR = 10;
sigma_n = 0.1;
P_fa = [1e-5, 5e-5, 1e-4, 5e-4, 1e-3, 5e-3, 1e-2, 5e-2, 1e-1, 5e-1, 1];
len_P_fa = length(P_fa);

h0_rep_time = 99 * 1e4;
h1_rep_time = 1e5;
% h0_rep_time = 1000;
% h1_rep_time = 1000;

a = sqrt(10^(SNR/10) * sigma_n^2);

P_fa_node_cnt = zeros(len_P_fa, h0_rep_time);

parfor rep = 1: h0_rep_time
% for rep = 1: h0_rep_time
    
    %% 噪声处理
    noise = random('Normal', 0, sigma_n/sqrt(2), 1, 1) + 1j * random('Normal', 0, sigma_n/sqrt(2), 1, 1);
    
    y = noise;
    
    stat = abs(y)^2;

    kd = sigma_n^2 * chi2inv(1 - P_fa, 2) / 2;

    for cnt_h_th = 1: len_P_fa
        P_fa_node_cnt(cnt_h_th, rep) = stat > kd(cnt_h_th);
    end

    fprintf('Pfa-%d\n', rep);
    
end

P_fa_node = mean(P_fa_node_cnt, 2);

figure(1)
loglog(P_fa,P_fa_node)
title('Pfa')



P_d_node_cnt = zeros(len_P_fa, h1_rep_time);

parfor rep = 1: h1_rep_time
% for rep = 1: h1_rep_time
    
    %% 噪声处理
    noise = random('Normal', 0, sigma_n/sqrt(2), 1, 1) + 1j * random('Normal', 0, sigma_n/sqrt(2), 1, 1);
    
    y = a + noise;
    
    stat = abs(y)^2;

    kd = sigma_n^2 * chi2inv(1 - P_fa, 2) / 2;

    for cnt_h_th = 1: len_P_fa
        P_d_node_cnt(cnt_h_th, rep) = stat > kd(cnt_h_th);
    end

    fprintf('Pd-%d\n', rep);
    
end

P_d_node = mean(P_d_node_cnt, 2);

figure(2)
loglog(P_fa,P_d_node)
title('Pd')

figure(3)
loglog(P_fa_node,P_d_node)
title('ROC')

save node_detect.mat ...
    SNR...
    P_fa...
    P_fa_node...
    P_d_node...
    sigma_n...
    h0_rep_time...
    h1_rep_time...
    a;