Matlab：绘制简单能量的接收机工作特性曲线(Energy_detection_simulation_ok)

Matlab：绘制简单能量的接收机工作特性曲线(Energy_detection_simulation_ok)

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输出结果

实现代码

% This code is to plot receiver operating characteristic curve for simple energy
%绘制简单能量的接收机工作特性曲线
% detection, when the primary signal is real Gaussian signal and noise is
% addive white real Gaussian. Here, the threshold is available
% analytically.
% Code written by: Sanket Kalamkar, Indian Institute of Technology Kanpur,
% India.

%% 以下代码绘制在虚警概率一定时，检测概率和信噪比之间的关系曲线称为检测器的检测性能曲线
clc
close all
clear all
L = 1000;                                             % The number of samples
snr = 0.01:0.01:10;
Pf = 10e-4;                                           % Pf = Probability of False Alarm 虚警概率确定
%% Simulation to plot SNR vs.Probability of Detection (Pd)
for m = 1:length(snr)
    i = 0;
    thresh = (qfuncinv(Pf)./sqrt(L))+ 1;              % Theoretical value of Threshold, refer, Sensing-Throughput Tradeoff for Cognitive Radio Networks, Y. C. Liang
    for kk = 1:5000                                   % Number of Monte Carlo Simulations(https://cn.mathworks.com/discovery/monte-carlo-simulation.html)
        n = randn(1,L);                               % AWGN noise with mean 0 and variance 1
        s = sqrt(snr(m)).*randn(1,L);                 % Real valued Gaussina Primary User Signal
        y = s + n;                                    % Received signal at SU(认知用户接收到的信号)
        energy = abs(y).^2;                           % Energy of received signal over N samples
        energy_fin =(1/L).*sum(energy);               % Test Statistic for the energy detection
        if(energy_fin >= thresh)                      % Check whether the received energy is greater than threshold, if so, increment Pd (Probability of detection) counter by 1
            i = i+1;
        end
    end
    Pd(m) = i/kk;
end
plot(10*log(snr), Pd,  'r')
xlabel('信噪比，单位db');
ylabel('检测概率');
title('能量感知检测性能曲线');
grid on
hold on
%% Theroretical expression of Probability of Detection; refer above reference.
thresh = (qfuncinv(Pf)./sqrt(L))+ 1;
%Pd_the = qfunc(((thresh - (snr + 1)).*sqrt(L))./(sqrt(2).*(snr + 1))); % 原来代码中的表达与论文中不一致
for k = 1:length(snr)
    Pd_the(k) = qfunc(((thresh - (snr(k) + 1)).*sqrt(L))./(sqrt(2).*snr(k) + 1)); % 与论文中的方程式保持一致
end
plot(10*log(snr), Pd_the, 'b')
legend('实际检测概率', '理论检测概率', 'Location', 'SouthEast');
hold on
grid on