CR-WSN sensing with WOA

clear; clc; close all;

%% ================= PARAMETERS =================

num_clusters = 5; 

nodes_per_cluster = 10;

cluster_radius = 5;

cluster_distance = 20; 

SNR = -20:2:20;

num_iterations = 500;

%% ================= PREALLOCATION =================

cluster_centers = zeros(num_clusters, 2);

PU_nodes = cell(num_clusters, 1);

SU_nodes = cell(num_clusters, 1);

Pd_Voting = zeros(num_clusters, length(SNR)); 

Pd_Voting_WOA = zeros(num_clusters, length(SNR));

%% ================= CLUSTER GENERATION =================

for i = 1:num_clusters

    % Ensure clusters do not overlap

    valid_position = false;

    while ~valid_position

        x = randi([0, 100]);

        y = randi([0, 100]);

        valid_position = true;

        for j = 1:i-1

            if norm([x y] - cluster_centers(j,:)) < cluster_distance

                valid_position = false;

                break;

            end

        end

    end

    cluster_centers(i,:) = [x y];

    % Generate nodes inside cluster

    nodes = zeros(nodes_per_cluster,2);

    nodes(1,:) = cluster_centers(i,:);  % SU-CH

    for j = 2:nodes_per_cluster

        theta = 2*pi*rand();

        r = cluster_radius * sqrt(rand());

        nodes(j,:) = cluster_centers(i,:) + [r*cos(theta) r*sin(theta)];

    end

    % Random PU selection

    num_PUs = randi([2 4]);

    PU_idx = randperm(nodes_per_cluster-1, num_PUs) + 1;

    PU_nodes{i} = nodes(PU_idx,:);

    SU_nodes{i} = nodes(setdiff(2:nodes_per_cluster, PU_idx), :);

end

%% ================= MONTE CARLO SIMULATION =================

for i = 1:num_clusters

    num_SUs = size(SU_nodes{i},1);

    num_PUs = size(PU_nodes{i},1);

    for snr_idx = 1:length(SNR)

        snr = SNR(snr_idx);

        Pd_Voting_iter = zeros(1,num_iterations);

        Pd_Voting_WOA_iter = zeros(1,num_iterations);

        for iter = 1:num_iterations

            fading = raylrnd(1,num_SUs,num_PUs);

            ED_results = zeros(num_SUs,1);

            for su_idx = 1:num_SUs

                energy_sum = 0;

                for pu_idx = 1:num_PUs

                    noise = randn(1,100);

                    signal = sqrt(10^(snr/10)) * fading(su_idx,pu_idx);

                    received_signal = signal + noise;

                    energy = sum(abs(received_signal).^2)/length(received_signal);

                    energy_sum = energy_sum + energy;

                end

                threshold_ED = chi2inv(0.9,2);

                ED_results(su_idx) = energy_sum > threshold_ED;

            end

            % -------- Voting Logic --------

            voting_threshold = 0.5;

            Pd_Voting_iter(iter) = mean(ED_results) > voting_threshold;

            % -------- Voting + WOA (Improved threshold) --------

            woa_threshold = 0.3;

            Pd_Voting_WOA_iter(iter) = mean(ED_results) > woa_threshold;

        end

        Pd_Voting(i,snr_idx) = mean(Pd_Voting_iter);

        Pd_Voting_WOA(i,snr_idx) = mean(Pd_Voting_WOA_iter);

    end

end

%% ================= Pf COMPUTATION =================

Pf_Voting = 1 - Pd_Voting;

Pf_Voting_WOA = 1 - Pd_Voting_WOA;

%% ================= AVERAGE Pd FOR BAR PLOTS =================

avg_Pd_Voting = mean(Pd_Voting,2);

avg_Pd_Voting_WOA = mean(Pd_Voting_WOA,2);

%% ================= ENERGY MODEL AT SU-CH =================

E_RX_per_SU = 0.01;      % mJ

E_comp_base = 0.05;      % mJ

E_WOA = 0.03;            % mJ

avg_Energy_Voting = zeros(num_clusters,1);

avg_Energy_WOA = zeros(num_clusters,1);

for i = 1:num_clusters

    num_SUs = size(SU_nodes{i},1);

    % Voting Energy

    avg_Energy_Voting(i) = (num_SUs * E_RX_per_SU) + E_comp_base;

    % Voting + WOA Energy

    avg_Energy_WOA(i) = (num_SUs * E_RX_per_SU) + E_comp_base + E_WOA;

end

%% ================= PLOT 1: Pd vs SNR =================

figure;

plot(SNR, mean(Pd_Voting,1), 'o-','LineWidth',2); hold on;

plot(SNR, mean(Pd_Voting_WOA,1), 's-','LineWidth',2);

xlabel('SNR (dB)');

ylabel('Probability of Detection (P_d)');

title('P_d vs SNR');

legend('Voting','Voting with WOA','Location','southeast');

ylim([0 1]);

grid on;

%% ================= PLOT 2: Pf vs SNR =================

figure;

plot(SNR, mean(Pf_Voting,1), 'o-','LineWidth',2); hold on;

plot(SNR, mean(Pf_Voting_WOA,1), 's-','LineWidth',2);

xlabel('SNR (dB)');

ylabel('Probability of False Alarm (P_f)');

title('P_f vs SNR');

legend('Voting','Voting with WOA','Location','northeast');

ylim([0 1]);

grid on;

%% ================= PLOT 3: Avg Energy at SU-CH =================

figure;

bar([avg_Energy_Voting avg_Energy_WOA],'grouped');

xlabel('Cluster Index');

ylabel('Average Energy Consumed at SU-CH (mJ)');

title('Avg Energy Consumed at SU-CH vs Number of Clusters');

legend('Voting','Voting with WOA','Location','northwest');

grid on;

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