CR-WSN Simulation Analysis

% Parameters

num_clusters = 4;

nodes_per_cluster = 10;

cluster_radius = 5;

cluster_distance = 20; % Minimum distance between clusters

% Preallocate arrays for cluster centers and node positions

cluster_centers = zeros(num_clusters, 2);

node_positions = cell(num_clusters, 1);

PU_nodes = cell(num_clusters, 1);

SU_nodes = cell(num_clusters, 1);

for i = 1:num_clusters

    % Generate unique cluster center

    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 within cluster

    nodes = zeros(nodes_per_cluster, 2);

    nodes(1, :) = cluster_centers(i,:); % Cluster head at center

    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

     % Assign nodes to clusters

    node_positions{i} = nodes;

    % Assign PU and SU nodes

    num_PUs = randi([2, 4]);

    PU_idx = randperm(nodes_per_cluster-1, num_PUs) + 1; % Avoiding the CH

    PU_nodes{i} = nodes(PU_idx, :);

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

end

figure;

hold on;

for i = 1:num_clusters

    plot(cluster_centers(i,1), cluster_centers(i,2), 'ro', 'MarkerSize', 10, 'LineWidth', 2);

    plot(PU_nodes{i}(:,1), PU_nodes{i}(:,2), 'bs', 'MarkerSize', 8, 'LineWidth', 2);

    plot(SU_nodes{i}(:,1), SU_nodes{i}(:,2), 'g^', 'MarkerSize', 8, 'LineWidth', 2);

    viscircles(cluster_centers(i,:), cluster_radius, 'LineStyle', '--');

end

xlabel('X-coordinate');

ylabel('Y-coordinate');

title('CR-WSN Topology');

legend('Cluster Head', 'Primary User', 'Secondary User');

hold off;

% Monte Carlo Simulation for SNR values

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_cluster_ED = zeros(1, num_iterations);

        Pf_cluster_ED = zeros(1, num_iterations);

        Pd_cluster_AND = zeros(1, num_iterations);

        Pf_cluster_AND = zeros(1, num_iterations);

        Pd_cluster_OR = zeros(1, num_iterations);

        Pf_cluster_OR = zeros(1, num_iterations);

        Pd_cluster_Voting = zeros(1, num_iterations);

        Pf_cluster_Voting = zeros(1, num_iterations);

        for iter = 1:num_iterations

            % Generate Rayleigh fading channel

            fading = raylrnd(1, num_SUs, num_PUs);

            % Simulate SU sensing PUs for ED

            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);

                    received_signal = sqrt(10^(snr/10)) * fading(su_idx, pu_idx) + noise;

                    % Energy detection

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

                    energy_sum = energy_sum + energy;

                end

               % Threshold for ED

                threshold_ED = chi2inv(0.9, 2); % Assuming a fixed threshold for simplicity

                ED_results(su_idx) = energy_sum > threshold_ED;

            end

            Pd_cluster_ED(iter) = mean(ED_results); % Probability of detection

            Pf_cluster_ED(iter) = mean(~ED_results); % Probability of false alarm

             % Collaborative sensing using AND logic

            Pd_cluster_AND(iter) = all(ED_results);

            Pf_cluster_AND(iter) = ~any(ED_results);

            % Collaborative sensing using OR logic

            Pd_cluster_OR(iter) = any(ED_results);

            Pf_cluster_OR(iter) = ~all(ED_results);

            % Collaborative sensing using Voting logic

            % Combine results from AND and OR logic

            % Voting logic: weighted average of AND and OR results

            voting_threshold = 0.5; % Adjust this threshold to balance detection and false alarm

            Pd_cluster_Voting(iter) = (mean(ED_results) + Pd_cluster_AND(iter) + Pd_cluster_OR(iter)) / 3 >  voting_threshold;

            Pf_cluster_Voting(iter) = (1 - mean(ED_results) + (1 - Pd_cluster_AND(iter)) + (1 - Pd_cluster_OR(iter))) / 3 > voting_threshold;

        end

        Pd_ED(i, snr_idx) = mean(Pd_cluster_ED);

        Pf_ED(i, snr_idx) = mean(Pf_cluster_ED);

        Pd_AND(i, snr_idx) = mean(Pd_cluster_AND);

        Pf_AND(i, snr_idx) = mean(Pf_cluster_AND);

        Pd_OR(i, snr_idx) = mean(Pd_cluster_OR);

        Pf_OR(i, snr_idx) = mean(Pf_cluster_OR);

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

        Pf_Voting(i, snr_idx) = mean(Pf_cluster_Voting);

    end

end

% Plot Probability of Detection vs SNR for ED, AND, OR, and Voting logic using subplots

figure;

for i = 1:num_clusters

    subplot(ceil(sqrt(num_clusters)), ceil(sqrt(num_clusters)), i);

    hold on;

    plot(SNR, Pd_ED(i, :), '-o', 'DisplayName', 'ED');

    plot(SNR, Pd_AND(i, :), '--x', 'DisplayName', 'AND');

    plot(SNR, Pd_OR(i, :), '-.*', 'DisplayName', 'OR');

    plot(SNR, Pd_Voting(i, :), '-s', 'DisplayName', 'Voting');

    xlabel('SNR (dB)');

    ylabel('Probability of Detection');

    title(['Cluster ' num2str(i)]);

    legend('show');

    hold off;

end

sgtitle('Probability of Detection vs SNR for ED, AND, OR, and Voting logic across Clusters');

% Plot Probability of False Alarm vs SNR for ED, AND, OR, and Voting logic using subplots

figure;

for i = 1:num_clusters

    subplot(ceil(sqrt(num_clusters)), ceil(sqrt(num_clusters)), i);

    hold on;

    plot(SNR, Pf_ED(i, :), '-o', 'DisplayName', 'ED');

    plot(SNR, Pf_AND(i, :), '--x', 'DisplayName', 'AND');

    plot(SNR, Pf_OR(i, :), '-.*', 'DisplayName', 'OR');

    plot(SNR, Pf_Voting(i, :), '-s', 'DisplayName', 'Voting');

    xlabel('SNR (dB)');

    ylabel('Probability of False Alarm');

    title(['Cluster ' num2str(i)]);

    legend('show');

    hold off;

end

sgtitle('Probability of False Alarm vs SNR for ED, AND, OR, and Voting logic across Clusters');

% Plot Probability of Detection vs SNR for ED, AND, OR, and Voting logic

figure;

hold on;

for i = 1:num_clusters

    plot(SNR, Pd_ED(i, :), '-o', 'DisplayName', ['Cluster ' num2str(i) ' ED']);

    plot(SNR, Pd_AND(i, :), '--x', 'DisplayName', ['Cluster ' num2str(i) ' AND']);

    plot(SNR, Pd_OR(i, :), '-.*', 'DisplayName', ['Cluster ' num2str(i) ' OR']);

    plot(SNR, Pd_Voting(i, :), '-s', 'DisplayName', ['Cluster ' num2str(i) ' Voting']);

end

xlabel('SNR (dB)');

ylabel('Probability of Detection');

title('Probability of Detection vs SNR for ED and Collaborative ED (AND, OR, Voting)');

legend('show');

hold off;

% Plot Probability of False Alarm vs SNR for ED, AND, OR, and Voting logic

figure;

hold on;

for i = 1:num_clusters

    plot(SNR, Pf_ED(i, :), '-o', 'DisplayName', ['Cluster ' num2str(i) ' ED']);

    plot(SNR, Pf_AND(i, :), '--x', 'DisplayName', ['Cluster ' num2str(i) ' AND']);

    plot(SNR, Pf_OR(i, :), '-.*', 'DisplayName', ['Cluster ' num2str(i) ' OR']);

    plot(SNR, Pf_Voting(i, :), '-s', 'DisplayName', ['Cluster ' num2str(i) ' Voting']);

end

xlabel('SNR (dB)');

ylabel('Probability of False Alarm');

title('Probability of False Alarm vs SNR for ED and Collaborative ED (AND, OR, Voting)');

legend('show');

hold off;

Click to download MATLAB Code with Simulated Output