当前位置：首页 > news >正文

MATLAB的宽频带频谱感知算法仿真

news 2025/9/23 9:48:40

一、架构

%% 主程序框架
clear; clc; close all;%% 参数设置
fs = 10e6;        % 采样频率
B = 10e6;         % 带宽
Q = 64;           % 子带数
SNR = -10:2:20;   % 信噪比范围
num_snap = 1000;  % 快拍数%% 信号生成
[P, S] = generate_primary_user(B, Q, fs); % 主用户信号生成
noise = wgn(1,num_snap,SNR(1),'linear');  % 高斯噪声%% 算法仿真循环
for i = 1:length(SNR)% 添加噪声y = P + noise*10^(-SNR(i)/10);% 时域特征提取time_feat = extract_time_features(y);% 频域感知算法[occupied, Pd, Pfa] = spectrum_sensing(y, Q, fs);% 性能记录results(i).Pd = Pd;results(i).Pfa = Pfa;
end%% 结果可视化
plot_results(SNR, results);

二、核心算法实现

2.1 基于FDC准则的感知算法

function [occupied, Pd, Pfa] = fdc_spectrum_sensing(y, Q, fs)% 参数设置t = 1.9; % 灵活系数R = cov(y); % 协方差矩阵% 特征值分解[V,D] = eig(R);eigvals = diag(D);[~,idx] = sort(eigvals,'descend');eigvals = eigvals(idx);% FDC改进GMDL准则threshold = 10*log10(t*mean(eigvals(2:end)));occupied = find(eigvals(1:end-1) > threshold);% 性能计算Pd = sum(occupied == true_occupied)/num_snap;Pfa = sum(occupied ~= true_occupied)/num_snap;
end

2.2 压缩感知OMP算法

function [occupied] = omp_spectrum_sensing(y, M, N)% 参数设置sparsity = 5; % 稀疏度A = sensing_matrix(M,N); % 测量矩阵% 迭代重构residual = y;idx_set = [];for iter = 1:Mproj = abs(A' * residual);[~,max_idx] = max(proj);idx_set = [idx_set, max_idx];if length(idx_set) >= sparsitybreak;endA = A(:,idx_set);residual = y - A*(A'*y);endoccupied = idx_set(1:sparsity);
end

三、关键性能

3.1 ROC曲线绘制

function plot_roc(SNR, results)figure;hold on;for i = 1:length(SNR)plot(results(i).Pfa, results(i).Pd, 'DisplayName', ['SNR=' num2str(SNR(i)) 'dB']);endxlabel('虚警概率(Pfa)');ylabel('检测概率(Pd)');title('ROC曲线对比');legend show;grid on;
end

3.2 计算复杂度分析

function complexity = compute_complexity(algorithm, params)switch algorithmcase 'FDC'complexity = 0.5*params.Q^3 + 0.2*params.num_snap; % 理论复杂度case 'OMP'complexity = 2*params.sparsity*params.M; % 迭代复杂度case 'Energy'complexity = 0.1*params.num_snap; % 能量检测复杂度end
end