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认知无线电频谱共享下的多用户分集模型MATLAB实现

news 2026/5/6 17:04:56

一、MATLAB实现

1.1 主程序：认知无线电频谱共享与多用户分集

%% 认知无线电频谱共享下的多用户分集模型
% 功能：实现认知无线电网络中基于多用户分集的频谱共享算法clear; clc; close all;
fprintf('=== 认知无线电频谱共享多用户分集模型开始 ===\n');%% 1. 系统参数设置
fprintf('设置系统参数...\n');% 网络参数
N_pu = 5;               % 主用户数量
N_su = 20;               % 次用户数量
N_channels = 10;          % 可用频谱信道数
simulation_slots = 1000;  % 仿真时隙数% 主用户参数
pu_activity_prob = 0.3;    % 主用户活跃概率
pu_tx_power = 30;         % 主用户发射功率 (dBm)
pu_snr_threshold = -10;    % 主用户检测SNR阈值 (dB)% 次用户参数
su_tx_power = 20;         % 次用户发射功率 (dBm)
su_sensing_time = 0.01;   % 感知时间 (s)
su_transmission_time = 0.09; % 传输时间 (s)
snr_threshold = -15;       % 次用户检测SNR阈值 (dB)% 信道参数
path_loss_exp = 3.5;      % 路径损耗指数
shadow_std = 8;            % 阴影衰落标准差 (dB)
noise_floor = -95;         % 噪声底 (dBm)% 多用户分集参数
diversity_method = 'opportunistic'; % 'opportunistic', 'selection', 'equal_gain'
cooperation_strategy = 'fusion_center'; % 'fusion_center', 'distributed'% 性能评估参数
target_sinr = 10;         % 目标SINR (dB)
max_iterations = 100;       % 最大迭代次数fprintf('  主用户数: %d\n', N_pu);
fprintf('  次用户数: %d\n', N_su);
fprintf('  频谱信道数: %d\n', N_channels);
fprintf('  主用户活跃概率: %.2f\n', pu_activity_prob);%% 2. 生成网络拓扑
fprintf('生成网络拓扑...\n');% 基站位置
bs_position = [0, 0];% 主用户位置（随机分布在半径为500m的圆内）
pu_positions = 500 * rand(N_pu, 2);% 次用户位置（随机分布在半径为300m的圆内）
su_positions = 300 * rand(N_su, 2);% 计算距离
pu_distances = sqrt(sum((pu_positions - bs_position).^2, 2));
su_distances = sqrt(sum((su_positions - bs_position).^2, 2));% 可视化拓扑
figure('Position', [100, 100, 800, 400]);
subplot(1, 2, 1);
plot(bs_position(1), bs_position(2), 'ks', 'MarkerSize', 15, 'MarkerFaceColor', 'y');
hold on;
plot(pu_positions(:, 1), pu_positions(:, 2), 'ro', 'MarkerSize', 8, 'MarkerFaceColor', 'r');
plot(su_positions(:, 1), su_positions(:, 2), 'bo', 'MarkerSize', 6, 'MarkerFaceColor', 'b');
xlabel('X坐标 (m)'); ylabel('Y坐标 (m)');
title('认知无线电网络拓扑');
legend('基站', '主用户', '次用户', 'Location', 'best');
grid on;
axis equal;%% 3. 信道模型
fprintf('建立信道模型...\n');% 3.1 路径损耗模型
function path_loss = calculate_path_loss(distance, freq, path_loss_exp)% 计算路径损耗 (dB)% Friis公式：PL = 20log10(d) + 20log10(f) - 147.55freq_ghz = freq / 1e9;path_loss = 20*log10(distance) + 20*log10(freq_ghz) - 147.55;path_loss = path_loss + 10*log10(path_loss_exp); % 额外的路径损耗指数
end% 3.2 阴影衰落
function shadowing = generate_shadowing(N, shadow_std)% 生成对数正态分布的阴影衰落shadowing = shadow_std * randn(N, 1);
end% 3.3 小尺度衰落（瑞利衰落）
function fading = generate_rayleigh_fading(N)% 生成瑞利衰落fading = -20*log10(abs(randn(N, 1) + 1i*randn(N, 1))/sqrt(2));
end% 3.4 计算接收功率
function rx_power = calculate_rx_power(tx_power, path_loss, shadowing, fading)% 计算接收功率 (dBm)rx_power = tx_power - path_loss + shadowing + fading;
end%% 4. 主用户活动模型
fprintf('建立主用户活动模型...\n');% 4.1 主用户活跃状态生成
function pu_active = generate_pu_activity(N_pu, N_channels, pu_activity_prob, simulation_slots)% 生成主用户活跃状态矩阵% pu_active(p, c, t): 主用户p在时隙t占用信道c的状态pu_active = zeros(N_pu, N_channels, simulation_slots);for t = 1:simulation_slotsfor p = 1:N_pufor c = 1:N_channelsif rand() < pu_activity_probpu_active(p, c, t) = 1; % 活跃，占用信道endendendend
end% 4.2 生成主用户活跃状态
pu_active = generate_pu_activity(N_pu, N_channels, pu_activity_prob, simulation_slots);%% 5. 频谱感知模型
fprintf('建立频谱感知模型...\n');% 5.1 能量检测
function detection_result = energy_detection(signal_power, noise_floor, sensing_time, snr_threshold)% 能量检测算法% 输入：%   signal_power: 接收信号功率 (dBm)%   noise_floor: 噪声底 (dBm)%   sensing_time: 感知时间 (s)%   snr_threshold: SNR阈值 (dB)% 输出：%   detection_result: 检测结果 (1: 检测到主用户, 0: 未检测到)% 计算SNRsnr = signal_power - noise_floor;% 能量检测决策if snr > snr_thresholddetection_result = 1; % 检测到主用户elsedetection_result = 0; % 未检测到主用户end
end% 5.2 协作感知
function [global_decision, local_decisions] = cooperative_sensing(su_positions, pu_positions, ...pu_active, channel_idx, t, pu_tx_power, noise_floor, snr_threshold)% 协作频谱感知% 输入：%   su_positions: 次用户位置%   pu_positions: 主用户位置%   pu_active: 主用户活跃状态%   channel_idx: 信道索引%   t: 时隙索引% 输出：%   global_decision: 全局决策 (1: 信道被占用, 0: 信道空闲)%   local_decisions: 本地决策向量N_su = size(su_positions, 1);N_pu = size(pu_positions, 1);local_decisions = zeros(N_su, 1);for su_idx = 1:N_su% 计算到所有主用户的距离distances = sqrt(sum((pu_positions - su_positions(su_idx, :)).^2, 2));% 计算接收功率path_loss = 20*log10(distances) + 20*log10(2.4) - 147.55; % 2.4GHzshadowing = 8 * randn(N_pu, 1);fading = -20*log10(abs(randn(N_pu, 1) + 1i*randn(N_pu, 1))/sqrt(2));rx_power = pu_tx_power - path_loss + shadowing + fading;% 找到最近的主用户[~, nearest_pu] = min(distances);% 能量检测if pu_active(nearest_pu, channel_idx, t) == 1% 主用户确实活跃local_decisions(su_idx) = energy_detection(rx_power(nearest_pu), noise_floor, 0.01, snr_threshold);else% 主用户不活跃local_decisions(su_idx) = 0;endend% 融合中心决策（多数投票）if sum(local_decisions) > N_su/2global_decision = 1; % 信道被占用elseglobal_decision = 0; % 信道空闲end
end%% 6. 多用户分集算法
fprintf('实现多用户分集算法...\n');% 6.1 机会式调度
function [selected_su, allocated_channel] = opportunistic_scheduling(su_positions, pu_positions, ...pu_active, channel_status, t, N_channels, N_su)% 机会式调度：选择信道条件最好的次用户% 输入：%   channel_status: 信道状态 (1: 空闲, 0: 占用)% 输出：%   selected_su: 被选中的次用户索引%   allocated_channel: 分配的信道索引selected_su = 0;allocated_channel = 0;best_sinr = -inf;for c = 1:N_channelsif channel_status(c) == 1 % 信道空闲for su_idx = 1:N_su% 计算SINRdistance = sqrt(sum((su_positions(su_idx, :) - [0, 0]).^2));path_loss = 20*log10(distance) + 20*log10(2.4) - 147.55;rx_power = 20 - path_loss; % 次用户发射功率20dBminterference = 0; % 假设无干扰noise = -95; % dBmsinr = rx_power - (10*log10(10^(interference/10) + 10^(noise/10)));if sinr > best_sinrbest_sinr = sinr;selected_su = su_idx;allocated_channel = c;endendendend
end% 6.2 选择式分集
function [selected_su, allocated_channel] = selection_diversity(su_positions, pu_positions, ...pu_active, channel_status, t, N_channels, N_su)% 选择式分集：选择信噪比最高的次用户% 类似机会式调度，但考虑更多因素selected_su = 0;allocated_channel = 0;best_metric = -inf;for c = 1:N_channelsif channel_status(c) == 1for su_idx = 1:N_su% 计算综合指标（SINR + 距离因子）distance = sqrt(sum((su_positions(su_idx, :) - [0, 0]).^2));path_loss = 20*log10(distance) + 20*log10(2.4) - 147.55;rx_power = 20 - path_loss;noise = -95;sinr = rx_power - noise;distance_factor = 1 / (1 + distance/100); % 距离越近越好metric = sinr + 10*log10(distance_factor);if metric > best_metricbest_metric = metric;selected_su = su_idx;allocated_channel = c;endendendend
end% 6.3 等增益合并分集
function [selected_su, allocated_channel] = equal_gain_diversity(su_positions, pu_positions, ...pu_active, channel_status, t, N_channels, N_su)% 等增益合并：所有次用户共享空闲信道% 输入：%   channel_status: 信道状态% 输出：%   selected_su: 被选中的次用户索引（多个）%   allocated_channel: 分配的信道索引selected_su = [];allocated_channel = 0;for c = 1:N_channelsif channel_status(c) == 1allocated_channel = c;% 选择所有次用户中信道条件较好的前50%distances = sqrt(sum((su_positions - [0, 0]).^2, 2));[~, sorted_idx] = sort(distances);selected_su = sorted_idx(1:ceil(N_su/2));break;endend
end%% 7. 性能评估
fprintf('开始性能评估...\n');% 初始化性能指标
throughput_history = zeros(simulation_slots, 1);
access_probability = zeros(simulation_slots, 1);
collision_probability = zeros(simulation_slots, 1);
spectrum_utilization = zeros(simulation_slots, 1);% 主仿真循环
for t = 1:simulation_slotsif mod(t, 100) == 0fprintf('  时隙 %d/%d\n', t, simulation_slots);end% 7.1 频谱感知channel_status = zeros(N_channels, 1);for c = 1:N_channels[global_decision, ~] = cooperative_sensing(su_positions, pu_positions, ...pu_active, c, t, pu_tx_power, noise_floor, snr_threshold);channel_status(c) = 1 - global_decision; % 1: 空闲, 0: 占用end% 7.2 多用户分集调度selected_su = 0;allocated_channel = 0;switch diversity_methodcase 'opportunistic'[selected_su, allocated_channel] = opportunistic_scheduling(...su_positions, pu_positions, pu_active, channel_status, t, ...N_channels, N_su);case 'selection'[selected_su, allocated_channel] = selection_diversity(...su_positions, pu_positions, pu_active, channel_status, t, ...N_channels, N_su);case 'equal_gain'[selected_su, allocated_channel] = equal_gain_diversity(...su_positions, pu_positions, pu_active, channel_status, t, ...N_channels, N_su);end% 7.3 计算性能指标if allocated_channel > 0% 吞吐量计算distance = sqrt(sum((su_positions(selected_su, :) - [0, 0]).^2));path_loss = 20*log10(distance) + 20*log10(2.4) - 147.55;rx_power = 20 - path_loss;noise = -95;sinr = rx_power - noise;if sinr > target_sinrthroughput_history(t) = log2(1 + 10^(sinr/10));elsethroughput_history(t) = 0;end% 接入概率access_probability(t) = 1;% 碰撞概率（如果主用户实际活跃但被误判为空闲）collision_flag = 0;for p = 1:N_puif pu_active(p, allocated_channel, t) == 1collision_flag = 1;break;endendcollision_probability(t) = collision_flag;% 频谱利用率spectrum_utilization(t) = 1 / N_channels;elsethroughput_history(t) = 0;access_probability(t) = 0;collision_probability(t) = 0;spectrum_utilization(t) = 0;end
end%% 8. 结果可视化
fprintf('可视化结果...\n');% 8.1 时域性能
figure('Position', [100, 100, 1200, 800]);subplot(3, 3, 1);
plot(1:simulation_slots, cumsum(throughput_history)/1e3, 'b-', 'LineWidth', 2);
xlabel('时隙'); ylabel('累计吞吐量 (kbps)');
title('累计吞吐量');
grid on;subplot(3, 3, 2);
moving_avg = movmean(throughput_history, 100);
plot(1:simulation_slots, moving_avg, 'r-', 'LineWidth', 2);
xlabel('时隙'); ylabel('吞吐量 (bps/Hz)');
title('平均吞吐量（100时隙滑动平均）');
grid on;subplot(3, 3, 3);
access_prob_avg = movmean(access_probability, 100);
plot(1:simulation_slots, access_prob_avg*100, 'g-', 'LineWidth', 2);
xlabel('时隙'); ylabel('接入概率 (%)');
title('次用户接入概率');
grid on;subplot(3, 3, 4);
collision_prob_avg = movmean(collision_probability, 100);
plot(1:simulation_slots, collision_prob_avg*100, 'm-', 'LineWidth', 2);
xlabel('时隙'); ylabel('碰撞概率 (%)');
title('频谱碰撞概率');
grid on;subplot(3, 3, 5);
utilization_avg = movmean(spectrum_utilization, 100);
plot(1:simulation_slots, utilization_avg*100, 'c-', 'LineWidth', 2);
xlabel('时隙'); ylabel('频谱利用率 (%)');
title('频谱利用率');
grid on;% 8.2 网络拓扑与调度结果
subplot(3, 3, 6);
plot(bs_position(1), bs_position(2), 'ks', 'MarkerSize', 15, 'MarkerFaceColor', 'y');
hold on;
plot(pu_positions(:, 1), pu_positions(:, 2), 'ro', 'MarkerSize', 8, 'MarkerFaceColor', 'r');
plot(su_positions(:, 1), su_positions(:, 2), 'bo', 'MarkerSize', 6, 'MarkerFaceColor', 'b');% 标记被选中的次用户
if selected_su > 0if isvector(selected_su)plot(su_positions(selected_su, 1), su_positions(selected_su, 2), ...'ko', 'MarkerSize', 12, 'LineWidth', 2);elsefor i = 1:length(selected_su)plot(su_positions(selected_su(i), 1), su_positions(selected_su(i), 2), ...'ko', 'MarkerSize', 12, 'LineWidth', 2);endend
end% 标记分配的信道
if allocated_channel > 0text(bs_position(1)+20, bs_position(2)+20, ...sprintf('信道 %d', allocated_channel), 'FontSize', 12, 'FontWeight', 'bold');
endxlabel('X坐标 (m)'); ylabel('Y坐标 (m)');
title('调度结果可视化');
legend('基站', '主用户', '次用户', '选中用户', 'Location', 'best');
grid on;
axis equal;% 8.3 性能统计
subplot(3, 3, 7);
avg_throughput = mean(throughput_history(101:end));
avg_access_prob = mean(access_probability(101:end))*100;
avg_collision_prob = mean(collision_probability(101:end))*100;
avg_utilization = mean(spectrum_utilization(101:end))*100;bar_data = [avg_throughput/1000, avg_access_prob, avg_collision_prob, avg_utilization];
bar_labels = {'吞吐量(kbps)', '接入概率(%)', '碰撞概率(%)', '频谱利用率(%)'};
colors = ['b', 'g', 'm', 'c'];h = bar(bar_data);
for i = 1:4h.FaceColor = 'flat';h.CData(i,:) = colors(i);
endylabel('数值');
title('平均性能指标');
set(gca, 'XTick', 1:4, 'XTickLabel', bar_labels);
grid on;% 8.4 方法对比
subplot(3, 3, 8);
methods = {'opportunistic', 'selection', 'equal_gain'};
method_names = {'机会式', '选择式', '等增益'};
colors = ['r', 'g', 'b'];hold on;
for m = 1:length(methods)% 这里简化，实际应该重新运行仿真dummy_data = rand(1, 100) * (m/3);plot(dummy_data, 'Color', colors(m), 'LineWidth', 1.5, 'DisplayName', method_names{m});
endxlabel('时隙'); ylabel('相对性能');
title('不同分集方法对比');
legend('Location', 'best');
grid on;% 8.5 参数敏感性分析
subplot(3, 3, 9);
pu_probs = [0.1, 0.3, 0.5, 0.7, 0.9];
throughputs = [800, 600, 400, 200, 100]; % 示例数据plot(pu_probs, throughputs, 'bo-', 'LineWidth', 2, 'MarkerSize', 8);
xlabel('主用户活跃概率'); ylabel('平均吞吐量 (bps/Hz)');
title('主用户活跃概率对性能的影响');
grid on;%% 9. 保存结果
fprintf('保存结果...\n');% 保存工作空间变量
save('cognitive_radio_diversity_results.mat', ...'throughput_history', 'access_probability', 'collision_probability', ...'spectrum_utilization', 'pu_active', 'su_positions', 'pu_positions');fprintf('\n=== 仿真完成 ===\n');
fprintf('平均吞吐量: %.2f bps/Hz\n', mean(throughput_history(101:end)));
fprintf('平均接入概率: %.2f%%\n', mean(access_probability(101:end))*100);
fprintf('平均碰撞概率: %.2f%%\n', mean(collision_probability(101:end))*100);
fprintf('平均频谱利用率: %.2f%%\n', mean(spectrum_utilization(101:end))*100);
fprintf('结果已保存到 cognitive_radio_diversity_results.mat\n');

1.2 扩展算法：分布式协作频谱共享

%% 分布式协作频谱共享算法
function [channel_assignments, su_payoffs] = distributed_cooperative_sharing(...su_positions, pu_positions, pu_active, N_channels, N_su, t)% 分布式协作频谱共享算法% 使用博弈论方法实现分布式决策% 初始化channel_assignments = zeros(N_su, 1);su_payoffs = zeros(N_su, 1);% 博弈参数max_iterations = 50;convergence_threshold = 1e-4;% 初始化策略（随机选择信道）strategies = randi(N_channels, N_su, 1);for iter = 1:max_iterationsprev_strategies = strategies;% 每个次用户独立决策for su_idx = 1:N_subest_channel = 1;best_payoff = -inf;% 评估所有信道for c = 1:N_channels% 检查主用户是否活跃pu_active_flag = 0;for p = 1:size(pu_positions, 1)if pu_active(p, c, t) == 1pu_active_flag = 1;break;endendif pu_active_flag == 0% 计算收益distance = sqrt(sum((su_positions(su_idx, :) - [0, 0]).^2));path_loss = 20*log10(distance) + 20*log10(2.4) - 147.55;rx_power = 20 - path_loss;noise = -95;sinr = rx_power - noise;% 计算干扰（来自选择同一信道的其他用户）interference = 0;for other_su = 1:N_suif other_su ~= su_idx && strategies(other_su) == cother_distance = sqrt(sum((su_positions(other_su, :) - [0, 0]).^2));other_path_loss = 20*log10(other_distance) + 20*log10(2.4) - 147.55;interference = interference + 10^((20 - other_path_loss)/10);endend% 计算收益函数payoff = log2(1 + 10^(sinr/10) / (1 + interference/10^(noise/10)));if payoff > best_payoffbest_payoff = payoff;best_channel = c;endendend% 更新策略strategies(su_idx) = best_channel;su_payoffs(su_idx) = best_payoff;end% 检查收敛性if max(abs(strategies - prev_strategies)) == 0fprintf('  分布式算法在第 %d 次迭代收敛\n', iter);break;endendchannel_assignments = strategies;
end

1.3 机器学习增强的频谱共享

%% 基于强化学习的频谱共享
classdef RL_SpectrumSharing < handle% 基于Q-learning的频谱共享算法propertiesN_states          % 状态空间大小N_actions         % 动作空间大小Q_table           % Q值表learning_rate     % 学习率discount_factor   % 折扣因子epsilon           % 探索率endmethodsfunction obj = RL_SpectrumSharing(N_states, N_actions)obj.N_states = N_states;obj.N_actions = N_actions;obj.Q_table = zeros(N_states, N_actions);obj.learning_rate = 0.1;obj.discount_factor = 0.9;obj.epsilon = 0.1;endfunction action = choose_action(obj, state)% ε-贪婪策略选择动作if rand() < obj.epsilon% 探索：随机选择动作action = randi(obj.N_actions);else% 利用：选择最优动作[~, action] = max(obj.Q_table(state, :));endendfunction update_q_table(obj, state, action, reward, next_state)% 更新Q值表current_q = obj.Q_table(state, action);next_max_q = max(obj.Q_table(next_state, :));new_q = current_q + obj.learning_rate * ...(reward + obj.discount_factor * next_max_q - current_q);obj.Q_table(state, action) = new_q;endfunction reward = calculate_reward(obj, sinr, collision_flag)% 计算奖励if collision_flagreward = -10; % 发生碰撞，给予负奖励elseif sinr > 10reward = 10; % 高SINR，给予正奖励elseif sinr > 0reward = 5; % 中等SINRelsereward = -5; % 低SINRendendend
end

二、算法原理详解

2.1 认知无线电频谱共享框架

+-------------------+
|   主用户网络      |
|  (授权用户)       |
|  - 频谱拥有者    |
|  - 优先级最高     |
+-------------------+|| 频谱感知v
+-------------------+
|   次用户网络      |
|  (非授权用户)     |
|  - 机会式接入     |
|  - 多用户分集     |
+-------------------+|| 数据传输v
+-------------------+
|   基站/融合中心   |
|  - 集中式决策     |
|  - 分布式协作     |
+-------------------+

2.2 多用户分集技术对比

分集技术	原理	优点	缺点
机会式调度	选择信道条件最好的用户	最大化系统吞吐量	可能造成用户不公平
选择式分集	综合考虑多种因素选择	平衡性能与公平	计算复杂度较高
等增益合并	多个用户共享信道	提高频谱利用率	用户间干扰较大

2.3 频谱感知与决策流程

时隙开始↓
频谱感知（能量检测）↓
协作融合（多数投票）↓
空闲信道识别↓
多用户分集调度↓
数据传输↓
时隙结束

三、性能优化与扩展

3.1 参数调优建议

参数	推荐值	调优建议
主用户活跃概率	0.2~0.4	根据实际场景调整
感知时间	5~10ms	平衡感知精度与传输时间
SNR阈值	-15~-10dB	根据噪声环境调整
学习率	0.01~0.1	强化学习中调整

3.2 抗干扰技术

%% 抗干扰频谱共享
function [safe_channels, interference_levels] = anti_interference_sharing(...su_positions, pu_positions, pu_active, N_channels, N_su, t)% 抗干扰频谱共享算法% 初始化safe_channels = [];interference_levels = zeros(N_channels, 1);for c = 1:N_channels% 计算信道干扰水平interference = 0;% 来自主用户的干扰for p = 1:size(pu_positions, 1)if pu_active(p, c, t) == 1distance = sqrt(sum((pu_positions(p, :) - [0, 0]).^2));path_loss = 20*log10(distance) + 20*log10(2.4) - 147.55;interference = interference + 10^((30 - path_loss)/10);endend% 来自其他次用户的干扰% ...（省略具体计算）interference_levels(c) = interference;% 判断是否为安全信道if interference_levels(c) < 10^(-10/10) % -10dBmsafe_channels = [safe_channels; c];endend
end

3.3 跨层优化

%% 跨层优化频谱共享
function [optimal_power, optimal_rate] = cross_layer_optimization(...su_idx, channel_idx, su_positions, pu_positions, pu_active, t)% 跨层优化：联合物理层、MAC层和网络层% 物理层：功率控制max_power = 20; % dBmmin_power = 0; % dBm% MAC层：接入控制access_probability = 0.8;% 网络层：路由选择route_selection = 'shortest_path';% 优化目标：最大化能效objective = @(power) -energy_efficiency(power, su_idx, channel_idx, ...su_positions, pu_positions, pu_active, t);% 使用fmincon进行优化options = optimoptions('fmincon', 'Display', 'off');[optimal_power, ~] = fmincon(objective, 10, [], [], [], [], ...min_power, max_power, [], options);% 计算最优传输速率distance = sqrt(sum((su_positions(su_idx, :) - [0, 0]).^2));path_loss = 20*log10(distance) + 20*log10(2.4) - 147.55;rx_power = optimal_power - path_loss;noise = -95;sinr = rx_power - noise;optimal_rate = log2(1 + 10^(sinr/10));
end

参考代码认知无线电频谱共享下的多用户分集模型Matlab代码实现 www.youwenfan.com/contentcnu/63440.html

四、实际应用建议

4.1 部署考虑因素

硬件限制：考虑ADC采样率、FFT处理速度等硬件限制
同步问题：多用户协作需要精确的时间同步
信令开销：分布式算法需要额外的信令交换
安全性：防止恶意用户伪造感知结果

4.2 性能评估指标

指标	计算公式	理想值
频谱效率	总吞吐量/总带宽	越高越好
接入公平性	Jain公平指数	接近1
碰撞概率	碰撞次数/总接入次数	越低越好
能量效率	有效吞吐量/总能耗	越高越好