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) - 147.55
freq_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_slots
for p = 1:N_pu
for c = 1:N_channels
if rand() < pu_activity_prob
pu_active(p, c, t) = 1; % 活跃，占用信道
end
end
end
end
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SNR
snr = signal_power - noise_floor;
% 能量检测决策
if snr > snr_threshold
detection_result = 1; % 检测到主用户
else
detection_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; % 2.4GHz
shadowing = 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;
end
end
% 融合中心决策（多数投票）
if sum(local_decisions) > N_su/2
global_decision = 1; % 信道被占用
else
global_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_channels
if channel_status(c) == 1 % 信道空闲
for su_idx = 1:N_su
% 计算SINR
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; % 次用户发射功率20dBm
interference = 0; % 假设无干扰
noise = -95; % dBm
sinr = rx_power - (10*log10(10^(interference/10) + 10^(noise/10)));
if sinr > best_sinr
best_sinr = sinr;
selected_su = su_idx;
allocated_channel = c;
end
end
end
end
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_channels
if channel_status(c) == 1
for 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_metric
best_metric = metric;
selected_su = su_idx;
allocated_channel = c;
end
end
end
end
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_channels
if channel_status(c) == 1
allocated_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;
end
end
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_slots
if mod(t, 100) == 0
fprintf(' 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_method
case '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_sinr
throughput_history(t) = log2(1 + 10^(sinr/10));
else
throughput_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_pu
if pu_active(p, allocated_channel, t) == 1
collision_flag = 1;
break;
end
end
collision_probability(t) = collision_flag;
% 频谱利用率
spectrum_utilization(t) = 1 / N_channels;
else
throughput_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 > 0
if isvector(selected_su)
plot(su_positions(selected_su, 1), su_positions(selected_su, 2), ...
'ko', 'MarkerSize', 12, 'LineWidth', 2);
else
for i = 1:length(selected_su)
plot(su_positions(selected_su(i), 1), su_positions(selected_su(i), 2), ...
'ko', 'MarkerSize', 12, 'LineWidth', 2);
end
end
end
% 标记分配的信道
if allocated_channel > 0
text(bs_position(1)+20, bs_position(2)+20, ...
sprintf('信道 %d', allocated_channel), 'FontSize', 12, 'FontWeight', 'bold');
end
xlabel('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 = 1:4
h.FaceColor = 'flat';
h.CData(i,:) = colors(i);
end
ylabel('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});
end
xlabel('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_iterations
prev_strategies = strategies;
% 每个次用户独立决策
for su_idx = 1:N_su
best_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) == 1
pu_active_flag = 1;
break;
end
end
if 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_su
if other_su ~= su_idx && strategies(other_su) == c
other_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);
end
end
% 计算收益函数
payoff = log2(1 + 10^(sinr/10) / (1 + interference/10^(noise/10)));
if payoff > best_payoff
best_payoff = payoff;
best_channel = c;
end
end
end
% 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)) == 0
fprintf(' 分布式算法在第 %d 次迭代收敛\n', iter);
break;
end
end
channel_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
properties
N_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
end
methods
function 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;
end
function 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, :));
end
end
function 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;
end
function reward = calculate_reward(obj, sinr, collision_flag)
% 计算奖励
if collision_flag
reward = -10; % 发生碰撞，给予负奖励
elseif sinr > 10
reward = 10; % 高SINR，给予正奖励
elseif sinr > 0
reward = 5; % 中等SINR
else
reward = -5; % 低SINR
end
end
end
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) == 1
distance = 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);
end
end
% 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) % -10dBm
safe_channels = [safe_channels; c];
end
end
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; % dBm
min_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/contentali/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 |
4.3 标准化建议
- IEEE 802.22：无线区域网（WRAN）标准
- IEEE 802.11af：电视空白频段WiFi
- IEEE 802.15.4m：低速率认知无线电
- 3GPP LTE-U/LAA：非授权频谱LTE
五、总结
本MATLAB实现提供了认知无线电频谱共享下多用户分集模型的完整解决方案：
- 完整建模：涵盖主用户、次用户、基站和信道模型
- 多种分集技术：机会式、选择式和等增益合并
- 协作感知：基于能量检测的协作频谱感知
- 性能评估：全面的性能指标计算和可视化
- 扩展算法：分布式博弈和强化学习增强
该模型可直接用于：
- 认知无线电网络研究
- 动态频谱接入算法开发
- 5G/6G非授权频谱技术验证
- 物联网频谱共享方案评估
