物联网学报2025，Vol.9Issue(4)：105-112,8.DOI:10.11959/j.issn.2096-3750.2025.00494
基于深度扩散确定性策略梯度的Wi-Fi网络性能优化
Deep diffusion deterministic policy gradient based performance optimization for Wi-Fi network
摘要
Abstract
The optimization of Wi-Fi network performance typically constitutes a multi-parameter,multi-objective dy-namic optimization problem,which presents significant challenges in mathematical modeling.Deep reinforcement learn-ing(DRL),which obviates the need for complex mathematical formulations,has been widely applied in recent years to optimize Wi-Fi network performance.Meanwhile,generative diffusion models(GDMs)have achieved remarkable prog-ress in modeling complex data distributions across various domains.Therefore,integrating DRL with GDMs can further enhance its capabilities in optimizing Wi-Fi network performance.The typical medium access control(MAC)mechanism in Wi-Fi network is the distributed coordination function(DCF),whose performance significantly degrades as the number of contending terminals increases.A deep diffusion deterministic policy gradient(D3PG)algorithm was proposed,which integrated diffusion models with the deep deterministic policy gradient(DDPG)framework to optimize Wi-Fi network performance.In addition,an access mechanism that jointly adjusted the contention window and the aggregation frame length based on the D3PG algorithm was proposed.Simulations have demonstrated that this mechanism significantly out-performs existing Wi-Fi standards in dense Wi-Fi scenarios,maintaining throughput performance even as the number of users increases sharply.关键词
Wi-Fi网络/生成扩散模型/深度强化学习/性能优化/接入控制Key words
Wi-Fi network/generative diffusion model/deep reinforcement learning/performance optimization/access control分类
信息技术与安全科学引用本文复制引用
刘铁,方旭明,何蓉..基于深度扩散确定性策略梯度的Wi-Fi网络性能优化[J].物联网学报,2025,9(4):105-112,8.基金项目
国家自然科学基金资助项目(No.62071393) （No.62071393）
四川省重点研发计划项目(No.2024YFHZ0093)The National Natural Science Foundation of China(No.62071393),Sichuan Science and Technology Program(No.2024YFHZ0093) （No.2024YFHZ0093）
