Generative Diffusion Models (GDMs), have made significant strides in modeling complex data distributions across diverse domains. Meanwhile, Deep Reinforcement Learning (DRL) has demonstrated substantial improvements in optimizing Wi-Fi network performance. Wi-Fi optimization problems are highly challenging to model mathematically, and DRL methods can bypass complex mathematical modeling, while GDMs excel in handling complex data modeling. Therefore, combining DRL with GDMs can mutually enhance their capabilities. The current MAC layer access mechanism in Wi-Fi networks is the Distributed Coordination Function (DCF), which dramatically declines in performance with a high number of terminals. In this paper, we apply diffusion models to deep deterministic policy gradient, namely the Deep Diffusion Deterministic Policy (D3PG) algorithm to optimize the Wi-Fi performance. Although such integrations have been explored previously, we are the first to apply it to Wi-Fi network performance optimization. We propose an access mechanism that jointly adjusts the contention window and frame length based on the D3PG algorithm. Through simulations, we have demonstrated that this mechanism significantly outperforms existing Wi-Fi standards in dense Wi-Fi scenarios, maintaining performance even as the number of users sharply increases.

翻译：生成扩散模型（GDM）在建模跨领域复杂数据分布方面取得了显著进展。与此同时，深度强化学习（DRL）在优化Wi-Fi网络性能方面展现出显著改进。Wi-Fi优化问题在数学建模上极具挑战性，而DRL方法可以绕过复杂的数学建模，GDM则擅长处理复杂数据建模。因此，将DRL与GDM结合能够相互增强各自能力。当前Wi-Fi网络的MAC层接入机制是分布式协调功能（DCF），在高终端数量下性能会急剧下降。本文我们将扩散模型应用于深度确定性策略梯度，即深度扩散确定性策略（D3PG）算法来优化Wi-Fi性能。尽管此类集成此前已有探索，但我们是首个将其应用于Wi-Fi网络性能优化的研究。我们提出了一种基于D3PG算法联合调整竞争窗口和帧长度的接入机制。仿真结果表明，在密集Wi-Fi场景下该机制显著优于现有Wi-Fi标准，即使终端数量急剧增加仍能保持性能稳定。