  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.
