Deep Diffusion Deterministic Policy Gradient based Performance Optimization for Wi-Fi Networks

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 decreases in performance with a high number of terminals. In this study, we propose the Deep Diffusion Deterministic Policy Gradient (D3PG) algorithm, which integrates diffusion models with the Deep Deterministic Policy Gradient (DDPG) framework to optimize Wi-Fi network performance. To the best of our knowledge, this is the first work to apply such an integration in Wi-Fi performance optimization. We propose an access mechanism that jointly adjusts the contention window and the aggregation 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 increases sharply.