Federated Deep Reinforcement Learning-Based Intelligent Channel Access in Dense Wi-Fi Deployments

TL;DR

This paper presents a federated deep reinforcement learning approach to improve channel access in dense Wi-Fi networks, significantly reducing MAC delays and outperforming existing methods in both static and dynamic scenarios.

Contribution

It introduces a novel federated deep reinforcement learning-based mechanism with training pruning and weight aggregation for efficient and effective channel contention management.

Findings

Reduces average MAC delay by 25.24% in static scenarios.

Outperforms A-FRL and DRL by 25.72% and 45.9% in dynamic environments.

Enhances model efficiency and reduces MAC delay through training pruning and weight aggregation.

Abstract

The IEEE 802.11 MAC layer utilizes the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) mechanism for channel contention, but dense Wi-Fi deployments often cause high collision rates. To address this, this paper proposes an intelligent channel contention access mechanism that combines Federated Learning (FL) and Deep Deterministic Policy Gradient (DDPG) algorithms. We introduce a training pruning strategy and a weight aggregation algorithm to enhance model efficiency and reduce MAC delay. Using the NS3-AI framework, simulations show our method reduces average MAC delay by 25.24\% in static scenarios and outperforms A-FRL and DRL by 25.72\% and 45.9\% in dynamic environments, respectively.