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

arXiv: https://arxiv.org/abs/2409.01004 · HTML: https://mlsanity.com/paper/2409.01004/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2024.

## Built on by (most cited first, 1 of 1)

- [Hidden in Rounds: Predicting the Time Cost of 802.11 Contention in Federated Learning](https://mlsanity.com/paper/2609.12903/) (2026)

## Abstract (arXiv)

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.