# Contention Window Optimization in IEEE 802.11ax Networks with Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2003.01492 · HTML: https://mlsanity.com/paper/2003.01492/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2020.

## Built on by (most cited first, 6 of 6)

- [Enhancement of High-definition Map Update Service Through Coverage-aware and Reinforcement Learning](https://mlsanity.com/paper/2402.14582/) (2024)
- [ML Framework for Wireless MAC Protocol Design](https://mlsanity.com/paper/2402.07208/) (2024)
- [Learning-based Autonomous Channel Access in the Presence of Hidden Terminals](https://mlsanity.com/paper/2207.03605/) (2022)
- [Multi-agent Assessment with QoS Enhancement for HD Map Updates in a Vehicular Network](https://mlsanity.com/paper/2407.21460/) (2024)
- [To Theoretically Understand Transformer-Based In-Context Learning for Optimizing CSMA](https://mlsanity.com/paper/2508.09146/) (2025)
- [AI-Enhanced Distributed Channel Access for Collision Avoidance in Future Wi-Fi 8](https://mlsanity.com/paper/2509.23154/) (2025)

## Abstract (arXiv)

The proper setting of contention window (CW) values has a significant impact on the efficiency of Wi-Fi networks. Unfortunately, the standard method used by 802.11 networks is not scalable enough to maintain stable throughput for an increasing number of stations, yet it remains the default method of channel access for 802.11ax single-user transmissions. Therefore, we propose a new method of CW control, which leverages deep reinforcement learning (DRL) principles to learn the correct settings under different network conditions. Our method, called centralized contention window optimization with DRL (CCOD), supports two trainable control algorithms: deep Q-network (DQN) and deep deterministic policy gradient (DDPG). We demonstrate through simulations that it offers efficiency close to optimal (even in dynamic topologies) while keeping computational cost low.