# Coordinated Multi-Armed Bandits for Improved Spatial Reuse in Wi-Fi

arXiv: https://arxiv.org/abs/2412.03076 · HTML: https://mlsanity.com/paper/2412.03076/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 17 references that are in the map.
- First posted on arXiv 4 Dec 2024.

## Builds on (most cited first, 9 of 9)

- [Potential and Pitfalls of Multi-Armed Bandits for Decentralized Spatial Reuse in WLANs](https://mlsanity.com/paper/1805.11083/) (2018)
- [Komondor: a Wireless Network Simulator for Next-Generation High-Density WLANs](https://mlsanity.com/paper/1811.12397/) (2018)
- [Collaborative Spatial Reuse in Wireless Networks via Selfish Multi-Armed Bandits](https://mlsanity.com/paper/1710.11403/) (2017)
- [Spatial Reuse in IEEE 802.11ax WLANs](https://mlsanity.com/paper/1907.04141/) (2019)
- [Throughput Analysis of IEEE 802.11bn Coordinated Spatial Reuse](https://mlsanity.com/paper/2309.09169/) (2023)
- [TXOP sharing with Coordinated Spatial Reuse in Multi-AP Cooperative IEEE 802.11be WLANs](https://mlsanity.com/paper/2112.00515/) (2021)
- [Multi-AP Coordinated Spatial Reuse for Wi-Fi 8: Group Creation and Scheduling](https://mlsanity.com/paper/2305.04846/) (2023)
- [A Survey on Multi-AP Coordination Approaches over Emerging WLANs: Future Directions and Open Challenges](https://mlsanity.com/paper/2306.04164/) (2023)
- [Spatial Reuse in IEEE 802.11bn Coordinated Multi-AP WLANs: A Throughput Analysis](https://mlsanity.com/paper/2407.16390/) (2024)

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

- [Decentralized Spatial Reuse Optimization in Wi-Fi: An Internal Regret Minimization Approach](https://mlsanity.com/paper/2602.08456/) (2026)

## Abstract (arXiv)

Multi-Access Point Coordination (MAPC) and Artificial Intelligence and Machine Learning (AI/ML) are expected to be key features in future Wi-Fi, such as the forthcoming IEEE 802.11bn (Wi-Fi~8) and beyond. In this paper, we explore a coordinated solution based on online learning to drive the optimization of Spatial Reuse (SR), a method that allows multiple devices to perform simultaneous transmissions by controlling interference through Packet Detect (PD) adjustment and transmit power control. In particular, we focus on a Multi-Agent Multi-Armed Bandit (MA-MAB) setting, where multiple decision-making agents concurrently configure SR parameters from coexisting networks by leveraging the MAPC framework, and study various algorithms and reward-sharing mechanisms. We evaluate different MA-MAB implementations using Komondor, a well-adopted Wi-Fi simulator, and demonstrate that AI-native SR enabled by coordinated MABs can improve the network performance over current Wi-Fi operation: mean throughput increases by 15%, fairness is improved by increasing the minimum throughput across the network by 210%, while the maximum access delay is kept below 3 ms.