# Potential and Pitfalls of Multi-Armed Bandits for Decentralized Spatial Reuse in WLANs

arXiv: https://arxiv.org/abs/1805.11083 · HTML: https://mlsanity.com/paper/1805.11083/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2018.

## Built on by (most cited first, 7 of 7)

- [INSPIRE: Distributed Bayesian Optimization for ImproviNg SPatIal REuse in Dense WLANs](https://mlsanity.com/paper/2204.10184/) (2022)
- [Federated Spatial Reuse Optimization in Next-Generation Decentralized IEEE 802.11 WLANs](https://mlsanity.com/paper/2203.10472/) (2022)
- [Machine Learning for Performance Prediction of Channel Bonding in Next-Generation IEEE 802.11 WLANs](https://mlsanity.com/paper/2105.14219/) (2021)
- [A Federated Reinforcement Learning Framework for Link Activation in Multi-link Wi-Fi Networks](https://mlsanity.com/paper/2304.14720/) (2023)
- [Machine Learning & Wi-Fi: Unveiling the Path Towards AI/ML-Native IEEE 802.11 Networks](https://mlsanity.com/paper/2405.11504/) (2024)
- [Coordinated Multi-Armed Bandits for Improved Spatial Reuse in Wi-Fi](https://mlsanity.com/paper/2412.03076/) (2024)
- [Decentralized Spatial Reuse Optimization in Wi-Fi: An Internal Regret Minimization Approach](https://mlsanity.com/paper/2602.08456/) (2026)

## Abstract (arXiv)

Spatial Reuse (SR) has recently gained attention to maximize the performance of IEEE 802.11 Wireless Local Area Networks (WLANs). Decentralized mechanisms are expected to be key in the development of SR solutions for next-generation WLANs, since many deployments are characterized by being uncoordinated by nature. However, the potential of decentralized mechanisms is limited by the significant lack of knowledge with respect to the overall wireless environment. To shed some light on this subject, we show the main considerations and possibilities of applying online learning to address the SR problem in uncoordinated WLANs. In particular, we provide a solution based on Multi-Armed Bandits (MABs) whereby independent WLANs dynamically adjust their frequency channel, transmit power and sensitivity threshold. To that purpose, we provide two different strategies, which refer to selfish and environment-aware learning. While the former stands for pure individual behavior, the second one considers the performance experienced by surrounding networks, thus taking into account the impact of individual actions on the environment. Through these two strategies we delve into practical issues of applying MABs in wireless networks, such as convergence guarantees or adversarial effects. Our simulation results illustrate the potential of the proposed solutions for enabling SR in future WLANs. We show that substantial improvements on network performance can be achieved regarding throughput and fairness.