# IEEE 802.11bn Multi-AP Coordinated Spatial Reuse with Hierarchical Multi-Armed Bandits

arXiv: https://arxiv.org/abs/2501.03680 · HTML: https://mlsanity.com/paper/2501.03680/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jan 2025.

## Built on by (most cited first, 2 of 2)

- [Learning Multi-Access Point Coordination in Agentic AI Wi-Fi with Large Language Models](https://mlsanity.com/paper/2511.20719/) (2025)
- [FM4WiFi: Flow Matching for Multi-AP Coordination in Dense Deployments of Beyond Wi-Fi 8 Networks](https://mlsanity.com/paper/2608.04050/) (2026)

## Abstract (arXiv)

Coordination among multiple access points (APs) is integral to IEEE 802.11bn (Wi-Fi 8) for managing contention in dense networks. This letter explores the benefits of Coordinated Spatial Reuse (C-SR) and proposes the use of reinforcement learning to optimize C-SR group selection. We develop a hierarchical multi-armed bandit (MAB) framework that efficiently selects APs for simultaneous transmissions across various network topologies, demonstrating reinforcement learning's promise in Wi-Fi settings. Among several MAB algorithms studied, we identify the upper confidence bound (UCB) as particularly effective, offering rapid convergence, adaptability to changes, and sustained performance.