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

TL;DR

This paper introduces a hierarchical multi-armed bandit framework using reinforcement learning to optimize coordinated spatial reuse among multiple Wi-Fi access points, improving network efficiency in dense environments.

Contribution

It proposes a novel hierarchical MAB approach for AP coordination in Wi-Fi 8, demonstrating the effectiveness of UCB algorithms in dynamic network conditions.

Findings

UCB algorithm shows rapid convergence and adaptability.

Hierarchical MAB improves AP transmission coordination.

Reinforcement learning enhances Wi-Fi dense network management.

Abstract

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.