Coordinated Spatial Reuse Scheduling With Machine Learning in IEEE 802.11 MAPC Networks

TL;DR

This paper introduces a machine learning-based scheduling method for Wi-Fi networks that improves throughput by coordinating simultaneous transmissions, validated through simulations and testbed experiments.

Contribution

It proposes a novel reinforcement learning approach for coordinated spatial reuse in Wi-Fi, achieving significant throughput gains over legacy systems.

Findings

H-MABs improve aggregate throughput by 80% in simulations

The framework is lightweight and suitable for real Wi-Fi devices

Theoretical bounds optimize throughput or fairness in scheduling

Abstract

The densification of Wi-Fi deployments means that fully distributed random channel access is no longer sufficient for high and predictable performance. Therefore, the upcoming IEEE 802.11bn amendment introduces multi-access point coordination (MAPC) methods. This paper addresses a variant of MAPC called coordinated spatial reuse (C-SR), where devices transmit simultaneously on the same channel, with the power adjusted to minimize interference. The C-SR scheduling problem is selecting which devices transmit concurrently and with what settings. We provide a theoretical upper bound model, optimized for either throughput or fairness, which finds the best possible transmission schedule using mixed-integer linear programming. Then, a practical, probing-based approach is proposed which uses multi-armed bandits (MABs), a type of reinforcement learning, to solve the C-SR scheduling problem. We validate both classical (flat) MAB and hierarchical MAB (H-MAB) schemes with simulations and in a testbed. Using H-MABs for C-SR improves aggregate throughput over legacy IEEE 802.11 (on average by 80% in random scenarios), without reducing the number of transmission opportunities per station. Finally, our framework is lightweight and ready for implementation in Wi-Fi devices.