Papers › Coordinated Spatial Reuse Scheduling With Machine Learning in IEEE 802.11 MAPC Networks
Coordinated Spatial Reuse Scheduling With Machine Learning in IEEE 802.11 MAPC Networks
Maksymilian Wojnar, Wojciech Ciężobka, Artur Tomaszewski, Piotr Chołda, Krzysztof Rusek, Katarzyna Kosek-Szott, Jetmir Haxhibeqiri, Jeroen Hoebeke, Boris Bellalta, Anatolij Zubow, Falko Dressler, Szymon Szott
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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.
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