Coordinated Multi-Armed Bandits for Improved Spatial Reuse in Wi-Fi

• URL: http://arxiv.org/abs/2412.03076v1
• Date: Wed, 04 Dec 2024 06:53:59 GMT
• Title: Coordinated Multi-Armed Bandits for Improved Spatial Reuse in Wi-Fi
• Authors: Francesc Wilhelmi, Boris Bellalta, Szymon Szott, Katarzyna Kosek-Szott, Sergio Barrachina-Muñoz,
• Abstract summary: We explore a coordinated solution based on online learning to drive the optimization of Spatial Reuse (SR) In particular, we focus on a Multi-Agent Multi-Armed Bandit (MA-MAB) setting, where multiple decision-making agents concurrently configure SR parameters from coexisting networks. We demonstrate that AI-native SR enabled by coordinated MABs can improve the network performance over current Wi-Fi operation.
• Score: 2.143751226500554
• License:
• Abstract: Multi-Access Point Coordination (MAPC) and Artificial Intelligence and Machine Learning (AI/ML) are expected to be key features in future Wi-Fi, such as the forthcoming IEEE 802.11bn (Wi-Fi 8) and beyond. In this paper, we explore a coordinated solution based on online learning to drive the optimization of Spatial Reuse (SR), a method that allows multiple devices to perform simultaneous transmissions by controlling interference through Packet Detect (PD) adjustment and transmit power control. In particular, we focus on a Multi-Agent Multi-Armed Bandit (MA-MAB) setting, where multiple decision-making agents concurrently configure SR parameters from coexisting networks by leveraging the MAPC framework, and study various algorithms and reward-sharing mechanisms. We evaluate different MA-MAB implementations using Komondor, a well-adopted Wi-Fi simulator, and demonstrate that AI-native SR enabled by coordinated MABs can improve the network performance over current Wi-Fi operation: mean throughput increases by 15%, fairness is improved by increasing the minimum throughput across the network by 210%, while the maximum access delay is kept below 3 ms.

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