Federated Spatial Reuse Optimization in Next-Generation Decentralized IEEE 802.11 WLANs

• URL: http://arxiv.org/abs/2203.10472v1
• Date: Sun, 20 Mar 2022 06:58:31 GMT
• Title: Federated Spatial Reuse Optimization in Next-Generation Decentralized IEEE 802.11 WLANs
• Authors: Francesc Wilhelmi, Jernej Hribar, Selim F. Yilmaz, Emre Ozfatura, Kerem Ozfatura, Ozlem Yildiz, Deniz G\"und\"uz, Hao Chen, Xiaoying Ye, Lizhao You, Yulin Shao, Paolo Dini, Boris Bellalta
• Abstract summary: We explore the feasibility of applying machine learning (ML) in next-generation wireless local area networks (WLANs) More specifically, we focus on the IEEE 802.11ax spatial reuse problem and predict its performance through federated learning (FL) models. The set of FL solutions overviewed in this work is part of the 2021 International Telecommunication Union (ITU) AI for 5G Challenge.
• Score: 14.954710245070343
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As wireless standards evolve, more complex functionalities are introduced to address the increasing requirements in terms of throughput, latency, security, and efficiency. To unleash the potential of such new features, artificial intelligence (AI) and machine learning (ML) are currently being exploited for deriving models and protocols from data, rather than by hand-programming. In this paper, we explore the feasibility of applying ML in next-generation wireless local area networks (WLANs). More specifically, we focus on the IEEE 802.11ax spatial reuse (SR) problem and predict its performance through federated learning (FL) models. The set of FL solutions overviewed in this work is part of the 2021 International Telecommunication Union (ITU) AI for 5G Challenge.

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