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Federated spatial reuse optimization in next-generation decentralized IEEE 802.11 WLANS
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Authors: Francesc Wilhelmi, Jernej Hribar, Selim F. Yilmaz, Emre Ozfatura, Kerem Ozfatura, Ozlem Yildiz, Deniz Gunduz, Hao Chen, Xiaoying Ye, Lizhao You, Yulin Shao, Paolo Dini, Boris Bellalta
Status: Final
Date of publication: 11 July 2022
Published in: ITU Journal on Future and Evolving Technologies, Volume 3 (2022), Issue 2, Pages 117-133
Article DOI : https://doi.org/10.52953/TNYT6291
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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 overview of the set of FL solutions in this work is part of the 2021 International Telecommunication Union (ITU) AI for 5G Challenge. |
Keywords: Federated learning, IEEE 802.11ax, ITU Challenge 2021, machine learning, network simulator, spatial reuse
Rights: © International Telecommunication Union, available under the CC BY-NC-ND 3.0 IGO license.
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