2022/03/20 by Francesc Wilhelmi, Jernej Hribar, Wilhelmi, Francesc +29
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Cooperative Communication and Network Coding #Distributed #FOS: Computer and information sciences #Indoor and Outdoor Localization Technologies #Machine Learning (cs.LG) #Networking and Internet Architecture (cs.NI) #Parallel #Wireless Networks and Protocols #and Cluster Computing (cs.DC) #cs.AI #cs.DC #cs.LG #cs.NI
paper · pdf · doi:10.48550/arxiv.2203.10472
openalex publication_date 2022/03/20 · openalex created_date 2022/04/03 · arxiv created 2022/06/07 · arxiv updated 2022/06/08 · openalex updated_date 2026/07/28
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.