mlsanityData to 7 Oct 2026

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

arXiv 2203.10472 ↗First posted 20 Mar 2022cs.NI cs.AI cs.DCcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 28 of its 65 references that are in the map. First posted on arXiv 20 Mar 2022.

Builds on

28 of 65 references.

References · 28

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16 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 1.

2 papers.

  1. 2024Private Collaborative Edge Inference via Over-the-Air Computationcited by 4
  2. 2026Studying the Role of Synthetic Data for Machine Learning-based Wireless Networks Traffic Forecastingcited by 0