Federated Spatial Reuse Optimization in Next-Generation Decentralized IEEE 802.11 WLANs
This work addresses efficiency improvements in wireless networks for users and operators, but appears incremental as it builds on existing federated learning approaches in a specific domain.
The paper tackles the problem of optimizing spatial reuse in next-generation IEEE 802.11ax wireless networks by applying federated learning models to predict performance, as part of a 2021 ITU challenge.
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.