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

TL;DR

This paper investigates the use of federated machine learning to optimize spatial reuse in next-generation IEEE 802.11ax WLANs, aiming to improve network performance without compromising data privacy.

Contribution

It demonstrates the feasibility of applying federated learning models to predict and optimize spatial reuse in WLANs, advancing AI-driven network management.

Findings

FL models can accurately predict spatial reuse performance

Federated approach preserves data privacy

Enhances network efficiency in WLANs

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.