Machine Learning & Wi-Fi: Unveiling the Path Towards AI/ML-Native IEEE 802.11 Networks
Francesc Wilhelmi, Szymon Szott, Katarzyna Kosek-Szott, Boris Bellalta
TL;DR
This paper investigates the role and evolution of AI/ML in IEEE 802.11 WLANs. It surveys current standardization activities (IEEE 802.11 AIML TIG) and four use cases (CSI feedback compression, enhanced roaming, DRL-based channel access, MAPC driven by AI/ML), and discusses adoption and standardization challenges. It proposes a three-stage roadmap toward AI-native Wi‑Fi—Early adoption, Consolidation, and AI-nativeness—with a roadmap for standardization, interoperability, and security. The paper supports its claims with a representative spatial reuse use case showing potential gains, while highlighting that realizing AI-native WLANs requires coordinated standardization, model management, and trustworthy AI practices.
Abstract
Artificial intelligence (AI) and machine learning (ML) are nowadays mature technologies considered essential for driving the evolution of future communications systems. Simultaneously, Wi-Fi technology has constantly evolved over the past three decades and incorporated new features generation after generation, thus gaining in complexity. As such, researchers have observed that AI/ML functionalities may be required to address the upcoming Wi-Fi challenges that will be otherwise difficult to solve with traditional approaches. This paper discusses the role of AI/ML in current and future Wi-Fi networks and depicts the ways forward. A roadmap towards AI/ML-native Wi-Fi, key challenges, standardization efforts, and major enablers are also discussed. An exemplary use case is provided to showcase the potential of AI/ML in Wi-Fi at different adoption stages.