Machine Learning & Wi-Fi - Unveiling the Path Towards AI/ML-Native IEEE 802.11 Networks

Aug 5, 2026

Artificial intelligence (AI) and machine learning (ML) are mature technologies increasingly viewed as essential for evolving future communications systems. Wi-Fi has progressed through decades of feature-by-feature enhancements, creating mounting complexity and new operational challenges. The work examines why AI/ML capabilities may be needed for emerging Wi-Fi problems, outlines a roadmap toward AI/ML-native Wi-Fi, and discusses key challenges, standardization efforts, enablers, and an illustrative use case across adoption stages.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages7

Reading Time18 min

AccessFree to download

Introduction

AI/ML as a driver for next-generation communications

Evolution and complexity of IEEE 802.11

Motivations for AI/ML-native Wi-Fi

Role and integration directions

Machine Learning & Wi-Fi: Unveiling the Path Towards AI/ML-Native IEEE 802.11 Networks Francesc Wilhelmi, Szymon Szott, Katarzyna Kosek-Szott, Boris Bellalta arXiv :2405 . 11504v2 [ cs .NI] 30 Aug 2024 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 WiFi, key challenges, standardization efforts, and major enablersare also discussed. An exemplary use case is provided to showcase the potential of AI/ML in Wi-Fi at different adoption stages. Index Terms—Artificial Intelligence, IEEE 802.11, Machine Learning, Wi-Fi I. INTRODUCTION The IEEE 802.11 standard (commercially known as Wi-Fi), since its first release in 1997, has accompanied humanity by providing an entry point to a connected world. Wi-Fi has continuously evolved together with its users, their applications, and habits. As a result, it continues to be the most popular technology for indoor connectivity [1]1. In addition, the technological evolution of Wi-Fi—which has included a plethora of physical (PHY) and medium access control (MAC) enhancements—has enabled it to expand to other domains beyond residential, including enterprise and industry. Today, communication systems are expected to enable new verticals like Industry 5.0, autonomous vehicles, or smart cities, where unprecedented levels of performance and reliability are required. However, the current incremental-based status quo on evolving wireless communications standards and technologies—with 5G/6G as two of the most relevant examples with the introduction of new paradigms like ultrareliable low latency communications (uRLLC) or integrated sensing and communications—seems to be insufficient for such stringent performance targets. Moreover, the underlying F. Wilhelmi is with Nokia Bell Labs. S. Szott and K. Kosek-Szott are with AGH University of Krakow. B. Bellalta is with Universitat Pompeu Fabra. This paper is supported by the CHIST-ERA Wireless AI 2022 call MLDR project (ANR-23-CHR4-0005), partially funded by AEI and NCN under projects PCI2023-145958-2 and 2023/05/Y/ST7/00004, respectively. B. Bellalta’s contribution is supported by Wi-XR PID2021123995NBI00 (MCIU/AEI/FEDER,UE) and MdMCEX2021-001195-M/ AEI /10 . 13039/501100011033 . 1Even if cellular networks provide stationary Internet connectivity through solutions like Fixed Wireless Access (FWA), the indoor part of the access network remains Wi-Fi. Meanwhile, future cellular 5G/6G networks may also be deployed indoors, although given the proliferation of Wi-Fi, such deployments will most likely be restricted to private networks in industrial environments. complexity and the incurred overheads of some mechanisms adopted in communications (e.g., massive antenna arrays) demand innovative ways of operating networks, which must now hoard self-configuration capabilities [2] . Nowadays, we are witnessing the first steps towards the adoption of Artificial Intelligence (AI) and Machine Learning (ML) in communication networks to automate high-level management operations such as the identification and prediction of potential issues and failures. However, the aforementioned factors call for a paradigm shift, which could be achieved by AI/ML. AI/ML is a computer programming paradigm that allows learning functions directly from data. AI/ML has boosted many domains thanks to its ability to exploit complex characteristics from data, thu

FAQs

Why are AI/ML functionalities increasingly relevant for Wi-Fi evolution?

Wi-Fi complexity has grown through continuous PHY and MAC enhancements, making upcoming challenges harder to address with traditional approaches. AI/ML offers automated mechanisms for identification and prediction of issues and failures.

What does the paper aim to cover regarding AI/ML-native IEEE 802.11 networks?

It presents the status and future evolution of IEEE 802.11 tied to AI/ML, covering the main challenges and enablers, standardization gaps, and required actions. It also includes simulation results and an exemplary use case.

How does the document position AI/ML in broader wireless communication systems?

AI/ML is described as a key building block for 5G/6G, with 3GPP incorporating AI/ML in cellular standards. The paper contrasts this with the growing adoption of AI/ML in Wi-Fi research and industry implementations.

Aug 5, 2026

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