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Towards an AI/ML-defined Radio for Wi-Fi: Overview, Challenges, and Roadmap

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arxiv 2405.12675 v1 pith:ZUKHCKTX submitted 2024-05-21 cs.NI

Boris Bellalta , Katarzyna Kosek-Szott , Szymon Szott , Francesc Wilhelmi This is my paper · ORCID
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keywords ml-definedradiochallengesradiosconceptoverviewpotentialroadmap
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Will AI/ML-defined radios become a reality in the near future? In this paper, we introduce the concept of an AI/ML-defined radio - a radio architecture specifically designed to support AI/ML-based optimization and decision-making in communication functions - and depict its promised benefits and potential challenges. Additionally, we discuss a potential roadmap for the development and adoption of AI/ML-defined radios, and highlight the enablers for addressing their associated challenges. While we offer a general overview of the AI/ML-defined radio concept, our focus throughout the paper remains on Wi-Fi, a wireless technology that may significantly benefit from the integration of AI/ML-defined radios, owing to its inherent decentralized management and operation within unlicensed frequency bands.

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  1. EvoOMG: An Evolution-Oriented Multi-Agent Guidance Framework for Heterogeneous Legacy-and-MLO Wi-Fi Networks

    cs.NI 2026-07 conditional novelty 6.0 of 10

    Decomposing Wi-Fi MAC control into staged contention-then-aggregation decisions improves throughput in mixed legacy/MLO networks compared to flat one-shot policies.

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