Decomposing Wi-Fi MAC control into staged contention-then-aggregation decisions improves throughput in mixed legacy/MLO networks compared to flat one-shot policies.
Towards an AI/ML-defined Radio for Wi-Fi: Overview, Challenges, and Roadmap
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abstract
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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cs.NI 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
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EvoOMG: An Evolution-Oriented Multi-Agent Guidance Framework for Heterogeneous Legacy-and-MLO Wi-Fi Networks
Decomposing Wi-Fi MAC control into staged contention-then-aggregation decisions improves throughput in mixed legacy/MLO networks compared to flat one-shot policies.