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arXiv Machine Learning By Chu-Hsiang Huang, Yuan-Chih Fan Chiang, Chao-Kai Wen, Geoffrey Ye Li

AI/ML Life Cycle Management for Interoperable AI Native RAN

Read the original on arXiv Machine Learning →

The article discusses the growing role of AI and ML in 5G Radio Access Networks (RAN) and the need for a standardized life‑cycle management (LCM) framework to address issues like model drift, vendor lock‑in, and limited transparency. It reviews the five‑block LCM architecture introduced by 3GPP Releases 17–20, KPI‑driven monitoring mechanisms, and inter‑vendor collaboration schemes, and proposes an enhanced LCM framework that integrates reference‑model and vendor‑model development for two‑sided operation. The paper also identifies open challenges in resource‑efficient monitoring, environment drift detection, intelligent decision‑making, and flexible model training, laying groundwork for AI‑native transceivers in 6G.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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