AI/ML Life Cycle Management for Interoperable AI Native RAN
This addresses the problem of large-scale AI/ML adoption in 5G networks for telecom operators and vendors, though it is incremental as it builds on existing 3GPP standards.
The paper tackles the lack of standardized life-cycle management for AI/ML models in 5G Radio Access Networks, which causes issues like model drift and vendor lock-in, by reviewing 3GPP standards from Releases 16-20 that establish a framework for interoperable AI-native RAN, culminating in features like a vendor-agnostic LCM profile and two-sided CSI-compression.
Artificial intelligence (AI) and machine learning (ML) models are rapidly permeating the 5G Radio Access Network (RAN), powering beam management, channel state information (CSI) feedback, positioning, and mobility prediction. However, without a standardized life-cycle management (LCM) framework, challenges, such as model drift, vendor lock-in, and limited transparency, hinder large-scale adoption. 3GPP Releases 16-20 progressively evolve AI/ML from experimental features to managed, interoperable network functions. Beginning with the Network Data Analytics Function (NWDAF) in Rel-16, subsequent releases introduced standardized interfaces for model transfer, execution, performance monitoring, and closed-loop control, culminating in Rel-20's two-sided CSI-compression Work Item and vendor-agnostic LCM profile. This article reviews the resulting five-block LCM architecture, KPI-driven monitoring mechanisms, and inter-vendor collaboration schemes, while identifying open challenges in resource-efficient monitoring, environment drift detection, intelligent decision-making, and flexible model training. These developments lay the foundation for AI-native transceivers as a key enabler for 6G.