为5G/6G无线网设计统一的AI模型管理框架，解决厂商锁定和模型漂移问题。
AI/ML Life Cycle Management for Interoperable AI Native RAN
- 提出五模块生命周期管理架构，支持从训练到部署的全流程管控。
- 3GPP R17–R20逐步实现模型全周期管理，支持跨厂商模型协作与双向信道压缩。
- 适用于5G演进和6G智能基站研发，尤其关注模型透明与高效运维。
人工智能（AI）和机器学习（ML）正快速融入5G无线接入网（RAN），支持波束管理、信道状态信息（CSI）反馈、定位和移动性预测。然而，缺乏标准化的生命周期管理（LCM）框架，导致模型漂移、厂商锁定和透明度不足等问题，制约大规模部署。3GPP R17–R20逐步引入了对AI/ML的管理及空中接口支持，涵盖模型训练、验证、部署、推理、数据收集、性能监控、适用性评估和特定功能控制。其中，R20进一步扩展至双向CSI压缩和跨厂商模型运行能力。本文综述了由此形成的五块式LCM架构、基于关键绩效指标（KPI）的监控机制以及跨厂商协作方案，并提出了增强型LCM框架，明确各功能模块间交互流程，建立参考模型与厂商模型在双向操作中的协同开发规程。同时指出资源高效监控、环境漂移检测、智能决策和灵活训练等开放挑战。这些进展为6G时代的原生智能收发器奠定了基础。
原文摘要 · Abstract (English)
Artificial intelligence (AI) and machine learning (ML) are rapidly becoming integral to the 5G Radio Access Network (RAN), enabling 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 deployment. 3GPP Releases 17--20 have progressively introduced AI/ML management and air-interface support, covering model training, validation, deployment, inference, data collection, performance monitoring, applicability assessment, and feature-specific control. Release 20 further extends these capabilities to two-sided CSI compression and inter-vendor model operation. This article reviews the resulting five-block LCM architecture, KPI-driven monitoring mechanisms, and inter-vendor collaboration schemes. We further propose an enhanced LCM framework with detailed interactions across functional blocks and an integrated procedure for reference-model and vendor-model development in two-sided operation, and identify open challenges in resource-efficient monitoring, environment drift detection, intelligent decision-making, and flexible model training. These developments provide a foundation for AI-native transceivers in 6G.
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