The AI-native vision of 6G requires Radio Access Networks to train, deploy, and continuously refine thousands of machine learning (ML) models that drive real-time radio network optimization. Although the Open RAN (O-RAN) architecture provides open interfaces and an intelligent control plane, it leaves the life-cycle management of these models unspecified. Consequently, operators still rely on ad-hoc, manual update practices that can neither scale across the heterogeneous, multi-layer stack of Cell-Site, Edge-, Regional-, and Central-Cloud domains, nor across the three O-RAN control loops (real-, near-real-, and non-real-time). We present a self-learning framework that provides an efficient closed-loop version management for an AI-native O-RAN edge. In this framework, training pipelines in the Central/Regional Cloud continuously generate new models, which are cataloged along with their resource footprints, security scores, and accuracy metrics in a shared version repository. An Update Manager consults this repository and applies a self-learning policy to decide when and where each new model version should be promoted into operation. A container orchestrator then realizes these decisions across heterogeneous worker nodes, enabling multiple services (rApps, xApps, and dApps) to obtain improved inference with minimal disruption. Simulation results show that an efficient RL-driven decision-making can guarantee quality of service, bounded latencies while balancing model accuracy, system stability, and resilience.

翻译：6G的AI原生愿景要求无线接入网能够训练、部署并持续优化数以千计驱动实时无线网络优化的机器学习模型。尽管开放无线接入网架构提供了开放接口与智能控制平面，但并未明确这些模型的生命周期管理机制。因此，运营商仍依赖临时性人工更新方式，这种方式既无法在基站、边缘云、区域云和中心云构成的异构多层架构中扩展，也无法跨越O-RAN的实时、近实时与非实时三大控制环路。本文提出一种自学习框架，为AI原生O-RAN边缘提供高效的闭环版本管理。在该框架中，中心/区域云中的训练流水线持续生成新模型，这些模型与其资源占用、安全评分及精度指标共同收录于共享版本仓库。更新管理器通过查询该仓库并应用自学习策略，决策每个新模型版本应在何时何地投入运营。容器编排器随后在异构工作节点上执行这些决策，使多种服务能够以最小干扰获得优化的推理能力。仿真结果表明，采用高效的强化学习驱动决策机制可在平衡模型精度、系统稳定性与鲁棒性的同时，保障服务质量与延迟上限。