Efficient Self-Learning and Model Versioning for AI-native O-RAN Edge

TL;DR

This paper introduces a self-learning framework for managing ML model lifecycle in AI-native 6G O-RAN networks, enabling scalable, automated updates across diverse network domains and control loops.

Contribution

It proposes an efficient, closed-loop version management system with a shared repository and RL-based decision policy for model promotion in O-RAN edge environments.

Findings

RL-driven decision-making guarantees quality of service

System balances model accuracy and stability

Simulation confirms effectiveness of the approach

Abstract

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.