End-to-end edge AI service provisioning framework in 6G ORAN

Abstract

As 6G networks evolve to support pervasive AI-driven applications, seamless provisioning of Edge AI services has become increasingly vital. However, current orchestration processes remain fragmented, requiring extensive coordination between AI-powered application developers and the network operators. In this paper, we propose a novel end-to-end orchestration framework that integrates Large Language Model (LLM) agents into O-RAN to automate edge AI service subscription and deployment. Our system translates high-level user intents into orchestrated workflows, including AI model selection, mobility-aware placement, and performance monitoring. We demonstrate the framework via a prototype built on our open-source ORAN simulator, showcasing intelligent, intent-driven AI service provisioning. This work represents a key step toward AI-native, accessible, and scalable service management in 6G.