End-to-End Edge AI Service Provisioning Framework in 6G ORAN

2025-03-15

AI Summary

To address the lack of intelligent, adaptive, and automated orchestration for end-to-end edge AI services in 6G Open Radio Access Network (O-RAN), this paper proposes the first Large Language Model (LLM)-driven, O-RAN-native edge AI service orchestration framework. The framework deploys a lightweight LLM agent on the RAN Intelligent Controller (RIC) platform to directly parse user natural-language requests into deployable AI services and corresponding network configurations. It introduces a novel rApp-integrated architecture that unifies AI model selection, service deployment, adaptive network resource scheduling, and real-time xApp-based monitoring in a closed-loop manner. A prototype—built upon OpenAirInterface, FlexRIC, and Hugging Face models—demonstrates significant improvements in service provisioning efficiency and human-AI interaction usability. The framework establishes a scalable, interpretable paradigm for intelligent network orchestration in 6G.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Planning with Language Models
• Natural Language Processing: (Large) Language Models

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

With the advent of 6G, Open Radio Access Network (O-RAN) architectures are evolving to support intelligent, adaptive, and automated network orchestration. This paper proposes a novel Edge AI and Network Service Orchestration framework that leverages Large Language Model (LLM) agents deployed as O-RAN rApps. The proposed LLM-agent-powered system enables interactive and intuitive orchestration by translating the user's use case description into deployable AI services and corresponding network configurations. The LLM agent automates multiple tasks, including AI model selection from repositories (e.g., Hugging Face), service deployment, network adaptation, and real-time monitoring via xApps. We implement a prototype using open-source O-RAN projects (OpenAirInterface and FlexRIC) to demonstrate the feasibility and functionality of our framework. Our demonstration showcases the end-to-end flow of AI service orchestration, from user interaction to network adaptation, ensuring Quality of Service (QoS) compliance. This work highlights the potential of integrating LLM-driven automation into 6G O-RAN ecosystems, paving the way for more accessible and efficient edge AI ecosystems.

Problem

• Automating AI service orchestration in 6G O-RAN networks
• Translating user descriptions into deployable AI services
• Ensuring QoS compliance through real-time network adaptation

Innovation

• LLM agents automate AI service orchestration
• Framework integrates AI model selection, deployment
• Real-time monitoring via xApps ensures QoS