用大模型代理实现6G开放基站端到端智能服务部署
End-to-End Edge AI Service Provisioning Framework in 6G ORAN
- 用大语言模型代理自动解析用户需求并生成AI服务与网络配置
- 支持从模型仓库调用、服务部署到实时监控的全流程自动化
- 适合想快速构建6G边缘AI应用的开发者和运营商
随着6G的到来，开放无线接入网（O-RAN）架构正向智能化、自适应和自动化网络编排演进。本文提出一种基于大语言模型（LLM）代理的边缘AI与网络服务编排框架，将LLM代理作为O-RAN的rApp部署。该系统通过将用户用例描述转化为可部署的AI服务与对应网络配置，实现交互式、直观的编排。LLM代理自动完成多项任务：从模型仓库（如Hugging Face）中选择AI模型、服务部署、网络适配以及通过xApps进行实时监控。我们基于开源O-RAN项目（OpenAirInterface与FlexRIC）实现了原型系统，展示了从用户交互到网络适配的端到端流程，并确保服务质量（QoS）合规。本工作凸显了将LLM驱动自动化融入6G O-RAN生态系统的潜力，为更易用高效的边缘AI生态系统铺平道路。
原文摘要 · Abstract (English)
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.
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