用大模型自动配置5G基站智能服务，提升部署效率。
Advanced AI Service Provisioning in O-RAN through LLM Engine Integration
- 大模型理解运维指令，自动生成数据采集与部署代码
- 轻量级模型实时训练，通过API快速响应网络需求
- 适合通信工程师与自动化系统研发者参考
开放无线接入网（O-RAN）架构允许通过模块化xApps和rApps将AI嵌入无线接入网，但构建这些应用——包括数据收集、模型训练、代码编写及安全部署——仍依赖人工且耗时。大型语言模型（LLMs）具备强大推理与代码生成能力，但无法满足实时无线接入网控制所需的快速确定性推理。本文提出一个双脑架构的原型系统：基于LLM的编排器将运营商意图转化为数据采集策略与部署代码，同时集成自动化机器学习引擎NeuralSmith，通过API按需训练轻量级分类器。文章描述了该架构与部署流程，并基于容器化的O-RAN 5G SA测试床分享实践经验，探讨未来研究方向。
原文摘要 · Abstract (English)
The Open Radio Access Network (O-RAN) architecture allows AI to be embedded directly into the RAN through modular xApps and rApps, yet creating these applications collecting data, training models, writing code, and deploying them safely remains slow and largely manual. Large Language Models (LLMs) offer strong reasoning and code-generation capabilities but are unsuited for the fast, deterministic inference required in real-time RAN control. We present a proof-of-concept Dual-Brain architecture that combines both strengths: an LLM-based orchestrator translates operator intents into data-collection policies and deployment code, while an automated ML engine, NeuralSmith, trains lightweight classifiers on demand via an API. We describe the architecture and provisioning workflow, share practical insights from a containerized O-RAN 5G~SA testbed, and discuss open research directions.
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