O-RAN 开发者终于有了将 LLM 的灵活性与实时推理效率结合起来的可行方案——双脑架构直接解决了 AI 应用部署慢、手动操作多的问题，做 RAN 智能化的团队值得关注这个原型和测试床经验。
O-RAN 架构允许通过模块化的 xApps 和 rApps 将 AI 直接嵌入到无线接入网中，但创建这些应用（数据收集、模型训练、代码编写和安全部署）仍然缓慢且主要依赖人工。大型语言模型（LLM）具备强大的推理和代码生成能力，但不适合实时 RAN 控制所需的快速、确定性推理。本文提出了一种概念验证的“双脑”架构，结合了两种优势：基于 LLM 的编排器将运营商意图转化为数据收集策略和部署代码，而自动化 ML 引擎 NeuralSmith 通过 API 按需训练轻量级分类器。文章描述了架构和部署工作流，分享了来自容器化 O-RAN 5G SA 测试床的实践见解，并讨论了开放的研究方向。
Advanced AI Service Provisioning in O-RAN through LLM Engine Integration
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.