The convergence of communication, sensing, and Artificial Intelligence (AI) in the Radio Access Network (RAN) offers compelling economic advantages through shared spectrum and infrastructure. How can inference and sensing be integrated in the RAN infrastructure at a system level? Current abstractions in O-RAN and 3GPP lack the interfaces and capabilities to support (i) a dynamic life cycle for inference and Integrated Sensing and Communication (ISAC) algorithms, whose requirements and sensing targets may change over time and across sites; (ii) pipelines for AI-driven ISAC, which need complex data flows, training, and testing; (iii) dynamic device and stack configuration to balance trade-offs between connectivity, sensing, and inference services. This paper analyzes the role of a programmable, software-driven, open RAN in enabling the intelligent edge for 5G and 6G systems. We identify real-time user-plane data exposure, open interfaces for plug-and-play inference and ISAC models, closed-loop control, and AI pipelines as elements that evolutions of the O-RAN architecture can uniquely provide. Specifically, we describe how dApps - a real-time, user-plane extension of O-RAN - and a hierarchy of controllers enable real-time AI inference and ISAC. Experimental results on an open-source RAN testbed demonstrate the value of exposing I/Q samples and real-time RAN telemetry to dApps for sensing applications.

翻译：通信、感知与人工智能在无线接入网中的融合，通过频谱与基础设施共享展现出显著的经济优势。如何在系统层面将推理与感知集成到RAN基础设施中？当前O-RAN与3GPP的抽象层缺乏支持以下能力的接口与功能：（i）推理与通感一体化算法动态生命周期管理——其需求与感知目标可能随时间和站点变化；（ii）AI驱动的通感一体化流水线——需复杂数据流、训练与测试支持；（iii）动态设备与协议栈配置——需平衡连接、感知与推理服务间的性能折衷。本文分析了可编程、软件驱动的开放RAN在赋能5G/6G智能边缘系统中的作用。我们识别出实时用户面数据暴露、即插即用推理及通感一体化模型的开放接口、闭环控制与AI流水线，作为O-RAN架构演进可独特提供的核心要素。具体而言，我们阐述了dApps（O-RAN的实时用户面扩展）与分层控制器体系如何实现实时AI推理与通感一体化。基于开源RAN测试平台的实验结果表明，将I/Q样本与实时RAN遥测数据暴露给dApps对感知应用具有重要价值。