Localization is increasingly becoming an integral component of wireless cellular networks. The advent of artificial intelligence (AI) and machine learning (ML) based localization algorithms presents potential for enhancing localization accuracy. Nevertheless, current standardization efforts in the third generation partnership project (3GPP) and the O-RAN Alliance do not support AI/ML-based localization. In order to close this standardization gap, this paper describes an O-RAN framework that enables the integration of AI/ML-based localization algorithms for real-time deployments and testing. Specifically, our framework includes an O-RAN E2 Service Model (E2SM) and the corresponding radio access network (RAN) function, which exposes the Uplink Sounding Reference Signal (UL-SRS) channel estimates from the E2 agent to the Near real-time RAN Intelligent Controller (Near-RT RIC). Moreover, our framework includes, as an example, a real-time localization external application (xApp), which leverages the custom E2SM-SRS in order to execute continuous inference on a trained Channel Charting (CC) model, which is an emerging self-supervised method for radio-based localization. Our framework is implemented with OpenAirInterface (OAI) and FlexRIC, democratizing access to AI-driven positioning research and fostering collaboration. Furthermore, we validate our approach with the CC xApp in real-world conditions using an O-RAN based localization testbed at EURECOM. The results demonstrate the feasibility of our framework in enabling real-time AI/ML localization and show the potential of O-RAN in empowering positioning use cases for next-generation AI-native networks.

翻译：定位技术正日益成为无线蜂窝网络的核心组成部分。基于人工智能（AI）与机器学习（ML）的定位算法为提升定位精度带来了潜力。然而，当前第三代合作伙伴计划（3GPP）与O-RAN联盟的标准化工作尚未支持基于AI/ML的定位。为填补这一标准化空白，本文提出一种O-RAN框架，支持基于AI/ML的定位算法在实时部署与测试中的集成。具体而言，该框架包含一个O-RAN E2服务模型（E2SM）及相应的无线接入网（RAN）功能，可将上行探测参考信号（UL-SRS）的信道估计从E2代理暴露给近实时无线接入网络智能控制器（Near-RT RIC）。此外，作为示例，本框架还包含一个实时定位外部应用（xApp），该应用利用定制的E2SM-SRS对训练后的信道图谱（CC）模型执行持续推理——信道图谱是一种新兴的基于无线电的自监督定位方法。本框架基于OpenAirInterface（OAI）与FlexRIC实现，为AI驱动的定位研究提供了开放平台并促进协作。进一步地，我们在EURECOM的O-RAN定位测试平台上，通过CC xApp在真实环境中验证了该方案。结果表明，该框架能够有效支持实时AI/ML定位，并展现了O-RAN在赋能下一代AI原生网络定位应用场景方面的潜力。