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不可或缺的组成部分。基于人工智能（AI）与机器学习（ML）的定位算法展现出提升定位精度的潜力。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的定位方案。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O-RAN E2服务模型（E2SM）及对应的无线接入网（RAN）功能，可将上行探测参考信号（UL-SRS）信道估计从E2代理端开放至近实时无线接入网络智能控制器（Near-RT RIC）。此外，本框架以实时定位外部应用（xApp）为例，通过定制化E2SM-SRS对训练完成的信道图谱（CC）模型执行持续推理——这是一种新兴的基于无线电信号的自监督定位方法。该框架基于OpenAirInterface（OAI）与FlexRIC实现，为AI驱动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在真实场景中验证了该方案。实验结果表明，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。