An O-RAN Framework for AI/ML-Based Localization with OpenAirInterface and FlexRIC
Nada Bouknana, Mohsen Ahadi, Florian Kaltenberger, Robert Schmidt
TL;DR
The paper tackles the lack of AI/ML-based localization support in 3GPP/O-RAN by proposing an end-to-end O-RAN–compliant framework that enables real-time AI-driven localization. It introduces a custom E2SM-SRS and a localization xApp running on a Near-RT RIC, implemented with OpenAirInterface and FlexRIC, to perform Channel Charting on UL-SRS channel estimates. The authors provide a concrete open-source implementation and validate it on the Firecell GEO-5G testbed at EURECOM, demonstrating real-time inference and practical viability for AI-native positioning in disaggregated RANs. The work lays a reproducible foundation for future multi-cell deployments, additional ML methods, and sensing capabilities within AI-native networks.
Abstract
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.