An O-RAN Framework for AI/ML-Based Localization with OpenAirInterface and FlexRIC

2025-11-24

AI Summary

Current 3GPP and O-RAN standards lack native support for AI/ML-based positioning algorithms, representing a critical standardization gap. To address this, we propose E2SM-SRS—the first O-RAN E2 Service Model explicitly designed for intelligent positioning—which exposes uplink reference signal channel estimates in real time via the E2 interface to enable low-latency, online AI inference. Methodologically, we integrate self-supervised channel charting with open-source platforms including OpenAirInterface, FlexRIC, and the Near-Real-Time RIC. We implement and validate the end-to-end AI positioning framework on the EURECOM O-RAN testbed. Results demonstrate millisecond-level continuous inference and significantly improved positioning accuracy. This work constitutes the first standardized and experimentally validated AI-native positioning solution within the O-RAN architecture, bridging both theoretical and practical gaps. It establishes a deployable technical paradigm for intelligent, AI-driven positioning in 6G networks.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Plan Execution and Monitoring
• Machine Learning: Hardware-aware ML

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• Developing an O-RAN framework for AI/ML-based localization integration
• Addressing the standardization gap in 3GPP and O-RAN for AI localization
• Enabling real-time deployment and testing of AI-driven positioning algorithms

Innovation

• O-RAN framework enables AI/ML localization integration
• Custom E2SM-SRS exposes UL-SRS channel estimates to RIC
• OpenAirInterface and FlexRIC implementation supports real-time inference