Experimental Insights from OpenAirInterface 5G positioning Testbeds: Challenges and solutions

2025-08-27

AI Summary

To address the challenge of high-precision localization in 5G NR for smart cities and factories—constrained by synchronization errors, multipath propagation, and unfavorable base station geometry—this work establishes three indoor/outdoor uplink time-difference-of-arrival (UL-TDoA) testbeds based on OpenAirInterface. It introduces, for the first time in an open-source 5G system, a 3GPP-compliant Location Management Function (LMF). Methodologically, we propose a customized filtering strategy for time-of-arrival (ToA)/TDoA measurements and a particle swarm optimization (PSO)-based positioning algorithm. Furthermore, we pioneer a data-driven localization framework that leverages unconventional channel impulse response (CIR) features to train AI/ML models. Experimental evaluation across diverse representative scenarios demonstrates sub-meter accuracy: 90% of positioning errors are ≤1–2 m, validating system feasibility. The publicly released real-world dataset serves as a valuable benchmark resource for the research community.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Localization, Mapping, and Navigation
• Search and Optimization: Local Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

5G New Radio (NR) is a key enabler of accurate positioning in smart cities and smart factories. This paper presents the experimental results from three 5G positioning testbeds running open-source OpenAirInterface (OAI) gNB and Core Network (CN), using Uplink Time Difference of Arrival (UL-TDoA) with the newly integrated Location Management Function (LMF). The testbeds are deployed across both indoor factories and outdoor scenarios with O-RAN Radio Units (RUs), following a 3GPP-compliant system model. The experiments highlight the impact of synchronization impairments, multipath propagation, and deployment geometry on positioning accuracy. To address these challenges, we propose tailored ToA and TDoA filtering as well as a novel position estimation method based on Particle Swarm Optimization (PSO) within the LMF pipeline. Moreover, we show a beyond-5G framework that leverages non-conventional measurements such as Channel Impulse Response (CIR) to train and test Artificial Intelligence and Machine Learning (AI/ML) models for data-driven positioning. The results demonstrate the feasibility of achieving 1-2 meter positioning accuracy in 90% of cases in different testbeds, offering practical insights for the design of robust 5G positioning systems. Moreover, we publicly release the datasets collected in this work to support the research within the 5G positioning community.

Problem

• Addressing 5G positioning challenges from synchronization and multipath
• Proposing filtering and optimization methods for location accuracy
• Developing AI/ML framework using non-conventional measurements for positioning

Innovation

• UL-TDoA with LMF for 5G positioning
• PSO-based position estimation method
• AI/ML models using CIR measurements