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

TL;DR

This paper presents experimental results from 5G positioning testbeds using open-source software, addressing challenges like synchronization and multipath, and proposing solutions including filtering, PSO, and AI/ML models to achieve 1-2 meter accuracy.

Contribution

It introduces novel filtering and position estimation methods, demonstrates the feasibility of high-accuracy 5G positioning, and publicly releases datasets for community use.

Findings

Achieved 1-2 meter positioning accuracy in 90% of cases

Identified key challenges affecting accuracy like synchronization and multipath

Proposed effective filtering and AI/ML approaches for robust positioning

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.