Experimental Insights from OpenAirInterface 5G positioning Testbeds: Challenges and solutions
Mohsen Ahadi, Adeel Malik, Omid Esrafilian, Florian Kaltenberger, Cedric Thienot

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…
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