From Concept to Reality: 5G Positioning with Open-Source Implementation of UL-TDoA in OpenAirInterface
read the original abstract
This paper presents, for the first time, an open-source implementation of the 3GPP Uplink Time Difference of Arrival (UL-TDoA) positioning method using the OpenAirInterface (OAI) framework. UL-TDoA is a critical positioning technique in 5G networks, leveraging the time differences of signal arrival at multiple base stations to determine the precise location of User Equipment (UE). This implementation aims to democratize access to advanced positioning technology by integrating UL-TDoA capabilities into both the Radio Access Network (RAN) and Core Network (CN) components of OAI, providing a comprehensive and 3GPP-compliant solution. The development includes the incorporation of essential protocol procedures, message flows, and interfaces as defined by 3GPP standards. Validation is conducted using two distinct methods: an OAI-RF simulator-based setup for controlled testing and an O-RAN-based Localization Testbed at EURECOM in real-world conditions. The results demonstrate the viability of this open-source UL-TDoA implementation, enabling precise positioning in various environments. By making this implementation publicly available, the study paves the way for widespread research, development, and innovation in the field of 5G positioning technologies, fostering collaboration and accelerating the advancement of cellular network positioning.
This paper has not been read by Pith yet.
Forward citations
Cited by 2 Pith papers
-
CellSense: A Sub-6 GHz Cellular ISAC System for Clutter-Robust Passive Sensing
CellSense integrates ISAC into the 5G protocol stack for sub-6 GHz passive sensing, reporting 74-94% detection probability and 0.33-1.43 m localization error in simulations plus 76% detection and 1.28 m accuracy in cl...
-
TDoA-Based Self-Supervised Channel Charting with NLoS Mitigation
TDoA-based self-supervised channel charting with NLoS mitigation and UE displacement achieves 2-4 meter accuracy in 90% of cases in a real 5G testbed, outperforming prior semi- and self-supervised methods.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.