This paper presents an experimental study on the integration of the fifth generation (5G) cellular network and the Global Navigation Satellite System (GNSS) for positioning. Along with the use of experimental data collected from both 5G and GNSS deployed technologies, the novelty of this research includes the design of a Bayesian tracking methodology based on extended Kalman filtering for processing the 5G Time of Flights (TOFs) and jointly estimating the user position and the clock offsets of the 5G Base Stations (BSs), as well for their fusion with GNSS observations (both single and dual frequency). Data collection and experimental analyses are conducted in both static and mobile positioning scenarios. For standalone 5G positioning, we demonstrate the need of tracking the clock offsets of BSs, as they currently represent a primary impairment for precise positioning. In static conditions, we manage to reduce the positioning error from more than 100 m to approximately 5 m by jointly estimating these offsets and correct the TOFs accordingly, showcasing that the current 5G network is a possible alternative to single frequency code-only GNSS positioning. The 5G+GNSS hybrid solution is shown to guarantee a more reliable and accurate positioning as the two technologies mutually assist each other, particularly when single frequency GNSS processing is considered. On the other hand, when the GNSS receiver is able to perform dual frequency processing, location estimation does not benefit from the hybridization with 5G as GNSS is already highly accurate. Overall, the achieved results confirm the viability of 5G+GNSS integration from an experimental standpoint, showing the potentialities of the currently-deployed 5G network for positioning.

Integration of 5G and GNSS Technologies for Enhanced Positioning: An Experimental Study

Brambilla, Mattia;Alghisi, Marianna;Camajori Tedeschini, Bernardo;Catalin Grec, Florin;Italiano, Lorenzo;Pileggi, Chiara;Biagi, Ludovico;Nicoli, Monica;
2024-01-01

Abstract

This paper presents an experimental study on the integration of the fifth generation (5G) cellular network and the Global Navigation Satellite System (GNSS) for positioning. Along with the use of experimental data collected from both 5G and GNSS deployed technologies, the novelty of this research includes the design of a Bayesian tracking methodology based on extended Kalman filtering for processing the 5G Time of Flights (TOFs) and jointly estimating the user position and the clock offsets of the 5G Base Stations (BSs), as well for their fusion with GNSS observations (both single and dual frequency). Data collection and experimental analyses are conducted in both static and mobile positioning scenarios. For standalone 5G positioning, we demonstrate the need of tracking the clock offsets of BSs, as they currently represent a primary impairment for precise positioning. In static conditions, we manage to reduce the positioning error from more than 100 m to approximately 5 m by jointly estimating these offsets and correct the TOFs accordingly, showcasing that the current 5G network is a possible alternative to single frequency code-only GNSS positioning. The 5G+GNSS hybrid solution is shown to guarantee a more reliable and accurate positioning as the two technologies mutually assist each other, particularly when single frequency GNSS processing is considered. On the other hand, when the GNSS receiver is able to perform dual frequency processing, location estimation does not benefit from the hybridization with 5G as GNSS is already highly accurate. Overall, the achieved results confirm the viability of 5G+GNSS integration from an experimental standpoint, showing the potentialities of the currently-deployed 5G network for positioning.
2024
5G
Positioning
GNSS
Localization
tracking
File in questo prodotto:
File Dimensione Formato  
Integration_of_5G_and_GNSS_Technologies_for_Enhanced_Positioning_An_Experimental_Study.pdf

accesso aperto

: Publisher’s version
Dimensione 5.35 MB
Formato Adobe PDF
5.35 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1277826
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact