Mobile networks are highly complex systems. Therefore, it is crucial to examine them from an empirical perspective to better understand how network features affect performance, and suggest additional improvements. This article presents a large-scale dataset of measurements collected over fourth generation (4G) and fifth generation (5G) operational networks, providing Long Term Evolution (LTE), Narrowband Internet of Things (NB-IoT), and 5G New Radio (NR) connectivity. We collected our dataset during seven weeks in Rome, Italy, by performing several tests on the infrastructures of two major mobile network operators (MNOs). The open-sourced dataset has enabled multi-faceted analyses of network deployment, coverage, and end-user performance, and can be further used for designing and testing artificial intelligence (AI) and machine learning (ML) solutions for network optimization.

A large-scale dataset of 4G, NB-IoT, and 5G non-standalone network measurements / Kousias, Konstantinos; Rajiullah, Mohammad; Caso, Giuseppe; Ali, Usman; Alay, Ozgu; Brunstrom, Anna; De Nardis, Luca; Neri, Marco; Di Benedetto, Maria-Gabriella. - In: IEEE COMMUNICATIONS MAGAZINE. - ISSN 0163-6804. - 62:5(2024). [10.1109/mcom.011.2200707]

A large-scale dataset of 4G, NB-IoT, and 5G non-standalone network measurements

Caso, Giuseppe;Ali, Usman;De Nardis, Luca;Neri, Marco;Di Benedetto, Maria-Gabriella
2024

Abstract

Mobile networks are highly complex systems. Therefore, it is crucial to examine them from an empirical perspective to better understand how network features affect performance, and suggest additional improvements. This article presents a large-scale dataset of measurements collected over fourth generation (4G) and fifth generation (5G) operational networks, providing Long Term Evolution (LTE), Narrowband Internet of Things (NB-IoT), and 5G New Radio (NR) connectivity. We collected our dataset during seven weeks in Rome, Italy, by performing several tests on the infrastructures of two major mobile network operators (MNOs). The open-sourced dataset has enabled multi-faceted analyses of network deployment, coverage, and end-user performance, and can be further used for designing and testing artificial intelligence (AI) and machine learning (ML) solutions for network optimization.
2024
5G; positioning; experimental dataset
01 Pubblicazione su rivista::01a Articolo in rivista
A large-scale dataset of 4G, NB-IoT, and 5G non-standalone network measurements / Kousias, Konstantinos; Rajiullah, Mohammad; Caso, Giuseppe; Ali, Usman; Alay, Ozgu; Brunstrom, Anna; De Nardis, Luca; Neri, Marco; Di Benedetto, Maria-Gabriella. - In: IEEE COMMUNICATIONS MAGAZINE. - ISSN 0163-6804. - 62:5(2024). [10.1109/mcom.011.2200707]
File allegati a questo prodotto
File Dimensione Formato  
Kousias_A Large Scale_2024.pdf

accesso aperto

Note: Full text
Tipologia: Documento in Post-print (versione successiva alla peer review e accettata per la pubblicazione)
Licenza: Creative commons
Dimensione 1.62 MB
Formato Adobe PDF
1.62 MB Adobe PDF

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/11573/1702273
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 12
  • ???jsp.display-item.citation.isi??? 7
social impact