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.File | Dimensione | Formato | |
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