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A Large-Scale Dataset of 4G, NB-IoT, and 5G Non-Standalone Network Measurements
University of Oslo, Norway.
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).ORCID iD: 0000-0003-2765-7873
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).ORCID iD: 0000-0003-0611-5637
Sapienza University of Rome, Italy.
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2024 (English)In: IEEE Communications Magazine, ISSN 0163-6804, E-ISSN 1558-1896, Vol. 62, no 5, p. 44-49Article in journal (Refereed) Published
Abstract [en]

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, so to suggest additional improvements. To this aim, this paper 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.

Place, publisher, year, edition, pages
IEEE, 2024. Vol. 62, no 5, p. 44-49
National Category
Communication Systems
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-97321DOI: 10.1109/mcom.011.2200707ISI: 001216638200001Scopus ID: 2-s2.0-85171556164OAI: oai:DiVA.org:kau-97321DiVA, id: diva2:1810124
Available from: 2023-11-07 Created: 2023-11-07 Last updated: 2024-07-08Bibliographically approved

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Rajiullah, MohammadCaso, GiuseppeAlay, ÖzgüBrunstrom, Anna

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