DoNext: An Open-Access Measurement Dataset for Machine Learning-Driven 5G Mobile Network Analysis
Future mobile use cases such as teleoperation rely on highly available mobile networks. Due to the nature of the mobile access channel and the inherent competition, the availability may be restricted in certain initially unknown areas or timespans. We automated mobile network data acquisition using...
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IEEE
2025-01-01
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| Series: | IEEE Transactions on Machine Learning in Communications and Networking |
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| Online Access: | https://ieeexplore.ieee.org/document/10976440/ |
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| author | Hendrik Schippers Melina Geis Stefan Bocker Christian Wietfeld |
| author_facet | Hendrik Schippers Melina Geis Stefan Bocker Christian Wietfeld |
| author_sort | Hendrik Schippers |
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| description | Future mobile use cases such as teleoperation rely on highly available mobile networks. Due to the nature of the mobile access channel and the inherent competition, the availability may be restricted in certain initially unknown areas or timespans. We automated mobile network data acquisition using a smartphone application and dedicated hardware to address this challenge, providing detailed connectivity insights. DoNext, a massive dataset of 4G and 5G mobile network data and active measurements, was collected over two years in Dortmund, Germany. To the best ofour knowledge, it is the most extensive openly available mobile dataset. Machine learning methods were applied to the data to demonstrate its utility inkey performance indicator prediction. Radio environmental maps facilitating key performance indicator predictions and application planning across different locations are generated through spatial aggregation for in-advance predictions. We also showcase signal strength modeling with transfer learning for arbitrary locations in individual mobile network cells, covering private and restricted areas. By openly providing the dataset, we aim to enable other researchers to develop and evaluate their machine-learning methods without conducting extensive measurement campaigns. |
| format | Article |
| id | doaj-art-4f8bdce420ed490e86d87d5c8386f94b |
| institution | OA Journals |
| issn | 2831-316X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Machine Learning in Communications and Networking |
| spelling | doaj-art-4f8bdce420ed490e86d87d5c8386f94b2025-08-20T02:15:35ZengIEEEIEEE Transactions on Machine Learning in Communications and Networking2831-316X2025-01-01358560410.1109/TMLCN.2025.356423910976440DoNext: An Open-Access Measurement Dataset for Machine Learning-Driven 5G Mobile Network AnalysisHendrik Schippers0https://orcid.org/0000-0003-2584-7104Melina Geis1https://orcid.org/0000-0003-3256-1921Stefan Bocker2https://orcid.org/0000-0003-1967-7794Christian Wietfeld3https://orcid.org/0000-0001-7653-2589Communication Networks Institute (CNI), TU Dortmund University, Dortmund, GermanyCommunication Networks Institute (CNI), TU Dortmund University, Dortmund, GermanyCommunication Networks Institute (CNI), TU Dortmund University, Dortmund, GermanyCommunication Networks Institute (CNI), TU Dortmund University, Dortmund, GermanyFuture mobile use cases such as teleoperation rely on highly available mobile networks. Due to the nature of the mobile access channel and the inherent competition, the availability may be restricted in certain initially unknown areas or timespans. We automated mobile network data acquisition using a smartphone application and dedicated hardware to address this challenge, providing detailed connectivity insights. DoNext, a massive dataset of 4G and 5G mobile network data and active measurements, was collected over two years in Dortmund, Germany. To the best ofour knowledge, it is the most extensive openly available mobile dataset. Machine learning methods were applied to the data to demonstrate its utility inkey performance indicator prediction. Radio environmental maps facilitating key performance indicator predictions and application planning across different locations are generated through spatial aggregation for in-advance predictions. We also showcase signal strength modeling with transfer learning for arbitrary locations in individual mobile network cells, covering private and restricted areas. By openly providing the dataset, we aim to enable other researchers to develop and evaluate their machine-learning methods without conducting extensive measurement campaigns.https://ieeexplore.ieee.org/document/10976440/5G new radio6Gdatasetmachine learningpredictive QoSmulti-MNO |
| spellingShingle | Hendrik Schippers Melina Geis Stefan Bocker Christian Wietfeld DoNext: An Open-Access Measurement Dataset for Machine Learning-Driven 5G Mobile Network Analysis IEEE Transactions on Machine Learning in Communications and Networking 5G new radio 6G dataset machine learning predictive QoS multi-MNO |
| title | DoNext: An Open-Access Measurement Dataset for Machine Learning-Driven 5G Mobile Network Analysis |
| title_full | DoNext: An Open-Access Measurement Dataset for Machine Learning-Driven 5G Mobile Network Analysis |
| title_fullStr | DoNext: An Open-Access Measurement Dataset for Machine Learning-Driven 5G Mobile Network Analysis |
| title_full_unstemmed | DoNext: An Open-Access Measurement Dataset for Machine Learning-Driven 5G Mobile Network Analysis |
| title_short | DoNext: An Open-Access Measurement Dataset for Machine Learning-Driven 5G Mobile Network Analysis |
| title_sort | donext an open access measurement dataset for machine learning driven 5g mobile network analysis |
| topic | 5G new radio 6G dataset machine learning predictive QoS multi-MNO |
| url | https://ieeexplore.ieee.org/document/10976440/ |
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