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...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Transactions on Machine Learning in Communications and Networking |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10976440/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|---|
| ISSN: | 2831-316X |