Journal Article on DoNext, an Open-Access Measurement Dataset

We are pleased to announce the publication of our latest research in the prestigious IEEE Transactions on Machine Learning in Communications and Networking journal. The article, titled "DoNext: An Open-Access Measurement Dataset for Machine Learning-Driven 5G Mobile Network Analysis," presents DoNext — the most extensive openly available dataset of 4G and 5G mobile network measurements to date.

Collected over two years in Dortmund, Germany, the DoNext dataset combines active and passive mobile network measurements across diverse urban, suburban, and rural environments. This large-scale dataset enables the development and evaluation of machine learning (ML) models without the need for costly measurement campaigns.

We demonstrate the application of ML techniques to predict key performance indicators (KPIs) such as data rate and latency, supporting next-generation mobile use cases like teleoperation and intelligent transportation systems. Methods such as Random Forests, XGBoost, and Convolutional Neural Networks were employed to showcase predictive quality of service and radio propagation modeling.

The open-access dataset and tools are designed to foster further research and innovation in mobile network analysis and planning. More information and access to the dataset and article are available below.

H. Schippers, M. Geis, S. Böcker, C. Wietfeld, "DoNext: An Open-Access Measurement Dataset for Machine Learning-Driven 5G Mobile Network Analysis," IEEE Transactions on Machine Learning in Communications and Networking, April 2025. DOI: 10.1109/TMLCN.2025.3564239. (Early access.)  [IEEE Xplore] [Details]

>> DoNext Dataset <<

This work has been supported by the German Federal Ministry for Digital and Transport (BMDV) in the context of the project Virtual Integration of decentralized charging infrastructure in cab stands under the funding reference 16DKVM006B, the Federal Ministry of Education and Research (BMBF) in the course of the 6GEM Research Hub under grant number 16KISK038, and by the Ministry of Economic Affairs, Industry, Climate Action and Energy of the state of North Rhine–Westphalia (MWIKE NRW) along with the Competence Center 5G.NRW under grant number 005–01903–0047.