AI Insight
Researchers have released the Vienna 4G/5G Drive-Test Dataset, a comprehensive open dataset containing georeferenced measurements of LTE and 5G networks collected across Vienna, Austria. The dataset combines passive wideband scanner observations with active handset logs, providing both network-side and user-side perspectives of deployed radio access networks across diverse urban and suburban environments. It includes base station deployment information, high-resolution building and terrain models, and is structured to support machine learning applications in mobile network analysis, planning, and optimization.
Why it matters
This dataset addresses a critical limitation in mobile network research by providing real-world, large-scale data that can be used to develop and benchmark machine learning models for network optimization, propagation modeling, and coverage analysis. The inclusion of detailed environmental data enables more accurate predictive models that could improve mobile network deployment and performance in urban settings.
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⚠️ Preprint – Noch nicht peer-reviewed
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Abstract: Machine learning for mobile network analysis, planning, and optimization is often limited by the lack of large, comprehensive real-world datasets. This paper introduces the Vienna 4G/5G Drive-Test Dataset, a city-scale open dataset of georeferenced Long Term Evolution (LTE) and 5G New Radio (NR) measurements collected across Vienna, Austria. The dataset combines passive wideband scanner observations with active handset logs, providing complementary network-side and user-side views of deployed radio access networks. The measurements cover diverse urban and suburban settings and are aligned with time and location information to support consistent evaluation. For a representative subset of base stations (BSs), we provide inferred deployment descriptors, including estimated BS locations, sector azimuths, and antenna heights. The release further includes high-resolution building and terrain models, enabling geometry-conditioned learning and calibration of deterministic approaches such as ray tracing. To facilitate practical reuse, the data are organized into scanner, handset, estimated cell information, and city-model components, and the accompanying documentation describes the available fields and intended joins between them. The dataset enables reproducible benchmarking across environment-aware learning, propagation modeling, coverage analysis, and ray-tracing calibration workflows.