AI-Driven Wireless Network Anomaly Detection Dataset
Published: 21 March 2025| Version 1 | DOI: 10.17632/p4n85smvms.1
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Description
This dataset is designed for machine learning-based anomaly detection in wireless communication networks. It contains channel measurement data collected from different propagation environments, including Rural Macro (RMa) and Urban Macro (UMa) scenarios with Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) conditions. Source & Collection Process Based on 3GPP TR 38.901 standard channel models. Simulated using QuaDRiGa and real-world propagation conditions. Feature extraction performed using Space-Alternating Generalized Expectation-Maximization (SAGE).
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Institutions
- Arab Academy for Science Technology and Maritime Transport
Categories
Wireless Communication