Spectrogram Data Set for Deep-Learning-Based RF Frame Detection

Jakob Wicht (), Ulf Wetzker and Vineeta Jain
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Jakob Wicht: Division Engineering of Adaptive Systems EAS, Fraunhofer Institute for Integrated Circuits, 01187 Dresden, Germany
Ulf Wetzker: Division Engineering of Adaptive Systems EAS, Fraunhofer Institute for Integrated Circuits, 01187 Dresden, Germany
Vineeta Jain: Division Engineering of Adaptive Systems EAS, Fraunhofer Institute for Integrated Circuits, 01187 Dresden, Germany

Data, 2022, vol. 7, issue 12, 1-16

Abstract: Automated spectrum analysis serves as a troubleshooting tool that helps to diagnose faults in wireless networks such as difficult signal propagation conditions and coexisting wireless networks. It provides a higher monitoring coverage while requiring less expertise compared with manual spectrum analysis. In this paper, we introduce a data set that can be used to train and evaluate deep learning models, capable of detecting frames from different wireless standards as well as interference between single frames. Since manually labeling a high variety of frames in different environments is too challenging, an artificial data generation pipeline was developed. The data set consists of 20,000 augmented signal segments, each containing a random number of different Wi-Fi and Bluetooth frames, their spectral image representations and labels that describe the position and type of frame within the spectrogram. The data set contains results of intermediate processing steps that enable the research or teaching community to create new data sets for specific requirements or to provide new interesting examination examples.

Keywords: spectrogram data set; wireless network monitoring; spectrum analysis; frame detection; object detection; deep learning (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2022
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