Article
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Preserved in Portico This version is not peer-reviewed
Spectrogram Data Set for Deep Learning Based RF-Frame Detection
Version 1
: Received: 30 September 2022 / Approved: 4 October 2022 / Online: 4 October 2022 (09:56:48 CEST)
A peer-reviewed article of this Preprint also exists.
Wicht, J.; Wetzker, U.; Jain, V. Spectrogram Data Set for Deep-Learning-Based RF Frame Detection. Data 2022, 7, 168. Wicht, J.; Wetzker, U.; Jain, V. Spectrogram Data Set for Deep-Learning-Based RF Frame Detection. Data 2022, 7, 168.
Abstract
Automated spectrum analysis serves as a troubleshooting tool that helps to diagnose faults in wireless networks like difficult signal propagation conditions and coexisting wireless networks. It provides a higher monitoring coverage while requiring less expertise compared to 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 labelling a high variety of frames in different environments is too challenging, an artificial data generation pipeline has been 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 enables 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
Subject
Engineering, Electrical and Electronic Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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