Spectrogram Data Set for Deep Learning Based RF-Frame Detection

Abstract

Automated spectrum analysis serves as a troubleshooting tool that helps to diagnose faults in wireless networks like difficult signal propagation conditions as well as coexisting wireless networks. It provides a higher monitoring coverage while requiring less expertise compared to manual spectrum analysis. In this publication, we introduce a data set that can be used to train and evaluate a deep learning model, capable to detect frames of 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 20000 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 dataset contains results of intermediate processing steps that enables the research or teaching community to create new datasets for specific requirements or to provide new interesting examination examples.