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.

An increase in popularity of wireless networks, mainly in industrial automation and manufacturing has escalated the need of reliable wireless networks. One subtle way of achieving reliability is early diagnosis of transmission failures and taking preventive measures against it. However, it is difficult to achieve as these failures mainly arise by challenging signal propagation conditions and interference from co-existing networks, which is hard to diagnose. This leads to Quality-of-service degradation and faulty applications which may result in system breakdown and financial distress. In this paper, we propose a novel Dynamic Time Warping (DTW) based method known as Variable Adaptive DTW (VADTW) for synchronizing spectral and protocol domains to obtain precise and complete information about collisions and co-existing links causing interference and further utilize it for troubleshooting. VADTW divides the spectral and protocol sequences in adaptive time bins and calculates variable window limits for each frame to perform synchronization. To prove the efficiency of the proposed approach, we tested it in a Wi-Fi network as a proof-of-concept. The experiments are performed with real-time data traffic captured in different scenarios. VADTW successfully synchronizes sequences even with a frame loss ratio of 50% in both cases. We also perform comparative analysis of VADTW with other DTW based approaches on the basis of performance, computational cost and execution time.

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.