Wireless Anomaly Signal Dataset (WASD): An Open Dataset for Wireless Cellular Spectrum Monitoring and Anomaly Detection
Year: 2024
Type: article
Abstract: Illegal and jamming signals disrupt wireless communication, causing degraded quality, false detection, and malfunctions. Spectrum monitoring is crucial across various fields like communications, broadcasting, radar, military, and security to detect these anomalies. Traditional detection algorithms struggle in diverse environments due to their parameter-dependent nature. Deep learning offers a promising solution with its ability to learn from large datasets and adapt to varying conditions. Â However, existing wireless datasets for deep learning are often proprietary or limited to narrowband single-signal modulation classification, unsuitable for wideband multi-signal spectrum monitoring. This paper introduces a novel spectrum monitoring dataset designed for object detection tasks. This dataset utilizes licensed frequency bands and employs Short Time Fourier Transform (STFT) spectrograms to capture both time and frequency characteristics. By annotating generated anomalous signals within these spectrograms, the dataset enables effective training of object detection models for identifying illegal and jamming signals in wideband spectrum monitoring applications.
Source: IEEE Access
Authors: Jinha Kim, Hyeongwoo Kim, Byungkwan Kim
Institution: Chungnam National University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,995
Award: G3129561886
OpenAlex ID: W4405778536