Wireless Anomaly Signal Dataset (WASD): An Open Dataset for Wireless Cellular Spectrum Monitoring and Anomaly Detection

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.