Abstract
Reliable detection of wireless standards in spectrograms is essential for spectrum monitoring, efficient utilization, and interference mitigation. Deep learning-based detectors can localize Radio Frequency (RF) transmissions in time–frequency representations, but require large annotated datasets that are costly to obtain. To develop a robust deep learning model for RF standard identification, we constructed a synthetic dataset of 30,000 spectrograms, extending the dataset generation procedure of Wicht et al. (https://fordatis.fraunhofer.de/handle/fordatis/287). While their dataset included Wi-Fi and Bluetooth signals and their collisions, in this publication we additionally incorporate 5G to better reflect modern wireless environments.
