The frequency spectrum is a limited and valuable resource and faces challenges due to anomalies. Hence, the detection of anomalies in the frequency spectrum is crucial for maintaining the integrity and reliability of telecommunication systems. These anomalies, which include jamming signals and interference, can disrupt communication channels and degrade system performance. This paper presents a comprehensive review of deep learning applications in spectrum anomaly detection, focusing on research conducted between 2017 and 2024. The review examines various pre-processing techniques used in spectrum anomaly detection, highlighting the widespread use of spectrogram and short-time Fourier transform (STFT), particularly in reconstruction-based methods, due to their effectiveness in capturing time-frequency information despite their computational challenges. Additionally, the study underscores the importance of selecting appropriate problem-solving approaches, such as classification, segmentation, or object detection, and tailoring models to suit specific tasks. These findings underscore the potential of deep learning-based approaches in enhancing spectrum monitoring and interference management.
Aghalari et al. (Wed,) studied this question.