Spectrum Anomaly Detection Using Deep Neural Networks: A Wireless Signal Perspective

In modern society, LTE and 5G communications serve as essential infrastructures, making the stability of communication networks a critical challenge. However, advancements in wireless communication devices and technologies can lead to the transmission of illegal signals, device malfunctions, and radio interference, potentially causing degradation of communication performance. This study proposes a wideband spectrum monitoring method for LTE and 5G communication bands used in cellular networks by utilizing object detection techniques, thereby ensuring stable network performance and security. The proposed network accounts for the temporal and frequency characteristics present in signals on the spectrogram and is designed to exhibit high detection performance even for low-power signals. It achieved an mean Average Precision(mAP)50 performance of 80% at an Interference Signal Ratio(ISR) of –10dB corresponding to low power and an overall mAP50 performance of 93%.