Anomaly Detection for Wireless Cellular Communication Based on Synthetic Anomaly

With the growing demand for wireless communication services, efficient anomaly detection in the electromagnetic spectrum is crucial to ensure service quality and prevent illegal spectrum usage. This study introduces a novel Synthetic Anomaly Detection (SAD)-based method tailored for spectrum anomaly detection. By leveraging synthetic anomaly data generation that captures the unique characteristics of spectrum signals, the proposed method addresses the limitations of conventional approaches, such as reconstruction errors and inadequate representation of spectrum anomalies. A rectangular mask and Gaussian noise were employed to simulate anomalies like tones, chirps, and pulses commonly found in LTE and 5G bands. The method achieved AUROC scores of 90% (image-level) and 82% (pixel-level) under ISR 0 dB, outperforming traditional unsupervised learning-based detection methods. An ablation study demonstrated the effectiveness of the synthetic anomaly generation process, highlighting its contribution to enhanced detection performance. These results validate the practicality and robustness of the proposed approach, providing a solid framework for spectrum anomaly detection in dynamic electromagnetic environments.