Intelligent Sensing and Identification of Spectrum Anomalies With Alpha-Stable Noise

Abstract

As the electromagnetic environment becomes more complex, a significant number of interferences and malfunctions of authorized equipment can result in anomalies in spectrum usage. Utilizing intelligent spectrum technology to sense and identify anomalies in the electromagnetic space is of great significance for the efficient use of the electromagnetic space. In this paper, a method for intelligent sensing and identification of anomalies in spectrum with alpha-stable noise is proposed. First, we use a delayed feedback network (DFN) to suppress alpha-stable noise. Then, we use a long short-term memory (LSTM) autoencoder-based attention mechanism to sense anomaly. Finally, we use the deep forest model to identify abnormal spectrum. Simulation results demonstrate that the proposed method effectively suppresses alpha-stable noise, and it outperforms existing methods in abnormal spectrum sensing and identification.