A Radio Anomaly Detection Algorithm Based on Modified Generative Adversarial Network
Xuanhan Zhou, Jun Xiong, Xiaochen Zhang, Xiaoran Liu, Jibo Wei
Abstract
Detecting ever increasing anomalous signals is critical to effective spectrum management. In this letter, we present a radio anomaly detection algorithm based on modified generative adversarial network (GAN). Firstly, short time fourier transform (STFT) is applied to obtain the spectrogram image from the received signal. Then, a novel encoder-GAN (E-GAN) structure is proposed by incorporating an encoder network into the original GAN to reconstruct the spectrogram. As a result, the existence of anomalies can be detected based on the reconstruction error and discriminator loss. In addition, the reconstruction error can also be exploited to locate the anomalies in time-frequency domain. Simulation results show that the proposed algorithm brings a performance improvement of up to 10 dB compared with the spectrum anomaly detector with interpretable features (SAIFE).