IEEE Wireless Communications Letters | 2021

A Radio Anomaly Detection Algorithm Based on Modified Generative Adversarial Network

 
 
 
 
 

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).

Volume 10
Pages 1552-1556
DOI 10.1109/LWC.2021.3074135
Language English
Journal IEEE Wireless Communications Letters

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