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).

中文翻译：

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一种基于改进生成对抗网络的无线电异常检测算法


检测不断增加的异常信号对于有效的频谱管理至关重要。在这封信中，我们提出了一种基于改进的生成对抗网络（GAN）的无线电异常检测算法。首先，应用短时傅里叶变换（STFT）从接收信号中获得频谱图图像。然后，通过将编码器网络合并到原始 GAN 中来重建频谱图，提出了一种新颖的编码器-GAN（E-GAN）结构。因此，可以根据重建误差和鉴别器损失来检测异常的存在。此外，还可以利用重构误差来定位时频域的异常。仿真结果表明，与具有可解释特征的频谱异常检测器（SAIFE）相比，该算法带来了高达10 dB的性能提升。