A Radio Anomaly Detection Algorithm Based on Modified Generative Adversarial Network
A Radio Anomaly Detection Algorithm Based on Modified Generative Adversarial Network
复制标题
一种基于改进生成对抗网络的无线电异常检测算法
DOI：
10.1109/lwc.2021.3074135
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发表时间：
2021-07
影响因子：
6.3
通讯作者：
Xuanhan Zhou;Jun Xiong;Xiaochen Zhang;Xiaoran Liu;Jibo Wei
中科院分区：
文献类型：
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作者：
Xuanhan Zhou;Jun Xiong;Xiaochen Zhang;Xiaoran Liu;Jibo Wei
检测不断增加的异常信号对于有效的频谱管理至关重要。本文提出了一种基于改进生成对抗网络的无线电异常检测算法。首先，应用短时傅立叶变换（STFT）从接收信号中获得频谱图图像。然后，提出了一种新的编码器GAN（E-GAN）结构，通过将编码器网络到原始GAN重建的频谱图。结果，可以基于重构误差和重构损失来检测异常的存在。此外，重建误差也可以用来定位异常的时间-频率域。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，该算法的性能提高了10 dB。
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).