Adversarial Autoencoder Empowered Joint Anomaly Detection and Signal Reconstruction From Sub-Nyquist Samples
Han Zhang, Zihang Song, Jian Yang, Yue Gao
Abstract
Anomaly detection is an essential part of spectrum monitoring applications. Malicious users and malfunctioning nodes could be identified via anomaly detection methods. Meanwhile, the spectrum bands that would be utilized in future 6G or satellite communication system settings are going to be wider than ever. Acquiring Nyquist sampled data from such a spectrum would require components with a very high sampling rate. To monitor a wide spectrum, a compressive sensing recovery algorithm combined with a sub-sampling approach could accomplish the task with a lower hardware cost. To solve the anomaly detection problem using a sub-sampled data stream, a joint signal recovery and anomaly detection solution utilizing an adversarial autoencoder (AAE) structure are proposed in this article. An AAE is constructed via an autoencoder and a discriminator weaved together. The discriminator would guide the autoencoder to drive its extracted feature onto a designed feature space, while the autoencoder would provide a reconstruction of the original Nyquist sampled signal. The proposed AAE structure could learn the distribution of the signal from either labelled or unlabelled training data, enabling it to work on both supervised and unsupervised data sets. The proposed AAE has shown superior reconstruction and detection performance on very sparse sampling scenarios.