With the rapid development of wireless communication, spectrum plays increasingly important role in both military and civilian fields. Spectrum anomaly detection aims at detecting emerging anomaly signals and spectrum usage behavior in the environment, which is indispensable to secure safety and improve spectrum efficiency. However, spectrum anomaly detection faces many difficulties, especially for unauthorized frequency bands. In unauthorized bands, the composition of spectrum is complex and the anomaly usage patterns are unknown in prior. In this paper, a Variational Autoencoder- (VAE-) based method is proposed for spectrum anomaly detection in unauthorized bands. First of all, we theoretically prove that the anomalies in unauthorized bands will introduce Background Noise Enhancement (BNE) effect and Anomaly Signal Disappearance (ASD) effects after VAE reconstruction. Then, we introduce a novel anomaly metric termed as percentile (PER) score, which focuses on capturing the distribution variation of reconstruction error caused by ASD and BNE. In order to verify the effectiveness of our method, we developed an ISM Anomaly Detection (IAD) dataset. The proposed PER score achieves superior performance against different type of anomalies. For QPSK type anomaly, our method increases the recall rate from 80% to 93% while keeping a false alarm rate of 5%. The proposed method is beneficial to broadband spectrum sensing and massive spectrum data processing. The code will be released at git@github.com:QXSLAB/vae_ism_ano.git.

For the space-based remote sensing system, onboard intelligent processing based on deep learning has become an inevitable trend. To adapt to the dynamic changes of the observation scenes, there is an urgent need to perform distributed deep learning onboard to fully utilize the plentiful real-time sensing data of multiple satellites from a smart constellation. However, the network bandwidth of the smart constellation is very limited. Therefore, it is of great significance to carry out distributed training research in a low-bandwidth environment. This paper proposes a Randomized Decentralized Parallel Stochastic Gradient Descent (RD-PSGD) method for distributed training in a low-bandwidth network. To reduce the communication cost, each node in RD-PSGD just randomly transfers part of the information of the local intelligent model to its neighborhood. We further speed up the algorithm by optimizing the programming of random index generation and parameter extraction. For the first time, we theoretically analyze the convergence property of the proposed RD-PSGD and validate the advantage of this method by simulation experiments on various distributed training tasks for image classification on different benchmark datasets and deep learning network architectures. The results show that RD-PSGD can effectively save the time and bandwidth cost of distributed training and reduce the complexity of parameter selection compared with the TopK-based method. The method proposed in this paper provides a new perspective for the study of onboard intelligent processing, especially for online learning on a smart satellite constellation.