Towards Semi-Supervised Classification of Abnormal Spectrum Signals Based on Deep Learning

In order to cope with the heavy labor cost challenge of the manual abnormal spectrum classification and improve the effectiveness of existing machine learning schemes for spectral datasets with interference-to-signal ratios, we proposes a semi-supervised classification of abnormal spectrum signals (SSC-ASS), aimed at addressing some of the challenges in abnormal spectrum signal (ASS) classification tasks. A significant advantage of SSC-ASS is that it does not require manual labeling of every abnormal data, but instead achieves high-precision classification of ASSs using only a small number of labeled data. Furthermore, the method can to some extent avoid the introduction of erroneous information resulting from the complex and variable nature of abnormal signals, thereby improving classification accuracy. Specifically, SSC-ASS uses a memory AutoEncoder module to efficiently extract features from abnormal spectrum signals by learning from the reconstruction error. Additionally, SSC-ASS combines convolutional neural network and the $K$ -means using a DeepCluster framework to fully utilize the unlabeled data. Furthermore, SSC-ASS also utilizes pre-training, category mean memory module and replaces pseudo-labels to further improve the classification accuracy of ASSs. And we verify the classification effectiveness of SSC-ASS on synthetic spectrum datasets and real on-air spectrum dataset.