Semi-Supervised Learning For Signal Recognition With Sparsity And Robust Promotion

Due to the emergence of deep learning, signal recognition has made great strides in performance improvement. The success of most deep learning methods relies on the accessibility of abundant labelled training data. However, the annotation of signals is quite expensive, making it challenging to train deep learning models substantially. This calls for the development of semi-supervised learning (SSL) method to fully utilize the unlabelled data to assist the training of deep learning models. To achieve this goal, three types of loss function tailored to the task of signal recognition are carefully designed in this paper. Together with the novel design of neural network structure, the proposed SSL method can effectively extract the information from unlabelled training data and thus overcome the difficulty of insufficient training. Extensive numerical results using real-world signal datasets are presented to show the remarkable performance of the proposed SSL method.