Data-Limited Modulation Classification With a CVAE-Enhanced Learning Model

Abstract

The modulation classification problem can be effectively handled by modern machine learning techniques, however, the available data for training is usually limited due to costly measurements in practice. Concerning the problem of insufficient training data, we propose a conditional variational auto-encoder (CVAE)-enhanced learning model for modulation classification. Different from the conventional data augmentation approaches where the data is independently generated, we introduce a feedback unit from the classifier to the CVAE generative network, to ensure that the generated data helps improving the classification accuracy. A two-stage generating-training algorithm is proposed. Via both simulation and practical implementation on a universal software radio peripheral (USRP) platform, it is shown that the proposed method effectively improves the classification accuracy in realistic propagation environment.