Modulation classification with data augmentation based on a semi-supervised generative model.

Although modulation classification with deep learning has been widely explored, this is challenging when the training data is limited. In this paper, we meet this challenge by data augmentation based on a semi-supervised generative model, named semi-supervised variational auto-encoder GAN (SS-VAEGAN). The proposed model has two novel aspects: first, the ensemble prediction technique is presented to establish a semi-supervised framework, and thus few labeled data and abundant unlabeled data can be used simultaneously to train the generative model; second, a modified feature matching strategy is utilized to ensure the diversity of the generated samples and contribute to the final classification under limited labeled data. With the aim of the constellation diagram with density and the ResNet-18 network, we have worked out a complete solution of modulation classification with data augmentation using SS-VAEGAN. In the experiments, we investigate the classification accuracy and robustness of the proposed data augmentation method and then compare the proposed SS-VAEGAN with other deep generative models. It is shown that our method can achieve the classification accuracy of 0.968 in the extreme absence of training data. [ABSTRACT FROM AUTHOR]