  This paper presents a semi-supervised learning framework that is new in being
designed for automatic modulation classification (AMC). By carefully utilizing
unlabeled signal data with a self-supervised contrastive-learning pre-training
step, our framework achieves higher performance given smaller amounts of
labeled data, thereby largely reducing the labeling burden of deep learning. We
evaluate the performance of our semi-supervised framework on a public dataset.
The evaluation results demonstrate that our semi-supervised approach
significantly outperforms supervised frameworks thereby substantially enhancing
our ability to train deep neural networks for automatic modulation
classification in a manner that leverages unlabeled data.
