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.
@article{arxiv.2203.15932,
title = {Self-Contrastive Learning based Semi-Supervised Radio Modulation Classification},
author = {Dongxin Liu and Peng Wang and Tianshi Wang and Tarek Abdelzaher},
journal= {arXiv preprint arXiv:2203.15932},
year = {2022}
}