Paper ID: 2203.15932

Self-Contrastive Learning based Semi-Supervised Radio Modulation Classification

Dongxin Liu, Peng Wang, Tianshi Wang, Tarek Abdelzaher

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.

Submitted: Mar 29, 2022