Self-Contrastive Learning based Semi-Supervised Radio Modulation Classification
This addresses the labeling burden for deep learning in radio modulation classification, but it appears incremental as it applies existing semi-supervised and contrastive learning techniques to a specific domain.
The paper tackles the problem of automatic modulation classification (AMC) by proposing a semi-supervised learning framework that uses self-supervised contrastive-learning pre-training on unlabeled signal data, resulting in higher performance with smaller amounts of labeled data and reducing labeling burden.
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.