Self-Contrastive Learning based Semi-Supervised Radio Modulation Classification

• URL: http://arxiv.org/abs/2203.15932v1
• Date: Tue, 29 Mar 2022 22:21:14 GMT
• Title: Self-Contrastive Learning based Semi-Supervised Radio Modulation Classification
• Authors: Dongxin Liu, Peng Wang, Tianshi Wang, and Tarek Abdelzaher
• Abstract summary: This paper presents a semi-supervised learning framework 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. We evaluate the performance of our semi-supervised framework on a public dataset.
• Score: 6.089994098441994
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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