Self-Contrastive Learning based Semi-Supervised Radio Modulation Classification
- Resource Type
- Conference
- Authors
- Liu, Dongxin; Wang, Peng; Wang, Tianshi; Abdelzaher, Tarek
- Source
- MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM) Military Communications Conference (MILCOM), MILCOM 2021 - 2021 IEEE. :777-782 Nov, 2021
- Subject
- Aerospace
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Fields, Waves and Electromagnetics
Robotics and Control Systems
Signal Processing and Analysis
Deep learning
Military communication
Conferences
Modulation
Training data
Semisupervised learning
Performance gain
Self-Supervised Learning
Semi-Supervised Learning
Automatic Modulation Classification
- Language
- ISSN
- 2155-7586
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.