SSCL-AMC: A Self-supervised Automatic Modulation Classification Method via Dynamic Augmentation and Ensemble Learning

Abstract

Deep learning has demonstrated promising results over traditional hand-crafted methods for automatic modulation classification (AMC), which plays a critical role as an intermediate step between signal detection and modulation. However, acquiring large-scale labeled data remains challenging, as both data quality and annotation costs are critical factors in achieving accurate and efficient training. In this paper, we propose a novel self-supervised contrastive learning (SSCL) with gradient-adversarial-based data augmentation (GADA) approach for AMC. Additionally, a meticulous encoder based on Transformer-LSTM architectures is employed to pre-train a feature extractor using unlabeled base classes. Subsequently, knowledge transfer is employed to fine-tune the feature extractor, and ensemble learning is introduced to efficiently leverage multiple classifiers for joint decision-making. Experiments demonstrate that we achieve up to 91.87% accuracy on the challenging large-scale RadioML2018.10a dataset, demonstrating performance competitive with state-of-the-art supervised implementations.