Self-Supervised Aligned Data Augmentation Network for Imbalanced Modulation Classification

Automatic modulation classification (AMC) plays a pivotal role in radar and communication systems. Traditional AMC methods assume an equal number of samples for each modulation during training. However, in real-world scenarios, the number of samples collected for different modulations can vary significantly. This disparity leads the model to overfit to majority classes while underrepresenting minority classes in the optimization process, ultimately leading to degraded classification performance. To tackle this issue, this paper presents a Self-supervised Aligned Data Augmentation Network (SADA-Net) for imbalanced AMC. We leverage Data Augmentation (DA) not only to balance data distribution but also increase data diversity, boosting the model’s robustness in handling minority classes. To amplify the effectiveness of DA, a self-supervised aligned module is incorporated to maintain semantic consistency, preventing the model from focusing on irrelevant variations. Furthermore, an adaptive fusion learning strategy is proposed to dynamically adjust the focus between majority classes and minority classes during training process. This progressive training strategy avoids damaging the learned universal features from majority classes when emphasizing the minority data, ensuring a balanced feature learning process. Comprehensive experiments on simulated, publicly available and real-world datasets demonstrate the effectiveness and generalization of SADA-Net under various class-imbalanced conditions.