Automatic Modulation Classification (AMC) is a critical task in industrial cognitive communication systems. Existing state-of-the-art methods, typified by real-valued convolutional neural networks, have introduced innovative solutions for AMC. However, such models viewed the two constituent components of complex-valued modulated signals as discrete real-valued inputs, causing structural phase damage to original signals and reduced interpretability of the model. In this article, a novel end-to-end AMC model called a complex-valued depth-wise separable convolutional neural network (CDSCNN) is proposed, which adopts complex-valued operation units to enable automatic complex-valued feature learning specifically tailored for AMC. Considering the limited hardware resources available in industrial scenarios, complex-valued depth-wise separable convolution (CDSC) is designed to strike a balance between classification accuracy and model complexity. With an overall accuracy of 62.63% on the RadioML2016.10a dataset, CDSCNN outperforms its counterparts by 1% to 11%. After fine-tuning on the RadioML2016.10b dataset, the overall accuracy reaches 63.15%, demonstrating the robust recognition and generalization capability of CDSCNN. Moreover, the CDSCNN exhibits lower model complexity compared to other methods.

Recently, contrastive learning (CL) has exhibited considerable advantages for automatic modulation classification (AMC) with a scarcity of labeled samples. Nevertheless, the majority of the available CL-based AMC methods use the simple signal augmentation strategy and suffer from interference from false negatives. To explore the more generalizable global temporal semantics within signals, a novel masked contrastive learning with hard negatives (MCLHN) method is proposed in this paper. MCLHN first strategically incorporates semantic-preserving data augmentation, ensuring the diversity and semantic invariance of signals. Second, MCLHN adopts an encoder with temporal masking to enable robust temporal modeling. Moreover, a debiased hardness-weighted contrastive (DHWC) loss is designed to balance the adverse impact of the debiased strategy and the advantage of hard negatives. Extensive experiments are conducted on several benchmark datasets, and the experimental results demonstrate the superior performance and generalization capability of MCLHN to other methods. Significantly, the performance of MCLHN with only one labeled sample per modulation under each signal-to-noise ratio (SNR) rivals that of other methods with five to twenty times the number of labeled samples.