MCLHN: Toward Automatic Modulation Classification via Masked Contrastive Learning With Hard Negatives

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.