Modulation Consistency-based Contrastive Learning for Self-Supervised Automatic Modulation Classification
This work addresses the high cost of labeled data in automatic modulation classification for wireless communications, offering a self-supervised method that outperforms existing approaches in low-label scenarios.
Mod-CL introduces a self-supervised contrastive learning framework for automatic modulation classification that uses intra-instance modulation consistency as a task-aware prior, constructing positive pairs from different temporal segments of the same signal. It achieves substantial improvements in linear probing accuracy over strong baselines on RadioML datasets, especially in low-label regimes.
Deep learning-based AMC methods have achieved remarkable performance, but their practical deployment remains constrained by the high cost of labeled data. Although self-supervised learning (SSL) reduces the reliance on labels, existing SSL-based AMC methods often rely on task-agnostic pretext objectives misaligned with modulation classification, leading to representations entangled with nuisance factors such as symbol, channel, and noise. In this paper, we identify intra-instance modulation consistency as a task-aware structural prior, whereby different temporal segments of the same signal may differ in waveform while preserving the same modulation type, thus providing a principled cue for task-aligned self-supervision. Based on this prior, we propose Mod-CL, a Modulation consistency-based Contrastive Learning framework that constructs positive pairs from different temporal segments of the same signal instance, to encourage the model to learn shared modulation information while suppressing nuisance variations. We further develop a contrastive objective tailored to Mod-CL, which jointly exploits temporal segmentation and data augmentation to pull together views sharing the same modulation semantics while avoiding supervisory conflicts within each signal instance. Extensive experiments on RadioML datasets show that Mod-CL consistently outperforms strong baselines, especially in low-label regimes, achieving substantial improvements in linear probing accuracy.