Modulation Consistency-based Contrastive Learning for Self-Supervised Automatic Modulation Classification
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 fo...
| Authors | Chenxu Wang, Shuang Wang, Lirong Han et al. |
|---|---|
| Primary category | eess.SP |
| Published | 12 May 2026 |
| Last updated at source | 12 May 2026 |
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