Modulation Consistency-based Contrastive Learning for Self-Supervised Automatic Modulation Classification

ArXi:2605.11875v1 Announce Type: cross 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.