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 cons
Record details
Published: 12 May 2026
Source: arXiv
Category: Research
Topics: Safety & alignment
Retrieved: 14 July 2026
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ethics.ai (12 May 2026), “Modulation Consistency-based Contrastive Learning for Self-Supervised Automatic Modulation Classification,” evidence record 4482, https://ethics.ai/record/4482 (originally published by arXiv).
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