{
  "id": 4482,
  "url": "https://arxiv.org/abs/2605.11875v1",
  "title": "Modulation Consistency-based Contrastive Learning for Self-Supervised Automatic Modulation Classification",
  "summary": "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",
  "authors": "Chenxu Wang, Shuang Wang, Lirong Han, Xinyu Hu, Hanlin Mo, Hantong Xing et al.",
  "category": "research",
  "topics": "safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-12T09:52:23.000Z",
  "fetched_at": "2026-07-14T16:31:03.577Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/4482",
  "original_url": "https://arxiv.org/abs/2605.11875v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}