{
  "id": 103,
  "url": "https://arxiv.org/abs/2607.08031v1",
  "title": "DKDNet: Dual Knowledge and Data-Driven Network for Cross-Domain Automatic Modulation Classification",
  "summary": "The dynamics of communication environments induce significant distribution shifts across domains, challenging the generalization of deep learning-based automatic modulation classification (AMC) models. While existing UDA methods alleviate this problem by aligning source and target features, they give limited consideration to modulation-specific structures that remain informative across domain conditions. In this paper, we consider signal prior knowledge, grounded in communication protocols and p",
  "authors": "Shuang Wang, Chenxu Wang, Hantong Xing, Hanlin Mo, Lirong Han, Licheng Jiao",
  "category": "research",
  "topics": "environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-09T01:14:42.000Z",
  "fetched_at": "2026-07-14T14:14:19.966Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/103",
  "original_url": "https://arxiv.org/abs/2607.08031v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}