{
  "id": 356,
  "url": "https://arxiv.org/abs/2607.00860v1",
  "title": "Meta-Transfer Learning for mmWave Beam Alignment",
  "summary": "Millimeter-wave (mmWave) beam alignment plays a critical role in next-generation wireless systems, yet its efficient implementation remains challenging. Meta-learning and transfer learning have been explored to enable deep learning-based beam prediction models to rapidly adapt to unseen environments; however, existing meta-learning approaches adapt the entire network and are trained from random initialization, leading to a large number of updated parameters and a high meta-training cost, while t",
  "authors": "Ahmet Nuri Cevik, Sinem Coleri",
  "category": "research",
  "topics": "safety-alignment,environment",
  "orgs": "meta",
  "regions": null,
  "published_at": "2026-07-01T12:24:48.000Z",
  "fetched_at": "2026-07-14T14:14:28.437Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/356",
  "original_url": "https://arxiv.org/abs/2607.00860v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}