{
  "abstract": "Dynamic metasurface antennas (DMAs) have emerged as a promising technology for realizing large-scale antenna arrays in next-generation wireless networks owing to their ability to enable dense and energy-efficient antenna architectures. However, jointly optimizing digital beamforming and DMA element weights under time-varying channel conditions remains challenging due to their coupled effects on the received signal and interference powers. We formulate this problem to maximize time-averaged energy efficiency (EE) under time-varying channel conditions, subject to transmit power constraints, Lorentzian phase limitations, and minimum rate requirements. To enable long-term planning and improve sample efficiency compared with model-free reinforcement learning, we develop a world model framework that learns latent system dynamics from interactions with the environment and jointly optimizes digital beamforming and DMA element weights through imagined trajectories. Furthermore, we incorporate feasibility-preserving action mappings and safety cost signals to ensure constraint satisfaction during policy learning. Simulation results demonstrate that the proposed framework improves the time-averaged EE by approximately 20% compared with the deep Q-network baseline, while achieving higher sample efficiency and more reliable constraint satisfaction.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.35813",
  "author_total": 4,
  "authors": [
    {
      "name": "Abdolrasoul Sakhaei Gharagezlou"
    },
    {
      "name": "Mehdi Monemi"
    },
    {
      "name": "Matti Latva-aho"
    },
    {
      "name": "Mehdi Rasti"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3jrdcw6zbcjy0.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.35813",
  "fulltext_url": null,
  "id": "3jrdcw6zbcjy0",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.35813",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.IT"
    ],
    [
      "arxiv",
      "cs.SY"
    ],
    [
      "arxiv",
      "eess.SY"
    ],
    [
      "arxiv",
      "math.IT"
    ],
    [
      "openalex",
      "2208|Advanced Wireless Communication Technologies"
    ]
  ],
  "openalex": "W7223841057",
  "pdf_url": "https://arxiv.org/pdf/2610.35813",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-26",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3jrdcw6zbcjy0.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:43Z",
      "native_id": "2610.35813",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.35813"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.IT",
      "subfield": "general"
    }
  ],
  "title": "World Model for Energy Efficiency Maximization in Dynamic Metasurface Antennas",
  "title_text": "World Model for Energy Efficiency Maximization in Dynamic Metasurface Antennas",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:43Z",
  "url": "https://research.draconitic.com/paper/3jrdcw6zbcjy0",
  "venue": "arXiv",
  "versions": []
}