{
  "abstract": "To meet the 6G semantic transmission requirements, this paper proposes an air-ground dual-mode semantic transmission architecture for Reconfigurable Intelligent Surface (RIS)-aided Unmanned Aerial Vehicle (UAV) networks. By dynamically assigning RIS-reflected links to users with long packets and direct UAV line-of-sight (LOS) links to users with short packets, we jointly optimize semantic symbols, RIS phase shifts, UAV three-dimensional (3D) trajectory, and transmit power to maximize the system-level semantic Quality of Experience (QoE). To efficiently solve the formulated non-convex mixed-integer optimization problem, we present an Action Space Decoupled Hybrid Deep Reinforcement Learning (ASD-HDRL) framework, which employs the Dueling Double Deep Q-Network (D3QN) for discrete semantic resource decision-making and Deep Deterministic Policy Gradient (DDPG) for continuous control optimization. Simulation results verify that the proposed ASD-HDRL achieves superior system-level semantic QoE performance.",
  "abstract_shown": true,
  "affiliations": [
    "Center for Target Cognition Information Processing Science and Technology, and Key Laboratory of Modern Measurement and Control Technology, Ministry of Education, Beijing Information Science and Technology University, Beijing, China",
    "Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Yufei Dai"
    },
    {
      "affs": [
        0
      ],
      "name": "Yuanyuan Yao"
    },
    {
      "affs": [
        1
      ],
      "name": "Sai Huang"
    },
    {
      "affs": [
        0
      ],
      "name": "Xinwei Yue"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ew0cebv0exqrv.bib",
  "cited_by": 0,
  "doi": "10.3389/fphy.2026.1988637",
  "fulltext_url": "https://www.frontiersin.org/articles/10.3389/fphy.2026.1988637/full",
  "id": "ew0cebv0exqrv",
  "issns": [
    "2296-424X"
  ],
  "landing_url": "https://doi.org/10.3389/fphy.2026.1988637",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "QC1-999"
    ],
    [
      "openalex",
      "2208|Advanced Wireless Communication Technologies"
    ]
  ],
  "openalex": "W7223675396",
  "pdf_url": "https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2026.1988637/pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-26",
  "publisher": "Frontiers Media S.A.",
  "ris": "https://research.draconitic.com/paper/ew0cebv0exqrv.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:47:20Z",
      "native_id": "5c7048b4697f4cc9a509089d330671ef",
      "source": "doaj",
      "url": "https://doi.org/10.3389/fphy.2026.1988637"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "lcc:QC1-999",
      "subfield": "general"
    }
  ],
  "title": "Resource optimization for RIS-UAV integrated air-ground cooperative semantic communication driven by deep reinforcement learning",
  "title_text": "Resource optimization for RIS-UAV integrated air-ground cooperative semantic communication driven by deep reinforcement learning",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:47:20Z",
  "url": "https://research.draconitic.com/paper/ew0cebv0exqrv",
  "venue": "Frontiers in Physics",
  "versions": []
}