{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T12:58:44Z","timestamp":1775134724403,"version":"3.50.1"},"reference-count":27,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T00:00:00Z","timestamp":1775088000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"China National Key R&D Program","award":["2022YFB3303700"],"award-info":[{"award-number":["2022YFB3303700"]}]},{"name":"UPT-China Unicom Joint Innovation Center"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Digital Twin (DT) technology, which enables the simulation, evaluation, and optimization of physical entities through synchronized digital replicas, has attracted increasing attention in the context of wireless networks. Among the various components involved, the radio propagation environment is fundamental to communication performance, making its accurate digital replication a critical challenge. This paper focuses on constructing a high-precision radio propagation environment DT using deep learning (DL) methods. While data-driven DL has become a mainstream solution for signal propagation prediction in DTs, its performance depends heavily on the model\u2019s ability to learn intrinsic propagation patterns from data. Owing to the complex interactions between wireless signals and environmental obstacles, conventional DL models often struggle to efficiently capture implicit propagation laws solely from raw data. To address this issue, we propose a general methodology for incorporating expert knowledge of radio propagation into DL frameworks. Building upon the widely adopted encoder\u2013decoder architecture, the proposed approach explicitly integrates theoretical propagation knowledge to enhance learning efficiency and prediction accuracy. Ablation experiments demonstrate that the inclusion of expert knowledge significantly improves the performance of DL-based radio environment DTs. This work highlights the potential of knowledge\u2013data dual-driven DL as a promising direction for advancing radio propagation environment DTs.<\/jats:p>","DOI":"10.3390\/s26072199","type":"journal-article","created":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T11:51:03Z","timestamp":1775130663000},"page":"2199","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Expert Knowledge-Infused Learning for Indoor Radio Propagation Environment Digital Twins"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-5381-6778","authenticated-orcid":false,"given":"Haotian","family":"Wang","sequence":"first","affiliation":[{"name":"Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lili","family":"Xu","sequence":"additional","affiliation":[{"name":"Research Institute of China United Network Communications Co., Ltd., Beijing 100176, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Research Institute of China United Network Communications Co., Ltd., Beijing 100176, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Peng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenbo","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"13789","DOI":"10.1109\/JIOT.2021.3079510","article-title":"Digital Twin Networks: A Survey","volume":"8","author":"Wu","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1109\/MWC.002.2300564","article-title":"Wireless Network Digital Twin for 6G: Generative AI as a Key Enabler","volume":"31","author":"Tao","year":"2024","journal-title":"IEEE Wirel. 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