How to Bridge the Sim-to-Real Gap in Digital Twin-Aided Telecommunication Networks

[2507.07067] How to Bridge the Sim-to-Real Gap in Digital Twin-Aided Telecommunication Networks Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Electrical Engineering and Systems Science > Signal Processing arXiv:2507.07067 (eess) [Submitted on 9 Jul 2025 ( v1 ), last revised 27 May 2026 (this version, v4)] Title: How to Bridge the Sim-to-Real Gap in Digital Twin-Aided Telecommunication Networks Authors: Clement Ruah , Houssem Sifaou , Osvaldo Simeone , Bashir M. Al-Hashimi View a PDF of the paper titled How to Bridge the Sim-to-Real Gap in Digital Twin-Aided Telecommunication Networks, by Clement Ruah and 3 other authors View PDF HTML (experimental) Abstract: Training effective artificial intelligence models for telecommunications is challenging due to the scarcity of deployment-specific data. Real data collection is expensive, and available datasets often fail to capture the unique operational conditions and contextual variability of the network environment. Digital twinning provides a potential solution to this problem, as simulators tailored to the current network deployment can generate site-specific data to augment the available training datasets. However, there is a need to develop solutions to bridge the inherent simulation-to-reality (sim-to-real) gap between synthetic and real-world data. This paper reviews recent advances on two complementary strategies: 1) the calibration of digital twins (DTs) through real-world measurements, and 2) the use of sim-to-real gap-aware training strategies to robustly handle residual discrepancies between digital twin-generated and real data. For the latter, we evaluate two conceptually distinct methods that model the sim-to-real gap either at the level of the environment via Bayesian learning or at the level of the training loss via prediction-powered inference. Comments: This work has been accepted for publication in IEEE Communications Magazine Subjects: Signal Processing (eess.SP) ; Machine Learning (cs.LG) Cite as: arXiv:2507.07067 [eess.SP] (or arXiv:2507.07067v4 [eess.SP] for this version) https://doi.org/10.48550/arXiv.2507.07067 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Clement Ruah [ view email ] [v1] Wed, 9 Jul 2025 17:27:51 UTC (2,652 KB) [v2] Mon, 1 Dec 2025 16:04:07 UTC (2,671 KB) [v3] Sat, 11 Apr 2026 18:07:54 UTC (2,651 KB) [v4] Wed, 27 May 2026 17:03:39 UTC (2,650 KB) Full-text links: Access Paper: View a PDF of the paper titled How to Bridge the Sim-to-Real Gap in Digital Twin-Aided Telecommunication Networks, by Clement Ruah and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: eess.SP < prev | next > new | recent | 2025-07 Change to browse by: cs cs.LG eess References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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