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XR-RF imaging enabled by software-defined metasurfaces and machine learning: foundational vision, technologies and challenges

Liaskos Christos, Tsioliaridou Ageliki, Georgopoulos Konstantinos, Morianos Ioannis, Ioannidis Sotirios, Salem Iosif, Manessis Dionysios, Schmid Stefan, Tyrovolas Dimitrios, Tegos Sotiris A., Mekikis Prodromos-Vasileios, Diamantoulakis Panagiotis, Pitilakis Alexandros, Kantartzis, Nikolaos V, Karagiannidis, George K, Tasolamprou Anna, Tsilipakos Odysseas, Kafesaki Maria, Akyildiz, Ian Fuat, Pitsillides, Andreas, Pateraki Maria, Vakalellis Michael, Spais Ilias

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URIhttp://purl.tuc.gr/dl/dias/129703E9-CA30-4177-8BAD-4996461C283C-
Identifierhttps://doi.org/10.1109/ACCESS.2022.3219871-
Identifierhttps://ieeexplore.ieee.org/document/9940297-
Languageen-
Extent22 pagesen
TitleXR-RF imaging enabled by software-defined metasurfaces and machine learning: foundational vision, technologies and challengesen
CreatorLiaskos Christosen
CreatorTsioliaridou Agelikien
CreatorGeorgopoulos Konstantinosen
CreatorΓεωργοπουλος Κωνσταντινοςel
CreatorMorianos Ioannisen
CreatorΜοριανος Ιωαννηςel
CreatorIoannidis Sotiriosen
CreatorΙωαννιδης Σωτηριοςel
CreatorSalem Iosifen
CreatorManessis Dionysiosen
CreatorSchmid Stefanen
CreatorTyrovolas Dimitriosen
CreatorTegos Sotiris A.en
CreatorMekikis Prodromos-Vasileiosen
CreatorDiamantoulakis Panagiotisen
CreatorPitilakis Alexandrosen
CreatorKantartzis, Nikolaos Ven
CreatorKaragiannidis, George Ken
CreatorTasolamprou Annaen
CreatorTsilipakos Odysseasen
CreatorKafesaki Mariaen
CreatorAkyildiz, Ian Fuaten
CreatorPitsillides, Andreasen
CreatorPateraki Mariaen
CreatorVakalellis Michaelen
CreatorSpais Iliasen
PublisherInstitute of Electrical and Electronics Engineersen
DescriptionThis work was supported in part by the Foundation for research and technology-Hellas (FORTH) Synergy Grant WISAR; in part by the European Union's Horizon 2020 Research and Innovation Programme-Project COLLABS under Grant GA EU871518; in part by the European Research Council (ERC) (AdjustNet), 2020-2025, under Agreement 864228; in part by the European Union's Horizon 2020 Research and Innovation Programme under Agreement 957406; Bundesministerium fur Bildung und Forschung (BMBF) Project, 6G Research and Innovation Cluster (6G-RIC), 2021-2025, (ACCORDION), under Grant 871793 and Grant 101016509 (CHARITY).en
Content SummaryIn this work, we present a new approach to Extended Reality (XR), denoted as iCOPYWAVES, which seeks to offer naturally low-latency operation and cost effectiveness, overcoming the critical scalability issues faced by existing solutions. Specifically, iCOPYWAVES is enabled by emerging PWEs, a recently proposed technology in wireless communications. Empowered by intelligent metasurfaces, PWEs transform the wave propagation phenomenon into a software-defined process. To this end, we leverage PWEs to: i) create, and then ii) selectively copy the scattered RF wavefront of an object from one location in space to another, where a machine learning module, accelerated by FPGAs, translates it to visual input for an XR headset using PWE-driven, RF imaging principles (XR-RF). This makes an XR system whose operation is bounded in the physical-layer and, hence, has the prospects for minimal end-to-end latency. For the case of large distances, RF-to-fiber/fiber-to-RF is employed to provide intermediate connectivity. The paper provides a tutorial on the iCOPYWAVES system architecture and workflow. Finally, a proof-of-concept implementation via simulations is provided, demonstrating the reconstruction of challenging objects in iCOPYWAVES-produced computer graphics.en
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2023-12-14-
Date of Publication2022-
SubjectExtended/virtual/augmented realityen
SubjectSoftware-defined networkingen
SubjectWirelessen
SubjectXR-RF imagingen
SubjectMachine learningen
SubjectPropagationen
SubjectGenerative adversarial networksen
SubjectApplicationsen
Bibliographic CitationC. Liaskos, A. Tsioliaridou, K. Georgopoulos, I. Morianos, S. Ioannidis, I. Salem, D. Manessis, S. Schmid, D. Tyrovolas, S. A. Tegos, P. -V. Mekikis, P. D. Diamantoulakis, A. Pitilakis, N. V. Kantartzis, G. K. Karagiannidis, A. C. Tasolamprou, O. Tsilipakos, M. Kafesaki, I. F. Akyildiz, A. Pitsillides, M. Pateraki, M. Vakalellis and I. Spais, "XR-RF imaging enabled by software-defined metasurfaces and machine learning: foundational vision, technologies and challenges," IEEE Access, vol. 10, pp. 119841-119862, 2022, doi: 10.1109/ACCESS.2022.3219871.en

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