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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

Απλή Εγγραφή


URIhttp://purl.tuc.gr/dl/dias/129703E9-CA30-4177-8BAD-4996461C283C-
Αναγνωριστικόhttps://doi.org/10.1109/ACCESS.2022.3219871-
Αναγνωριστικόhttps://ieeexplore.ieee.org/document/9940297-
Γλώσσαen-
Μέγεθος22 pagesen
ΤίτλοςXR-RF imaging enabled by software-defined metasurfaces and machine learning: foundational vision, technologies and challengesen
ΔημιουργόςLiaskos Christosen
ΔημιουργόςTsioliaridou Agelikien
ΔημιουργόςGeorgopoulos Konstantinosen
ΔημιουργόςΓεωργοπουλος Κωνσταντινοςel
ΔημιουργόςMorianos Ioannisen
ΔημιουργόςΜοριανος Ιωαννηςel
ΔημιουργόςIoannidis Sotiriosen
ΔημιουργόςΙωαννιδης Σωτηριοςel
ΔημιουργόςSalem Iosifen
ΔημιουργόςManessis Dionysiosen
ΔημιουργόςSchmid Stefanen
ΔημιουργόςTyrovolas Dimitriosen
ΔημιουργόςTegos Sotiris A.en
ΔημιουργόςMekikis Prodromos-Vasileiosen
ΔημιουργόςDiamantoulakis Panagiotisen
ΔημιουργόςPitilakis Alexandrosen
ΔημιουργόςKantartzis, Nikolaos Ven
ΔημιουργόςKaragiannidis, George Ken
ΔημιουργόςTasolamprou Annaen
ΔημιουργόςTsilipakos Odysseasen
ΔημιουργόςKafesaki Mariaen
ΔημιουργόςAkyildiz, Ian Fuaten
ΔημιουργόςPitsillides, Andreasen
ΔημιουργόςPateraki Mariaen
ΔημιουργόςVakalellis Michaelen
ΔημιουργόςSpais Iliasen
ΕκδότηςInstitute of Electrical and Electronics Engineersen
ΠεριγραφήThis 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
ΠερίληψηIn 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
ΤύποςPeer-Reviewed Journal Publicationen
ΤύποςΔημοσίευση σε Περιοδικό με Κριτέςel
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2023-12-14-
Ημερομηνία Δημοσίευσης2022-
Θεματική ΚατηγορίαExtended/virtual/augmented realityen
Θεματική ΚατηγορίαSoftware-defined networkingen
Θεματική ΚατηγορίαWirelessen
Θεματική ΚατηγορίαXR-RF imagingen
Θεματική ΚατηγορίαMachine learningen
Θεματική ΚατηγορίαPropagationen
Θεματική ΚατηγορίαGenerative adversarial networksen
Θεματική ΚατηγορίαApplicationsen
Βιβλιογραφική ΑναφοράC. 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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