XR-RF imaging enabled by software-defined metasurfaces and machine learning: foundational vision, technologies and challengesXR-RF imaging enabled by software-defined metasurfaces and machine learning: foundational vision, technologies and challenges Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2023-12-142022enIn 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.This 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).http://creativecommons.org/licenses/by/4.0/IEEE Access10119841-119862Liaskos_et_al_IEEE Access_10_2022.pdfChania [Greece]Library of TUC2023-12-14application/pdf4.2 MBfree Liaskos Christos Tsioliaridou Ageliki Georgopoulos Konstantinos Γεωργοπουλος Κωνσταντινος Morianos Ioannis Μοριανος Ιωαννης Ioannidis Sotirios Ιωαννιδης Σωτηριος 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 Institute of Electrical and Electronics Engineers Extended/virtual/augmented reality Software-defined networking Wireless XR-RF imaging Machine learning Propagation Generative adversarial networks Applications