XR-RF Imaging Enabled by Software-Defined Metasurfaces and Machine Learning: Foundational Vision, Technologies and Challenges
Christos Liaskos, Ageliki Tsioliaridou, Κωνσταντίνος Γεωργόπουλος, Ioannis Morianos, Sotiris Ioannidis, Iosif Salem, Dionysios Manessis, Stefan Schmid, Dimitrios Tyrovolas, Sotiris A. Tegos, Prodromos‐Vasileios Mekikis, Panagiotis D. Diamantoulakis, Alexandros Pitilakis, Nikolaos V. Kantartzis, George K. Karagiannidis, Anna C. Tasolamprou, Odysseas Tsilipakos, Maria Kafesaki, Ian F. Akyildiz, Andreas Pitsillides, Maria Pateraki, Michael Vakalellis, Ilias Spais
Abstract
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. iCOPYWAVES is enabled by emerging PWEs, a recently proposed technology in wireless communications. Empowered by intelligent (meta)surfaces, PWEs transform the wave propagation phenomenon into a software-defined process. 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 for an XR system whose operation is bounded in the physical-layer and, hence, has the prospects for minimal end-to-end latency. Over 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. A proof-of-concept implementation via simulations is provided, demonstrating the reconstruction of challenging objects in iCOPYWAVES-produced computer graphics.