XR-RF Imaging Enabled by Software-Defined Metasurfaces and Machine Learning: Foundational Vision, Technologies and Challenges

Christos Liaskos, Ageliki Tsioliaridou, Konstantinos Georgopoulos, Ioannis Morianos, Sotiris Ioannidis, Iosif Salem, Dionyssios 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 PitsillidesMaria Pateraki, Michael Vakalellis, Ilias Spais

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


In 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.

Original languageEnglish
Pages (from-to)119841-119862
Number of pages22
JournalIEEE Access
Publication statusPublished - 2022
Externally publishedYes


  • Extended/virtual/augmented reality
  • XR-RF imaging
  • applications
  • generative adversarial networks
  • machine learning
  • propagation
  • software-defined networking
  • wireless

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering


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