1. XR-RF Imaging Enabled by Software-Defined Metasurfaces and Machine Learning: Foundational Vision, Technologies and Challenges
- Author
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Liaskos, C., Tsioliaridou, A., Georgopoulos, K., Morianos, G., Ioannidis, S., Salem, I., Manessis, D., Tyrovolas, S. Schmid D., Tegos, S. A., Mekikis, P. -V., Diamantoulakis, P. D., Pitilakis, A., Kantartzis, N., Tasolamprou, G. K. Karagiannidis A., Tsilipakos, O., Kafesaki, M., Akyildiz, I. F., Pitsillides, A., Pateraki, M., Vakalellis, M., and Spais, I.
- Subjects
Electrical Engineering and Systems Science - Signal Processing ,Computer Science - Emerging Technologies ,Electrical Engineering and Systems Science - Image and Video Processing - 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 PWEdriven, 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.
- Published
- 2022
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