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