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Realizing RF Wavefront Copying with RIS for Future Extended Reality Applications

Stavros Tsimpoukis, Dimitrios Tyrovolas, Sotiris Ioannidis, Ian F. Akyildiz, George K. Karagiannidis, Christos Liaskos

TL;DR

The paper tackles enabling XR-RF by enabling accurate RF wavefront copying in RIS-enabled PWEs. It introduces a two-room PWE system and a routing algorithm (getRoutes) to assemble low-deviation wavefronts, plus a statistical deviation model that compares Gamma and Rayleigh fits using MLE and KL-Divergence. The study demonstrates that RIS size and the number of receiver antennas influence replication accuracy, with the Gamma distribution consistently providing a closer fit to observed deviations. These results offer design guidance for practical XR-RF deployments and highlight how RIS granularity and array size affect fidelity and responsiveness.

Abstract

Lately a new approach to Extended Reality (XR), denoted as XR-RF, has been proposed which is realized by combining Radio Frequency (RF) Imaging and programmable wireless environments (PWEs). RF Imaging is a technique that aims to detect geometric and material features of an object through RF waves. On the other hand, the PWE focuses on the the conversion of the wireless RF propagation in a controllable, by software, entity through the utilization of Reconfigurable Intelligent Surfaces (RISs), which can have a controllable interaction with impinging RF waves. In that sense, this dynamic synergy leverages the potential of RF Imaging to detect the structure of an object through RF wavefronts and the PWE's ability to selectively replicate those RF wavefronts from one spatial location to wherever an XR-RF mobile user is presently located. Then the captured wavefront, through appropriate hardware, is mapped to the visual representation of the object through machine learning models. As a key aspect of the XR-RF's system workflow is the wavefront copying mechanism, this work introduces a new PWE configuration algorithm for XR-RF. Moreover, it is shown that the waveform replication process inevitably yields imprecision in the replication process. After statistical analysis, based on simulation results, it is shown that this imprecision can be effectively modeled by the gamma distribution.

Realizing RF Wavefront Copying with RIS for Future Extended Reality Applications

TL;DR

The paper tackles enabling XR-RF by enabling accurate RF wavefront copying in RIS-enabled PWEs. It introduces a two-room PWE system and a routing algorithm (getRoutes) to assemble low-deviation wavefronts, plus a statistical deviation model that compares Gamma and Rayleigh fits using MLE and KL-Divergence. The study demonstrates that RIS size and the number of receiver antennas influence replication accuracy, with the Gamma distribution consistently providing a closer fit to observed deviations. These results offer design guidance for practical XR-RF deployments and highlight how RIS granularity and array size affect fidelity and responsiveness.

Abstract

Lately a new approach to Extended Reality (XR), denoted as XR-RF, has been proposed which is realized by combining Radio Frequency (RF) Imaging and programmable wireless environments (PWEs). RF Imaging is a technique that aims to detect geometric and material features of an object through RF waves. On the other hand, the PWE focuses on the the conversion of the wireless RF propagation in a controllable, by software, entity through the utilization of Reconfigurable Intelligent Surfaces (RISs), which can have a controllable interaction with impinging RF waves. In that sense, this dynamic synergy leverages the potential of RF Imaging to detect the structure of an object through RF wavefronts and the PWE's ability to selectively replicate those RF wavefronts from one spatial location to wherever an XR-RF mobile user is presently located. Then the captured wavefront, through appropriate hardware, is mapped to the visual representation of the object through machine learning models. As a key aspect of the XR-RF's system workflow is the wavefront copying mechanism, this work introduces a new PWE configuration algorithm for XR-RF. Moreover, it is shown that the waveform replication process inevitably yields imprecision in the replication process. After statistical analysis, based on simulation results, it is shown that this imprecision can be effectively modeled by the gamma distribution.
Paper Structure (9 sections, 9 equations, 4 figures, 1 algorithm)

This paper contains 9 sections, 9 equations, 4 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. Preliminaries & System Model
  3. Preliminaries
  4. System Model
  5. Wavefront Copying
  6. Wavefront Routing Methodology
  7. Wavefront Deviation Modeling
  8. Simulation Results
  9. Conclusions

Figures (4)

  • Figure 1: Overview of the RF wavefront replication process within a two-room PWE.
  • Figure 2: Deviations of $\phi$ for a) $d_r=0.40$m and various $M$ values b) $M=4\times4$ and various $d_r$ values
  • Figure 3: Estimated Distribution Parameters versus $d_r$ for different $M$ values
  • Figure 4: KLD for a) Gamma Distribution b) Rayleigh Distribution