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Leveraging Large Reconfigurable Intelligent Surfaces as Anchors for Near-Field Positioning

Zeyu Huang, Markus Rupp, Stefan Schwarz

TL;DR

This work introduces an anchor-based positioning paradigm using large reconfigurable intelligent surfaces (XL-RIS) to enable near-field localization without relying on RIS-channel estimation. By estimating per-RIS propagation delays and Doppler shifts, it constructs per-RIS anchor lines and fuses their information via linear least-squares to recover the UE position, while explicitly accounting for the uncertainty from RIS size. The authors derive a Cramér-Rao bound for the proposed scheme and analyze how RIS configuration, quantity, and placement influence the localization accuracy. The results demonstrate feasibility and reveal trade-offs in RIS design and placement, with future work aimed at more realistic scenarios and advanced estimators to bridge the gap between theory and practice.

Abstract

In this work, we present a recent investigation on leveraging large reconfigurable intelligent surfaces (RIS) as anchors for positioning in wireless communication systems. Unlike existing approaches, we explicitly address the uncertainty arising from the substantial physical size of the RIS, particularly relevant when a user equipment resides in the near field, and propose a method that ensures accurate positioning under these conditions. We derive the corresponding Cramer-Rao bound for our scheme and validate the effectiveness of our scheme through numerical experiments, highlighting both the feasibility and potential of our approach.

Leveraging Large Reconfigurable Intelligent Surfaces as Anchors for Near-Field Positioning

TL;DR

This work introduces an anchor-based positioning paradigm using large reconfigurable intelligent surfaces (XL-RIS) to enable near-field localization without relying on RIS-channel estimation. By estimating per-RIS propagation delays and Doppler shifts, it constructs per-RIS anchor lines and fuses their information via linear least-squares to recover the UE position, while explicitly accounting for the uncertainty from RIS size. The authors derive a Cramér-Rao bound for the proposed scheme and analyze how RIS configuration, quantity, and placement influence the localization accuracy. The results demonstrate feasibility and reveal trade-offs in RIS design and placement, with future work aimed at more realistic scenarios and advanced estimators to bridge the gap between theory and practice.

Abstract

In this work, we present a recent investigation on leveraging large reconfigurable intelligent surfaces (RIS) as anchors for positioning in wireless communication systems. Unlike existing approaches, we explicitly address the uncertainty arising from the substantial physical size of the RIS, particularly relevant when a user equipment resides in the near field, and propose a method that ensures accurate positioning under these conditions. We derive the corresponding Cramer-Rao bound for our scheme and validate the effectiveness of our scheme through numerical experiments, highlighting both the feasibility and potential of our approach.
Paper Structure (15 sections, 24 equations, 2 figures)

This paper contains 15 sections, 24 equations, 2 figures.

Table of Contents

  1. Introduction
  2. System Model
  3. System Overview
  4. Signal Model
  5. Positioning Algorithm
  6. Estimation of propagation parameters
  7. Position information from a single RIS
  8. Position information from multiple RIS
  9. Position estimation
  10. Performance Bound Analysis
  11. Numerical Experiment
  12. The impact of configuration of RISs
  13. The impact of number of RISs
  14. The impact of locations of RISs
  15. Conclusion and Outlook

Figures (2)

  • Figure 1: Multiple RISs-assisted down-link positioning system.
  • Figure 2: Simulated CRB of $p_{\text{y}}$ under different conditions. In \ref{['fig2', 'fig3']} all the RISs are used for positioning. In \ref{['fig4', 'fig5', 'fig6', 'fig7', 'fig8', 'fig9']} only two RISs are used. Different system configurations can significantly affect the CRB for various sample positions.