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

2025-05-19

AI Summary

Large reconfigurable intelligent surfaces (RISs) deployed as near-field localization anchors suffer from degraded positioning accuracy due to modeling uncertainty induced by their physical dimensions. Method: This work explicitly models the RIS’s geometric size as a near-field position error source—the first such treatment—and proposes a high-accuracy joint phase–position estimation framework tailored to near-field electromagnetic propagation characteristics, with its Cramér–Rao bound (CRB) rigorously derived. Contribution/Results: By abandoning the conventional far-field assumption and leveraging precise near-field electromagnetic modeling coupled with joint parameter optimization, the proposed method achieves significantly lower localization error than far-field approaches in the near-field region, attaining the theoretical CRB both analytically and via numerical simulations. This work establishes the first systematic modeling, analysis, and validation paradigm for enabling high-precision near-field localization using large-scale RISs.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Search and Optimization: Local Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Using large RIS as anchors for near-field positioning
• Addressing uncertainty from RIS size in near-field conditions
• Ensuring accurate positioning with derived Cramer-Rao bound validation

Innovation

• Uses large reconfigurable intelligent surfaces (RIS)
• Addresses near-field positioning uncertainty effectively
• Validates with Cramer-Rao bound and numerical experiments