Near-field 5D Pose Estimation using Reconfigurable Intelligent Surfaces

TL;DR

This paper introduces a low-complexity method for estimating the 5D pose of user equipment using reconfigurable intelligent surfaces in near-field MIMO systems, addressing limitations of traditional localization techniques.

Contribution

It proposes a novel decoupling approach leveraging RIS and UE array symmetry, enabling closed-form solutions for 5D pose estimation in near-field scenarios.

Findings

Decouples 5D pose into five 1D problems

Uses ESPRIT-inspired approach for solutions

Achieves low-complexity, closed-form estimations

Abstract

The advent of 6G is expected to enable many use cases which may rely on accurate knowledge of the location and orientation of user equipment (UE). The conventional localization methods suffer from limitations such as synchronization and high power consumption required for multiple active anchors. This can be mitigated by utilizing a large dimensional passive reconfigurable intelligent surface (RIS). This paper presents a novel low-complexity approach for the estimation of 5D pose (i.e. 3D location and 2D orientation) of a UE in near-field RIS-assisted multiple-input multiple-output (MIMO) systems. The proposed approach exploits the symmetric arrangement of uniform planar array of RIS and uniform linear array of UE to decouple the 5D problem into five 1D sub-problems. Further, we solve these sub-problems using a total least squares ESPRIT inspired approach to obtain closed-form solutions.