Reconfigurable Intelligent Surface Assisted Localization Over Near-Field Beam Squint Effect

Abstract

High precision network sensing and localization is an important task in 6G wireless communications, where the millimeter-wave (mmWave) / terahertz (THz) extremely large-scale multiple-input-multiple-output (XL-MIMO) technique is expected to be deployed to further boost system capacity. However, the ever-increasing bandwidth and array aperture in mmWave/THz XL-MIMO induce the challenging near-field beam squint effect. In this paper, we propose a reconfigurable intelligent surface (RIS) assisted localization (RISAL) paradigm in near-field conditions. Specifically, the polar-domain gradient descent algorithm and multiple signal classification (MUSIC) algorithm are applied to RISAL, which is able to realize high precision localization under the near-field beam squint effect. Simulation results demonstrate the superiority of the proposed algorithm. With the proposed localization algorithm, the angle accuracy can be 1 to 2 orders of magnitude higher than existing algorithms, and centimeter-level distance accuracy can be achieved.