Hybrid Near-field and Far-field Localization with Holographic MIMO

TL;DR

This paper introduces a hybrid localization method using holographic MIMO and reconfigurable intelligent surfaces to accurately locate users in both near-field and far-field regions, addressing the challenge of unknown user regions.

Contribution

It presents a novel RIS-enabled localization approach that jointly estimates user positions in NF and FF regions and optimizes RIS phase shifts considering channel coupling effects.

Findings

The proposed method outperforms pure NF and FF localization techniques.

Derived localization error bounds considering channel coupling.

Simulation results demonstrate significant performance gains.

Abstract

Due to its ability to precisely control wireless beams, holographic multiple-input multiple-output (HMIMO) is expected to be a promising solution to achieve high-accuracy localization. However, as the scale of HMIMO increases to improve beam control capability, the corresponding near-field (NF) region expands, indicating that users may exist in both NF and far-field (FF) regions with different electromagnetic transmission characteristics. As a result, existing methods for pure NF or FF localization are no longer applicable. We consider a hybrid NF and FF localization scenario in this paper, where a base station (BS) locates multiple users in both NF and FF regions with the aid of a reconfigurable intelligent surface (RIS), which is a low-cost implementation of HMIMO. In such a scenario, it is difficult to locate the users and optimize the RIS phase shifts because whether the location of the user is in the NF or FF region is unknown, and the channels of different users are coupled. To tackle this challenge, we propose a RIS-enabled localization method that searches the users in both NF and FF regions and tackles the coupling issue by jointly estimating all user locations. We derive the localization error bound by considering the channel coupling and propose an RIS phase shift optimization algorithm that minimizes the derived bound. Simulations show the effectiveness of the proposed method and demonstrate the performance gain compared to pure NF and FF techniques.