Scalable Near-Field Localization Based on Partitioned Large-Scale Antenna Array

TL;DR

This paper introduces a scalable near-field localization algorithm using partitioned large-scale antenna arrays, significantly reducing computational complexity while improving accuracy in localizing user equipment.

Contribution

The paper proposes the APLE algorithm that partitions the antenna array for scalable near-field localization, with an enhanced version E-APLE that improves accuracy while maintaining linear complexity.

Findings

APLE reduces computational complexity to linear in the number of antennas.

E-APLE achieves higher localization accuracy than APLE.

Both algorithms outperform existing methods in accuracy.

Abstract

This paper studies a passive localization system, where an extremely large-scale antenna array (ELAA) is deployed at the base station (BS) to locate a user equipment (UE) residing in its near-field (Fresnel) region. We propose a novel algorithm, named array partitioning-based location estimation (APLE), for scalable near-field localization. The APLE algorithm is developed based on the basic assumption that, by partitioning the ELAA into multiple subarrays, the UE can be approximated as in the far-field region of each subarray. We establish a Bayeian inference framework based on the geometric constraints between the UE location and the angles of arrivals (AoAs) at different subarrays. Then, the APLE algorithm is designed based on the message-passing principle for the localization of the UE. APLE exhibits linear computational complexity with the number of BS antennas, leading to a significant reduction in complexity compared to existing methods. We further propose an enhanced APLE (E-APLE) algorithm that refines the location estimate obtained from APLE by following the maximum likelihood principle. The E-APLE algorithm achieves superior localization accuracy compared to APLE while maintaining a linear complexity with the number of BS antennas. Numerical results demonstrate that the proposed APLE and E-APLE algorithms outperform the existing baselines in terms of localization accuracy.