Near-Field Multiuser Localization Based on Extremely Large Antenna Array with Limited RF Chains

TL;DR

This paper proposes a novel multiuser localization method for near-field ELAA systems with limited RF chains, leveraging array partitioning and geometric constraints to improve accuracy and approach theoretical bounds.

Contribution

It introduces the APLE-LM algorithm that exploits array partitioning and geometric constraints for multiuser localization with limited RF chains in near-field ELAA systems.

Findings

APLE-LM outperforms baseline algorithms in localization accuracy.

The proposed method approaches the Bayesian Cramer-Rao Bound at high SNR.

Extensive simulations validate the effectiveness of the algorithm.

Abstract

Extremely large antenna array (ELAA) not only effectively enhances system communication performance but also improves the sensing capabilities of communication systems, making it one of the key enabling technologies in 6G wireless networks. This paper investigates the multiuser localization problem in an uplink Multiple Input Multiple Output (MIMO) system, where the base station (BS) is equipped with an ELAA to receive signals from multiple single-antenna users. We exploit analog beamforming to reduce the number of radio frequency (RF) chains. We first develop a comprehensive near-field ELAA channel model that accounts for the antenna radiation pattern and free space path loss. Due to the large aperture of the ELAA, the angular resolution of the array is high, which improves user localization accuracy. However, it also makes the user localization problem highly non-convex, posing significant challenges when the number of RF chains is limited. To address this issue, we use an array partitioning strategy to divide the ELAA channel into multiple subarray channels and utilize the geometric constraints between user locations and subarrays for probabilistic modeling. To fully exploit these geometric constraints, we propose the array partitioning-based location estimation with limited measurements (APLE-LM) algorithm based on the message passing principle to achieve multiuser localization. We derive the Bayesian Cramer-Rao Bound (BCRB) as the theoretical performance lower bound for our formulated near-field multiuser localization problem. Extensive simulations under various parameter configurations validate the proposed APLE-LM algorithm. The results demonstrate that APLE-LM achieves superior localization accuracy compared to baseline algorithms and approaches the BCRB at high signal-to-noise ratio (SNR).