Sensing User's Activity, Channel, and Location with Near-Field Extra-Large-Scale MIMO

TL;DR

This paper introduces a novel XL-MIMO-based massive access scheme for 6G IoT networks, enabling low-latency, high-data-rate communication and precise user localization by exploiting near-field properties and structured algorithms.

Contribution

It proposes a structured block orthogonal matching pursuit algorithm for active user detection and channel estimation, and a multi-subarray collaborative localization method utilizing AoA and TDoA.

Findings

Outperforms existing algorithms in AUD and CE performance

Achieves centimeter-level localization accuracy

Effectively exploits near-field SNS property and diversity gains

Abstract

This paper proposes a grant-free massive access scheme based on the millimeter wave (mmWave) extra-large-scale multiple-input multiple-output (XL-MIMO) to support massive Internet-of-Things (IoT) devices with low latency, high data rate, and high localization accuracy in the upcoming sixth-generation (6G) networks. The XL-MIMO consists of multiple antenna subarrays that are widely spaced over the service area to ensure line-of-sight (LoS) transmissions. First, we establish the XL-MIMO-based massive access model considering the near-field spatial non-stationary (SNS) property. Then, by exploiting the block sparsity of subarrays and the SNS property, we propose a structured block orthogonal matching pursuit algorithm for efficient active user detection (AUD) and channel estimation (CE). Furthermore, different sensing matrices are applied in different pilot subcarriers for exploiting the diversity gains. Additionally, a multi-subarray collaborative localization algorithm is designed for localization. In particular, the angle of arrival (AoA) and time difference of arrival (TDoA) of the LoS links between active users and related subarrays are extracted from the estimated XL-MIMO channels, and then the coordinates of active users are acquired by jointly utilizing the AoAs and TDoAs. Simulation results show that the proposed algorithms outperform existing algorithms in terms of AUD and CE performance and can achieve centimeter-level localization accuracy.