Extremely large-scale multiple-input multiple-output (XL-MIMO) is gaining attention as a prominent technology for enabling the sixth-generation (6G) wireless networks. However, the vast antenna array and the huge bandwidth introduce a non-negligible beam squint effect, causing beams of different frequencies to focus at different locations. One approach to cope with this is to employ true-time-delay lines (TTDs)-based beamforming to control the range and trajectory of near-field beam squint, known as the near-field controllable beam squint (CBS) effect. In this paper, we investigate the user localization in near-field wideband XL-MIMO systems under the beam squint effect and spatial non-stationary properties. Firstly, we derive the expressions for Cram\'er-Rao Bounds (CRBs) for characterizing the performance of estimating both angle and distance. This analysis aims to assess the potential of leveraging CBS for precise user localization. Secondly, a user localization scheme combining CBS and beam training is proposed. Specifically, we organize multiple subcarriers into groups, directing beams from different groups to distinct angles or distances through the CBS to obtain the estimates of users' angles and distances. Furthermore, we design a user localization scheme based on a convolutional neural network model, namely ConvNeXt. This scheme utilizes the inputs and outputs of the CBS-based scheme to generate high-precision estimates of angle and distance. More importantly, our proposed ConvNeXt-based user localization scheme achieves centimeter-level accuracy in localization estimates.

翻译：超大规模多输入多输出（XL-MIMO）作为实现第六代（6G）无线网络的关键技术正受到广泛关注。然而，庞大的天线阵列与超宽带宽会引入不可忽视的波束斜视效应，导致不同频率的波束聚焦于不同位置。应对此问题的一种方法是采用基于真实时延线（TTD）的波束赋形技术，以控制近场波束斜视的范围与轨迹，这被称为近场可控波束斜视（CBS）效应。本文研究了在波束斜视效应与空间非平稳特性下的近场宽带XL-MIMO系统用户定位问题。首先，我们推导了用于表征角度与距离估计性能的克拉美-罗界（CRB）表达式，旨在评估利用CBS实现精确用户定位的潜力。其次，提出了一种结合CBS与波束训练的用户定位方案。具体而言，我们将多个子载波分组，通过CBS将不同组的波束导向不同角度或距离，从而获取用户角度与距离的估计值。此外，我们设计了一种基于卷积神经网络模型（即ConvNeXt）的用户定位方案。该方案利用基于CBS方案的输入与输出，生成高精度的角度与距离估计值。更重要的是，我们提出的基于ConvNeXt的用户定位方案在定位估计中实现了厘米级精度。