标题： 基于深度学习的宽带XL-MIMO系统中近场用户定位与波束倾斜 显示英文标题

标题： Deep Learning Based Near-Field User Localization with Beam Squint in Wideband XL-MIMO Systems

摘要： 超大规模多输入多输出（XL-MIMO）作为一种关键技术，正在成为第六代（6G）无线网络的热门技术。 然而，庞大的天线阵列和巨大的带宽引入了不可忽视的波束失真效应，导致不同频率的波束聚焦在不同的位置。 一种应对方法是采用真时延线（TTDs）的波束成形来控制近场波束失真范围和轨迹，称为近场可控制波束失真（CBS）效应。 在本文中，我们研究了在波束失真效应和空间非平稳特性下，近场宽带XL-MIMO系统中的用户定位。 首先，我们推导了Cramér-Rao界限（CRBs）的表达式，以表征同时估计角度和距离的性能。 此分析旨在评估利用CBS进行精确用户定位的潜力。 其次，提出了一种结合CBS和波束训练的用户定位方案。 具体来说，我们将多个子载波分组，通过CBS将不同组的波束导向不同的角度或距离，以获得用户的角度和距离估计。 此外，我们设计了一种基于卷积神经网络模型的用户定位方案，即ConvNeXt。 该方案利用基于CBS的方案的输入和输出，生成高精度的角度和距离估计。 更重要的是，我们提出的基于ConvNeXt的用户定位方案实现了厘米级的定位精度。 显示英文摘要

摘要： 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.