标题： 重新审视双极化 XL-MIMO 系统中的近远场边界 显示英文标题

标题： Revisiting Near-Far Field Boundary in Dual-Polarized XL-MIMO Systems

摘要： 超大规模多输入多输出（XL-MIMO）预计将是在未来第六代（6G）网络中的关键技术。 与传统的单极化XL-MIMO相比，后者仅在一个极化方向上发送和接收信号，双极化XL-MIMO系统通过提高复用性能实现更高的数据速率，因此是本文的研究重点。 由于孔径扩大，近场区域在XL-MIMO通信中变得不可忽略，需要准确的远近场边界表征。 然而，现有为单极化系统开发的边界仅考虑阵列元素之间的相位或功率差异，而忽略了双极化XL-MIMO系统中交叉极化鉴别（XPD）的变化，从而降低了发射协方差优化性能。 在本文中，我们通过考虑XPD差异重新审视双极化XL-MIMO系统的远近场边界，这面临以下挑战。 与现有仅需考虑同极化信道分量的远近场边界不同，推导双极化XL-MIMO系统的边界需要对同极化和交叉极化分量的联合效应进行建模。 为了解决这个问题，我们对天线间的XPD变化进行建模，并引入一种非均匀XPD距离来补充现有的远近场边界。 基于新的距离准则，我们提出了一种高效的发射协方差优化方案。 数值结果验证了我们的分析，并展示了所提算法的有效性。 显示英文摘要

摘要： Extremely large-scale multiple-input multiple-output (XL-MIMO) is expected to be an important technology in future sixth generation (6G) networks. Compared with conventional single-polarized XL-MIMO, where signals are transmitted and received in only one polarization direction, dual-polarized XL-MIMO systems achieve higher data rate by improving multiplexing performances, and thus are the focus of this paper. Due to enlarged aperture, near-field regions become non-negligible in XL-MIMO communications, necessitating accurate near-far field boundary characterizations. However, existing boundaries developed for single-polarized systems only consider phase or power differences across array elements while irrespective of cross-polarization discrimination (XPD) variances in dual-polarized XL-MIMO systems, deteriorating transmit covariance optimization performances. In this paper, we revisit near-far field boundaries for dual-polarized XL-MIMO systems by taking XPD differences into account, which faces the following challenge. Unlike existing near-far field boundaries, which only need to consider co-polarized channel components, deriving boundaries for dual-polarized XL-MIMO systems requires modeling joint effects of co-polarized and cross-polarized components. To address this issue, we model XPD variations across antennas and introduce a non-uniform XPD distance to complement existing near-far field boundaries. Based on the new distance criterion, we propose an efficient scheme to optimize transmit covariance. Numerical results validate our analysis and demonstrate the proposed algorithm's effectiveness.