Extremely large-scale multiple-input multiple-output (XL-MIMO) is a key technology for next-generation wireless communication systems. By deploying significantly more antennas than conventional massive MIMO systems, XL-MIMO promises substantial improvements in spectral efficiency. However, due to the drastically increased array size, the conventional planar wave channel model is no longer accurate, necessitating a transition to a near-field spherical wave model. This shift challenges traditional beam training and channel estimation methods, which were designed for planar wave propagation. In this article, we present a comprehensive review of state-of-the-art beam training and channel estimation techniques for XL-MIMO systems. We analyze the fundamental principles, key methodologies, and recent advancements in this area, highlighting their respective strengths and limitations in addressing the challenges posed by the near-field propagation environment. Furthermore, we explore open research challenges that remain unresolved to provide valuable insights for researchers and engineers working toward the development of next-generation XL-MIMO communication systems.

翻译：超大规模多输入多输出（XL-MIMO）是下一代无线通信系统的关键核心技术。通过部署相较于传统大规模MIMO系统数量显著更多的天线，XL-MIMO有望实现频谱效率的大幅提升。然而，由于阵列规模急剧增大，传统的平面波信道模型不再精确，需转向近场球面波模型。这一转变对传统基于平面波传播设计的波束训练与信道估计方法构成了挑战。本文全面综述了XL-MIMO系统中当前最先进的波束训练与信道估计技术。我们分析了该领域的基本原理、关键方法以及最新进展，重点阐述了它们在应对近场传播环境挑战方面的各自优势与局限性。此外，我们探讨了尚未解决的研究挑战，旨在为致力于开发下一代XL-MIMO通信系统的研究人员与工程师提供有价值的见解。