发表机构
Xi’an Jiaotong University; Stevens Institute of Technology; Nanjing University of Science and Technology; Shanghai Jiao Tong University（西安交通大学; 史蒂文斯理工学院; 南京理工大学; 上海交通大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对可移动天线系统的信道估计难题，综述了基于张量分解的建模与参数估计方法，并通过案例对比验证其性能，展望了未来研究方向。
AI 中文摘要
可移动天线（MA）通过利用局部天线移动区域内的信道变化，已成为未来无线网络的一项有前景的技术。然而，由于估计精度与计算复杂度之间的权衡，在MA系统中实现准确高效的信道获取仍然具有挑战性。本文首先回顾了MA信道模型及相关的估计框架，其中通过利用共享路径参数，从有限测量中重建移动区域内的信道信息。MA观测在空间、时间和频率上的结构化依赖性自然促使采用基于张量的建模进行信道估计。随后，我们讨论了基于张量的信号模型及相应的多维观测参数估计方法。这些方法在估计精度、计算复杂度和通用适用性方面与常规信道估计方法进行了比较。此外，提供了一个代表性案例研究，以说明不同算法在MA信道估计设置下的性能和特点。最后，概述了MA系统中基于张量分解的信道估计的一些未来研究方向。
英文摘要
Movable antenna (MA) has emerged as a promising technology for future wireless networks by exploiting channel variation over local antenna movement regions. However, accurate and efficient channel acquisition in MA systems remains challenging due to the trade-off between estimation accuracy and computational complexity. In this article, the MA channel model and the associated estimation framework are first reviewed, where channel information over the movement region is reconstructed from finite measurements by exploiting shared path parameters. The structured dependence of MA observations across space, time, and frequency naturally motivates the adoption of tensor-based modeling for channel estimation. Subsequently, we discuss the tensor-based signal model and corresponding parameter estimation methods from multidimensional observations. These methods are further compared with conventional channel estimation methods in terms of estimation accuracy, computational complexity, and general applicability. Furthermore, a representative case study is provided to illustrate the performance and characteristics of different algorithms under MA channel estimation settings. Finally, some future research directions for tensor decomposition-based channel estimation in MA systems are outlined.
