面向移动性超大规模MIMO系统的混合场信道跟踪
Hybrid-Field Channel Tracking for Extremely Large-Scale MIMO Systems with Mobility
- East China Normal University（华东师范大学）
- Nanjing University of Information Science and Technology（南京信息工程大学）
- Southeast University（东南大学）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
本文提出一种利用历史信道状态信息的混合场信道跟踪算法，用于移动性超大规模MIMO系统，通过稀疏峰值搜索和牛顿细化实现低复杂度高性能的多径估计。
中文摘要 AI 辅助
本文提出了一种利用历史信道状态信息，针对具有移动性的超大规模多输入多输出（XL-MIMO）系统的混合场信道跟踪算法。考虑了多径场景，基于用户运动的时域连续性，在分数阶傅里叶域内的窄窗口中进行稀疏峰值搜索，从而粗略估计视距路径。此外，通过确保散射体生存概率来确定非视距（NLoS）路径的粗略估计，从而消除了检测现有NLoS路径的必要性。然后，在寻找可能的新路径之前，对估计的路径应用基于牛顿法的细化。数值结果验证了所提算法以低计算复杂度实现了优越的性能。
英文摘要
In this paper, we propose a hybrid-field channel tracking algorithm for extremely large-scale multiple-input multiple-output (XL-MIMO) systems with mobility, leveraging the historical channel state information. The multiple path scenario is considered, and the line-of-sight path is coarsely estimated through the sparse peak search within a narrow window in the fractional Fourier domain based on the temporal continuity of the user's motion. Moreover, coarse estimations of the non-line-of-sight (NLoS) paths are determined by ensuring the scatterer survival probability, eliminating the necessity of detecting existing NLoS paths. Then, a Newton-based refinement is applied to the estimated paths before seeking possible new paths. Numerical results validate that the proposed algorithm achieves superior performance with low computational complexity.
