AI 中文总结
本文针对可移动平面阵列近场UM-MIMO系统，提出基于信道图的信道估计框架，整合阵列重构与球面波建模，实现高精度低复杂度的LoS与NLoS估计，多用户场景下性能优于现有基准方法。
AI 中文摘要
精确的信道估计对于超大规模多输入多输出（UM-MIMO）系统中的相干传输至关重要，而近场传播和高维空间信道会带来巨大的信号处理挑战。可移动天线架构因与几何相关的信道变化进一步增加了估计复杂度。现有方法难以在精度和复杂度之间取得平衡，这促使人们利用与环境相关的传播结构实现高效估计。为此，本文提出一种用于UM-MIMO系统的统一基于信道图的信道估计框架，该框架整合了可移动平面阵列重构和近场球面波建模，以支持感知几何的视距（LoS）估计和高效非视距（NLoS）恢复。文中提出一种基于信道图的LoS估计器，将粗略的用户位置信息与费舍尔信息引导的天线放置策略相结合；还提出两种高效的NLoS估计方法，包括用于低复杂度处理的基于草图的降子空间估计器，以及利用散射体位置信息实现近最优性能的基于信道图的估计器。该框架还针对多用户场景纳入了可见区域建模和基于结构相似性的导频分配策略。仿真结果表明，与不使用信道图的最新基准方法相比，所提出的基于信道图的框架可提高估计精度、降低计算开销并增强可扩展性。
英文摘要
Accurate channel estimation is essential for coherent transmission in ultra-massive multiple-input multiple-output (UM-MIMO) systems, where near-field propagation and high-dimensional spatial channels impose substantial signal processing challenges. Movable antenna architectures increase the estimation complexity further due to geometry-dependent channel variations. Existing approaches struggle to balance accuracy and complexity, motivating the use of environment-dependent propagation structures for efficient estimation. To this end, this paper proposes a unified channel map-based channel estimation framework for UM-MIMO systems, which integrates movable planar array reconfiguration and near-field spherical-wave modeling to support geometry-aware line-of-sight (LoS) estimation and efficient non-LoS (NLoS) recovery. A channel map-based LoS estimator is proposed combining coarse user position information with a Fisher information-guided antenna placement strategy. Two efficient NLoS estimation methods are also presented, including a sketch-based reduced-subspace estimator for low-complexity processing and a channel map-based estimator that leverages scatterer location information for near-optimal performance. The framework further incorporates visibility-region modeling and a structural similarity-based pilot assignment strategy for multi-user scenarios. Simulation results show that the proposed channel map-based framework improves estimation accuracy, reduces computational overhead, and enhances scalability compared with state-of-the-art benchmarks without channel maps.
Comments14 pages, 8 figures
