标题： 通过观测矩阵设计增强RIS辅助系统的信道估计 显示英文标题

标题： Enhancing Channel Estimation in RIS-aided Systems via Observation Matrix Design

摘要： 可重构智能表面（RISs）作为一种有前途的技术，通过密集天线阵列增强了无线通信。 准确的信道估计对于释放其全部性能潜力至关重要。 为了增强RIS信道估计器，本文提出了一种新颖的观测矩阵设计方案。 采用贝叶斯优化框架生成观测矩阵，以最大化接收到的导频信号与RIS信道之间的互信息。 为高效解决该问题，我们开发了一种交替黎曼流形优化（ARMO）算法，以交替更新接收端组合器和RIS相移矩阵。 进一步引入自适应核训练策略，以迭代优化信道协方差矩阵，而无需额外的导频资源。 仿真结果表明，所提出的ARMO增强估计器在估计精度方面相对于最先进的方法取得了显著提升。 显示英文摘要

摘要： Reconfigurable intelligent surfaces (RISs) have emerged as a promising technology for enhancing wireless communications through dense antenna arrays. Accurate channel estimation is critical to unlocking their full performance potential. To enhance RIS channel estimators, this paper proposes a novel observation matrix design scheme. Bayesian optimization framework is adopted to generate observation matrices that maximize the mutual information between received pilot signals and RIS channels. To solve the formulated problem efficiently, we develop an alternating Riemannian manifold optimization (ARMO) algorithm to alternately update the receiver combiners and RIS phase-shift matrices. An adaptive kernel training strategy is further introduced to iteratively refine the channel covariance matrix without requiring additional pilot resources. Simulation results demonstrate that the proposed ARMO-enhanced estimator achieves substantial gains in estimation accuracy over state-of-the-art methods.