通过优化观测矩阵提升RIS系统的信道估计精度。
Enhancing Channel Estimation in RIS-aided Systems via Observation Matrix Design
- 用贝叶斯优化设计观测矩阵，最大化接收导频与信道的互信息。
- 提出的ARMO算法显著提高估计精度，优于现有方法。
- 无需额外导频即可自适应更新信道协方差矩阵，适合实际部署。
可重构智能表面（RIS）作为新兴技术，通过密集天线阵列增强无线通信性能。准确的信道估计是发挥其潜力的关键。本文提出一种新型观测矩阵设计方法，采用贝叶斯优化框架生成最大化接收导频信号与RIS信道间互信息的观测矩阵。为高效求解该问题，设计了交替黎曼流形优化（ARMO）算法，交替更新接收端波束成形器和RIS相移矩阵。进一步引入自适应核训练策略，在不增加导频资源的前提下迭代优化信道协方差矩阵。仿真结果表明，所提ARMO增强型估计器在估计精度上显著优于现有先进方法。
原文摘要 · Abstract (English)
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.
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