This paper investigates the channel estimation for holographic MIMO systems by unmasking their distinctions from the conventional one. Specifically, we elucidate that the channel estimation, subject to holographic MIMO's electromagnetically large antenna arrays, has to discriminate not only the angles of a user/scatterer but also its distance information, namely the three-dimensional (3D) azimuth and elevation angles plus the distance (AED) parameters. As the angular-domain representation fails to characterize the sparsity inherent in holographic MIMO channels, the tightly coupled 3D AED parameters are firstly decomposed for independently constructing their own covariance matrices. Then, the recovery of each individual parameter can be structured as a compressive sensing (CS) problem by harnessing the covariance matrix constructed. This pair of techniques contribute to a parametric decomposition and compressed deconstruction (DeRe) framework, along with a formulation of the maximum likelihood estimation for each parameter. Then, an efficient algorithm, namely DeRe-based variational Bayesian inference and message passing (DeRe-VM), is proposed for the sharp detection of the 3D AED parameters and the robust recovery of sparse channels. Finally, the proposed channel estimation regime is confirmed to be of great robustness in accommodating different channel conditions, regardless of the near-field and far-field contexts of a holographic MIMO system, as well as an improved performance in comparison to the state-of-the-art benchmarks.

翻译：本文通过揭示全息MIMO系统与传统系统的本质区别，研究其信道估计问题。具体而言，我们阐明：在全息MIMO电磁大天线阵列约束下，信道估计不仅需要区分用户/散射体的角度信息，还需分辨其距离信息，即三维（3D）方位角、俯仰角及距离（AED）参数。由于角域表示无法刻画全息MIMO信道的固有稀疏性，我们首先对高度耦合的三维AED参数进行分解，以独立构建各自的协方差矩阵；进而利用已构建的协方差矩阵将各参数恢复问题建模为压缩感知（CS）问题。这两种技术共同构参数解耦与压缩重建（DeRe）框架，并为各参数建立最大似然估计公式。在此基础上，提出一种高效算法——基于DeRe的变分贝叶斯推断与消息传递（DeRe-VM），实现三维AED参数的精确检测与稀疏信道的鲁棒恢复。最后，实验证实所提信道估计方案在近场和远场场景下均具有对不同信道条件的强鲁棒性，且相较于现有最优基准方法展现出更优性能。