Leveraging the concept of the electromagnetic signal and information theory, holographic multiple-input multiple-output (MIMO) technology opens the door to an intelligent and endogenously holography-capable wireless propagation environment, with their unparalleled capabilities for achieving high spectral and energy efficiency. Less examined are the important issues such as the acquisition of accurate channel information by accounting for holographic MIMO’s peculiarities. To fill this knowledge gap, 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.

中文翻译：

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基于角距离的全息 MIMO 信道估计


利用电磁信号和信息理论的概念，全息多输入多输出（MIMO）技术打开了通向智能且具有内生全息能力的无线传播环境的大门，其具有无与伦比的实现高频谱和能源效率的能力。较少研究的是重要问题，例如通过考虑全息 MIMO 的特性来获取准确的信道信息。为了填补这一知识空白，本文通过揭示全息 MIMO 系统与传统系统的区别来研究全息 MIMO 系统的信道估计。具体来说，我们阐明，受全息 MIMO 电磁大型天线阵列影响，信道估计不仅要区分用户/散射体的角度，还要区分其距离信息，即三维 (3D) 方位角和仰角以及距离（AED）参数。由于角域表示无法表征全息 MIMO 信道固有的稀疏性，因此首先分解紧耦合的 3D AED 参数以独立构建它们自己的协方差矩阵。然后，通过利用构建的协方差矩阵，可以将每个单独参数的恢复构造为压缩感知（CS）问题。这对技术有助于参数分解和压缩解构 (DeRe) 框架，以及每个参数的最大似然估计的公式。然后，提出了一种有效的算法，即基于 DeRe 的变分贝叶斯推理和消息传递（DeRe-VM），用于 3D AED 参数的锐检测和稀疏通道的鲁棒恢复。 最后，无论全息 MIMO 系统的近场和远场环境如何，所提出的信道估计机制被证实在适应不同的信道条件方面具有很强的鲁棒性，并且与状态相比具有改进的性能。 -最先进的基准。