Holographic MIMO (HMIMO) is being increasingly recognized as a key enabling technology for 6G wireless systems through the deployment of an extremely large number of antennas within a compact space to fully exploit the potentials of the electromagnetic (EM) channel. Nevertheless, the benefits of HMIMO systems cannot be fully unleashed without an efficient means to estimate the high-dimensional channel, whose distribution becomes increasingly complicated due to the accessibility of the near-field region. In this paper, we address the fundamental challenge of designing a low-complexity Bayes-optimal channel estimator in near-field HMIMO systems operating in unknown EM environments. The core idea is to estimate the HMIMO channels solely based on the Stein's score function of the received pilot signals and an estimated noise level, without relying on priors or supervision that is not feasible in practical deployment. A neural network is trained with the unsupervised denoising score matching objective to learn the parameterized score function. Meanwhile, a principal component analysis (PCA)-based algorithm is proposed to estimate the noise level leveraging the low-rank near-field spatial correlation. Building upon these techniques, we develop a Bayes-optimal score-based channel estimator for fully-digital HMIMO transceivers in a closed form. The optimal score-based estimator is also extended to hybrid analog-digital HMIMO systems by incorporating it into a low-complexity message passing algorithm. The (quasi-) Bayes-optimality of the proposed estimators is validated both in theory and by extensive simulation results. In addition to optimality, it is shown that our proposal is robust to various mismatches and can quickly adapt to dynamic EM environments in an online manner thanks to its unsupervised nature, demonstrating its potential in real-world deployment.

翻译：全息MIMO通过在紧凑空间内部署极大数量天线，充分挖掘电磁信道潜力，正日益被视为6G无线系统的关键使能技术。然而，若缺乏高效手段估计高维信道，全息MIMO系统的优势便无法完全释放——由于近场区域的可达性，信道分布变得日益复杂。本文针对未知电磁环境下近场全息MIMO系统的基本挑战，设计了一种低复杂度的贝叶斯最优信道估计器。核心思想是仅基于接收导频信号的Stein得分函数与估计噪声水平来估计全息MIMO信道，无需依赖实际部署中不可行的先验信息或监督。我们采用无监督降噪得分匹配目标训练神经网络，以学习参数化得分函数；同时，提出基于主成分分析的算法，利用近场空间相关性的低秩特性估计噪声水平。基于这些技术，我们为全数字全息MIMO收发器推导出闭合形式的贝叶斯最优得分信道估计器。通过将该最优得分估计器融入低复杂度消息传递算法，扩展至混合模数全息MIMO系统。理论与大量仿真结果均验证了所提估计器的（准）贝叶斯最优性。除最优性外，研究表明该方案对多种失配具有鲁棒性，并凭借其无监督特性能够在线快速适应动态电磁环境，彰显了在实际部署中的潜力。