In this work, we investigate the joint visibility region (VR) detection and channel estimation (CE) problem for extremely large-scale multiple-input-multiple-output (XL-MIMO) systems considering both the spherical wavefront effect and spatial non-stationary (SnS) property. Unlike existing SnS CE methods that rely on the statistical characteristics of channels in the spatial or delay domain, we propose an approach that simultaneously exploits the antenna-domain spatial correlation and the wavenumber-domain sparsity of SnS channels. To this end, we introduce a two-stage VR detection and CE scheme. In the first stage, the belief regarding the visibility of antennas is obtained through a VR detection-oriented message passing (VRDO-MP) scheme, which fully exploits the spatial correlation among adjacent antenna elements. In the second stage, leveraging the VR information and wavenumber-domain sparsity, we accurately estimate the SnS channel employing the belief-based orthogonal matching pursuit (BB-OMP) method. Simulations show that the proposed algorithms lead to a significant enhancement in VR detection and CE accuracy as compared to existing methods, especially in low signal-to-noise ratio (SNR) scenarios.

翻译：本文研究了超大规模多输入多输出（XL-MIMO）系统中考虑球面波前效应与空间非平稳（SnS）特性的联合可见区域（VR）检测与信道估计（CE）问题。与现有依赖空间域或时延域信道统计特性的SnS CE方法不同，我们提出了一种同时利用SnS信道天线域空间相关性与波数域稀疏性的方法。为此，我们引入了一个两阶段VR检测与CE方案。在第一阶段，通过基于VR检测导向的消息传递（VRDO-MP）方案获取天线可见性的置信度，该方案充分利用了相邻天线单元间的空间相关性。在第二阶段，我们结合VR信息与波数域稀疏性，采用基于置信度的正交匹配追踪（BB-OMP）方法精确估计SnS信道。仿真表明，与现有方法相比，所提算法显著提升了VR检测与CE的准确性，尤其在低信噪比（SNR）场景下表现突出。