In this paper, channel estimation problem for extremely large-scale multi-input multi-output (XL-MIMO) systems is investigated with the considerations of the spherical wavefront effect and the spatially non-stationary (SnS) property. Due to the diversities of SnS characteristics among different propagation paths, the concurrent channel estimation of multiple paths becomes intractable. To address this challenge, we propose a two-phase channel estimation scheme. In the first phase, the angles of departure (AoDs) on the user side are estimated, and a carefully designed pilot transmission scheme enables the decomposition of the received signal from different paths. In the second phase, the subchannel estimation corresponding to different paths is formulated as a three-layer Bayesian inference problem. Specifically, the first layer captures block sparsity in the angular domain, the second layer promotes SnS property in the antenna domain, and the third layer decouples the subchannels from the observed signals. To efficiently facilitate Bayesian inference, we propose a novel three-layer generalized approximate message passing (TL-GAMP) algorithm based on structured variational massage passing and belief propagation rules. Simulation results validate the convergence and effectiveness of the proposed algorithm, showcasing its robustness to different channel scenarios.

翻译：本文考虑球面波前效应和空间非平稳特性，研究了超大规模多输入多输出（XL-MIMO）系统的信道估计问题。由于不同传播路径的空间非平稳特征存在差异性，多路径并发信道估计变得难以处理。为应对这一挑战，我们提出了一种两阶段信道估计方案。第一阶段估计用户侧离开角，通过精心设计的导频传输方案实现不同路径接收信号的分解；第二阶段将对应不同路径的子信道估计建模为三层贝叶斯推理问题。具体而言，第一层捕获角度域的块稀疏性，第二层增强天线域的空间非平稳特性，第三层从观测信号中解耦子信道。为高效实现贝叶斯推理，我们基于结构化变分消息传递和置信传播规则，提出一种新型三层广义近似消息传递算法。仿真结果验证了所提算法的收敛性和有效性，展示了其对不同信道场景的鲁棒性。