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：本文研究了超大规模多输入多输出（XL-MIMO）系统的信道估计问题，重点考虑了球面波前效应和空间非平稳（SnS）特性。由于不同传播路径间SnS特性的多样性，多路径的并发信道估计变得极为困难。为应对这一挑战，我们提出了一种两阶段信道估计方案。在第一阶段，估计用户侧的离场角（AoDs），并通过精心设计的导频传输方案实现不同路径接收信号的解耦。在第二阶段，将对应于不同路径的子信道估计建模为一个三层贝叶斯推理问题。具体而言，第一层捕捉角域中的块稀疏性，第二层在天线域中促进SnS特性，第三层将子信道从观测信号中解耦。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，提出了一种新颖的三层广义近似消息传递（TL-GAMP）算法。仿真结果验证了所提算法的收敛性和有效性，展示了其在不同信道场景下的鲁棒性。