For the demonstration of ultra-wideband bandwidth and pencil-beamforming, the terahertz (THz)-band has been envisioned as one of the key enabling technologies for the sixth generation networks. However, the acquisition of the THz channel entails several unique challenges such as severe path loss and beam-split. Prior works usually employ ultra-massive arrays and additional hardware components comprised of time-delayers to compensate for these loses. In order to provide a cost-effective solution, this paper introduces a sparse-Bayesian-learning (SBL) technique for joint channel and beam-split estimation. Specifically, we first model the beam-split as an array perturbation inspired from array signal processing. Next, a low-complexity approach is developed by exploiting the line-of-sight-dominant feature of THz channel to reduce the computational complexity involved in the proposed SBL technique for channel estimation (SBCE). Additionally, based on federated-learning, we implement a model-free technique to the proposed model-based SBCE solution. Further to that, we examine the near-field considerations of THz channel, and introduce the range-dependent near-field beam-split. The theoretical performance bounds, i.e., Cram\'er-Rao lower bounds, are derived both for near- and far-field parameters, e.g., user directions, beam-split and ranges. Numerical simulations demonstrate that SBCE outperforms the existing approaches and exhibits lower hardware cost.

翻译：为验证超宽带带宽和笔形波束赋形的能力，太赫兹波段被视作第六代网络的关键使能技术之一。然而，太赫兹信道的获取面临若干独特挑战，如严重的路径损耗和波束分裂。现有研究通常采用超大规模阵列及由时延器构成的额外硬件组件来补偿这些损耗。为提供经济可行的解决方案，本文提出一种基于稀疏贝叶斯学习的联合信道与波束分裂估计技术。具体而言，我们首先借鉴阵列信号处理思想，将波束分裂建模为阵列扰动；随后利用太赫兹信道视距主导特性，开发了一种低复杂度方法以降低所提出的基于稀疏贝叶斯学习的信道估计（SBCE）的计算复杂度。此外，基于联邦学习，我们在所提出的模型驱动型SBCE方案中实现了无模型技术。进一步，我们研究了太赫兹信道的近场特性，并引入距离相关的近场波束分裂。理论性能界（即克拉美-罗下界）同时针对近场与远场参数（如用户方向、波束分裂及距离）进行推导。数值仿真表明，SBCE优于现有方法且具备更低的硬件成本。