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 for near- and far-field parameters, e.g., user directions, ranges and beam-split, and several numerical experiments are conducted. Numerical simulations demonstrate that SBCE outperforms the existing approaches and exhibits lower hardware cost.
翻译:对于展示超宽带带宽和铅笔波形,Thahertz(Thz)波段被认为是第六代网络的主要赋能技术之一,然而,Thz频道的购置带来了若干独特的挑战,如道路严重丢失和横线分割等。以前的工作通常使用超大宽幅阵列和额外的硬件组件来弥补这些损失。为了提供具有成本效益的解决方案,本文件采用了稀有的Bayesian(SBL)模拟学习技术(SBL),用于联合频道和波纹估计。具体地说,我们首先将Baam-split作为来自阵列信号处理的阵列穿透方法进行模拟。接下来,通过利用Thz频道的直观光谱阵列特征和由时间显示器组成的额外硬件组件来开发一种低兼容性的方法,以减少拟议SBL技术(SBCE)用于频道估算(SBCE)的计算复杂性。此外,我们根据Federerate-deal-learnical的学习,我们将一个无型技术用于拟议的基于SBCE的模型的模型、近BC-SBC-rodu-roduformal-lade-laft 和离近距离的模型解决方案。我们检查了目前和远端的轨道-Forth-rode-ro-tra-rode-traal-traal-ro-tra-ro-tra-ro-roal-lades-lades-lades-lad-lax-lad-lax-lades-lax-lades-lades-lades-lades-tra-lades-lades-lades-lades-lax-lax-lax-lax-lax-lax-lax-lax-tra-tra-tra-tra-tra-lax-lax-tra-tra-tra-tra-tra-tra-tra-tra-tra-tra-tra-tra-tra-lax-lax-lad-lax-lax-lax-lax-lax-lax-lad-lax-lax-lax-lax-lax-lax-lax-lax-lax-lax-lax-lax-lax-lax-lax-la