Terahertz (THz) communication is widely considered as a key enabler for future 6G wireless systems. However, THz links are subject to high propagation losses and inter-symbol interference due to the frequency selectivity of the channel. Massive multiple-input multiple-output (MIMO) along with orthogonal frequency division multiplexing (OFDM) can be used to deal with these problems. Nevertheless, when the propagation delay across the base station (BS) antenna array exceeds the symbol period, the spatial response of the BS array varies across the OFDM subcarriers. This phenomenon, known as beam squint, renders narrowband combining approaches ineffective. Additionally, channel estimation becomes challenging in the absence of combining gain during the training stage. In this work, we address the channel estimation and hybrid combining problems in wideband THz massive MIMO with uniform planar arrays. Specifically, we first introduce a low-complexity beam squint mitigation scheme based on true-time-delay. Next, we propose a novel variant of the popular orthogonal matching pursuit (OMP) algorithm to accurately estimate the channel with low training overhead. Our channel estimation and hybrid combining schemes are analyzed both theoretically and numerically. Moreover, the proposed schemes are extended to the multi-antenna user case. Simulation results are provided showcasing the performance gains offered by our design compared to standard narrowband combining and OMP-based channel estimation.
翻译:Terahertz (Thz) 通信被广泛视为未来6G无线系统的关键推进器,然而,由于频道的频率选择,Thz 连接会因频度选择而发生高传播损失和气象干扰。可使用大规模多输入多输出(MIMO)以及正方位频率多输出(OFDM)来处理这些问题。然而,当基础站(BS)天线阵列的传播延迟超过符号期时,BS阵列的空间反应在DM子载体中各不相同。这一现象被称为Bam Squint, 使窄带组合方法变得无效。此外,在培训阶段没有合并收益,频道估计就具有挑战性。在这项工作中,我们用统一的平面阵列阵列阵列解决了频道大段THZ MIMO的问题和混合问题。具体地说,我们首先采用基于真实时间delay的低相容光谱缩缩缩缩放减缓计划。我们提出的大众或深层匹配计划(OMP)的窄带匹配计划(OMP) 组合O-Simental commalalalalalalalal combal compeal comma-view view 和我们提出的低导算法是用来精确地分析我们提出的数字和模拟的比平流、平流平流、平流、平流路的平流路的平流路段和S-imal-imal-qual-qual-qal-qal-vial-vial-vical-vical-vial-vical-vical-vical-vical-vical-vical-vial-vial-vical-vical-vial-vial-sal-sal-sal-sal-sal-sal-sal-xxxxxxxxxxxxxxxxxxxxxxxal-s-s-s-vial-s-s-s-s-vical-vical-s-s-s-s-s-s-s-s-s-xxxxxxxxxxxxxxxxxxxxal-xxxxxxx-