论文标题
Hybrid MMWave MIMO系统在硬件障碍和光束斜视下：频道模型和词典学习辅助配置
Hybrid mmWave MIMO Systems under Hardware Impairments and Beam Squint: Channel Model and Dictionary Learning-aided Configuration
论文作者
论文摘要
基于压缩传感（CS）的低架空通道估计已被广泛研究，用于混合宽带毫米波（MMWAVE）多输入多输出（MIMO）系统。先前工作中使用的通道稀疏字典是根据根据离散的到达/出发角度评估的理想数组响应向量构建的。此外，这些字典被认为对所有子载波而言都是相同的，而无需考虑硬件障碍和横梁的影响。在此手稿中，我们得出了一个通用通道和信号模型，该模型明确地结合了硬件障碍，实用的脉冲成型功能和梁斜视的影响，从而克服了MMWAVE MIMO通道的局限性和先前工作中常用的信号模型。然后，我们提出了一种词典学习（DL）算法，以通过考虑横梁斜视的效果而无需将其引入学习过程，从而获得嵌入硬件障碍的稀疏字典。我们还设计了一种新颖的CS通道估计算法，在梁斜视和硬件障碍下，利用不同子载波的通道结构以低复杂性和高精度启用通道参数估计。数值结果证明了应用于现实的MMWAVE通道时所提出的DL和通道估计策略的有效性。
Low overhead channel estimation based on compressive sensing (CS) has been widely investigated for hybrid wideband millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. The channel sparsifying dictionaries used in prior work are built from ideal array response vectors evaluated on discrete angles of arrival/departure. In addition, these dictionaries are assumed to be the same for all subcarriers, without considering the impacts of hardware impairments and beam squint. In this manuscript, we derive a general channel and signal model that explicitly incorporates the impacts of hardware impairments, practical pulse shaping functions, and beam squint, overcoming the limitations of mmWave MIMO channel and signal models commonly used in previous work. Then, we propose a dictionary learning (DL) algorithm to obtain the sparsifying dictionaries embedding hardware impairments, by considering the effect of beam squint without introducing it into the learning process. We also design a novel CS channel estimation algorithm under beam squint and hardware impairments, where the channel structures at different subcarriers are exploited to enable channel parameter estimation with low complexity and high accuracy. Numerical results demonstrate the effectiveness of the proposed DL and channel estimation strategy when applied to realistic mmWave channels.
