Shuai S. A. Yuan, Li Wei, Xiaoming Chen, Chongwen Huang, Wei E. I. Sha
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引用次数: 0
Abstract
Holographic multiple-input and multiple-output (MIMO) communications
introduce innovative antenna array configurations, such as dense arrays and
volumetric arrays, which offer notable advantages over conventional planar
arrays with half-wavelength element spacing. However, accurately assessing the
performance of these new holographic MIMO systems necessitates careful
consideration of channel matrix normalization, as it is influenced by array
gain, which, in turn, depends on the array topology. Traditional normalization
methods may be insufficient for assessing these advanced array topologies,
potentially resulting in misleading or inaccurate evaluations. In this study,
we propose electromagnetic normalization approaches for the channel matrix that
accommodate arbitrary array topologies, drawing on the array gains from
analytical, physical, and full-wave methods. Additionally, we introduce a
normalization method for near-field MIMO channels based on a rigorous dyadic
Green's function approach, which accounts for potential losses of gain at near
field. Finally, we perform capacity analyses under quasi-static, ergodic, and
near-field conditions, through adopting the proposed normalization techniques.
Our findings indicate that channel matrix normalization should reflect the
realized gains of the antenna array in target directions. Failing to accurately
normalize the channel matrix can result in errors when evaluating the
performance limits and benefits of unconventional holographic array topologies,
potentially compromising the optimal design of holographic MIMO systems.
全息多输入多输出(MIMO)通信引入了创新的天线阵列配置,如密集阵列和体积阵列,与具有半波长元件间距的传统平面阵列相比具有显著优势。然而,要准确评估这些新型全息多输入多输出系统的性能,就必须仔细考虑信道矩阵归一化问题,因为它受到阵列增益的影响,而阵列增益又取决于阵列拓扑结构。传统的归一化方法可能不足以评估这些先进的阵列拓扑结构,可能导致误导或不准确的评估。在本研究中,我们利用分析、物理和全波方法中的阵列增益,提出了适应任意阵列拓扑的信道矩阵电磁归一化方法。此外,我们还基于严格的二元格林函数方法,为近场 MIMO 信道引入了归一化方法,该方法考虑了近场增益的潜在损失。最后,通过采用所提出的归一化技术,我们对准静态、遍历和近场条件下的容量进行了分析。在评估非常规全息阵列拓扑的性能极限和优势时,如果不能准确归一化信道矩阵,就会导致错误,从而可能影响全息多输入多输出系统的优化设计。