论文标题

使用散射参数网络分析的可重构智能表面的建模和架构设计

Modeling and Architecture Design of Reconfigurable Intelligent Surfaces Using Scattering Parameter Network Analysis

论文作者

Shen, Shanpu, Clerckx, Bruno, Murch, Ross

论文摘要

可重新配置的智能表面(RISS)是一种未来无线通信的新兴技术。关于RIS的最新研究的绝大多数都集中在系统级别的优化上。但是,开发适合RIS辅助通信建模的直接且可触及的电磁模型仍然是一个空旷的问题。在本文中,我们通过使用严格的散射参数网络分析来解决此问题并得出通信模型。我们还提出了基于组和完全连接的可重新配置阻抗网络的新RIS架构,这些网络不仅可以调整阶段,而且可以调整撞击波的幅度,而撞击波的幅度比传统的单一连接的可重构网络更一般,更有效,仅能调整撞击波的阶段。此外,还得出了带有可重构阻抗网络的RIS辅助系统接收的信号功率的缩放定律。与单个连接的可重构阻抗网络相比,我们的组和完全连接的可重新配置阻抗网络可以将接收到的信号功率提高高达62%,或者保持相同的接收信号功率,其中许多RIS元素降低了21%。我们还研究了具有距离依赖的路径和里奇亚式褪色通道的部署中提出的架构,并表明所提出的组和完全连接的可重新配置阻抗网络的表现分别优于单个连接的情况,最多高达34%和48%。

Reconfigurable intelligent surfaces (RISs) are an emerging technology for future wireless communication. The vast majority of recent research on RIS has focused on system level optimizations. However, developing straightforward and tractable electromagnetic models that are suitable for RIS aided communication modeling remains an open issue. In this paper, we address this issue and derive communication models by using rigorous scattering parameter network analysis. We also propose new RIS architectures based on group and fully connected reconfigurable impedance networks that can adjust not only the phases but also the magnitudes of the impinging waves, which are more general and more efficient than conventional single connected reconfigurable impedance network that only adjusts the phases of the impinging waves. In addition, the scaling law of the received signal power of an RIS aided system with reconfigurable impedance networks is also derived. Compared with the single connected reconfigurable impedance network, our group and fully connected reconfigurable impedance network can increase the received signal power by up to 62%, or maintain the same received signal power with a number of RIS elements reduced by up to 21%. We also investigate the proposed architecture in deployments with distance-dependent pathloss and Rician fading channel, and show that the proposed group and fully connected reconfigurable impedance networks outperform the single connected case by up to 34% and 48%, respectively.

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