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arXiv 2501.12752cs.ITcs.ETmath.IT
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300 GHz 频段超大规模可重构智能表面的室内信道表征

Indoor Channel Characterization with Extremely Large Reconfigurable Intelligent Surfaces at $300$ GHz

Fabio Cardoso, Sérgio Matos, Luís M. Pessoa, George C. Alexandropoulos

AI总结:

本文提出一种将超大规模可重构智能表面(RIS)纳入室内多径环境的新方法,通过全波仿真获取100×100 RIS的雷达截面,并证明其三射线模型可高效集成于射线追踪工具,且远场近似在极小距离内有效。

摘要:

可重构智能表面(RIS)技术近来被视为各种室内无线应用中的增强组件,能够实现波传播控制和覆盖扩展。然而,超大规模RIS的引入(近期在亚太赫兹频段的超高容量工业环境中被考虑)给室内信道表征带来了一些挑战。具体而言,此类RIS会贡献额外的多径分量,并且其相对于信号波长的巨大尺寸导致近场传播。为此,射线追踪方法变得相当繁琐,并且需要针对不同的RIS单元设计重新运行。在本文中,我们提出了一种新颖的方法,将RIS纳入室内多径环境,以实现高效的信道表征。我们展示了一个100×100的RIS设计,具有2比特分辨率的单元,在300 GHz处实现固定的异常反射,其雷达截面图通过全波仿真获得。结果表明,RIS的行为可以方便地近似为三射线模型,该模型可以有效地纳入现有的射线追踪工具中,并且远场近似对于距离RIS非常小的距离也是有效的。

英文摘要:

The technology of Reconfigurable Intelligent Surfaces (RISs) is lately being considered as a boosting component for various indoor wireless applications, enabling wave propagation control and coverage extension. However, the incorporation of extremely large RISs, as recently being considered for ultra-high capacity industrial environments at subTHz frequencies, imposes certain challenges for indoor channel characterization. In particular, such RISs contribute additional multipath components and their large sizes with respect to the signal wavelength lead to near-field propagation. To this end, ray tracing approaches become quite cumbersome and need to be rerun for different RIS unit cell designs. In this paper, we present a novel approach for the incorporation of RISs in indoor multipath environments towards their efficient channel characterization. An $100\times100$ RIS design with $2$-bit resolution unit cells realizing a fixed anomalous reflection at 300 GHz is presented, whose radar cross section patterns are obtained via full-wave simulations. It is showcased that the RIS behavior can be conveniently approximated by a three-ray model, which can be efficiently incorporated within available ray tracing tools, and that the far-field approximation is valid for even very small distances from the RIS.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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