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Scalability Analysis of Programmable Metasurfaces for Beam Steering

Taghvaee, Hamidreza; Abadal, Sergi; Pitilakis, Alexandros; Tsilipakos, Odysseas; Tasolamprou, Anna C.; Liaskos, Christos; Kafesaki, Maria; Kantartzis, Nikolaos V.; Cabellos-Aparicio, Albert; Alarcon, Eduard

Authors

Hamidreza Taghvaee

Sergi Abadal

Alexandros Pitilakis

Odysseas Tsilipakos

Anna C. Tasolamprou

Christos Liaskos

Maria Kafesaki

Nikolaos V. Kantartzis

Albert Cabellos-Aparicio

Eduard Alarcon



Abstract

Programmable metasurfaces have garnered significant attention as they confer unprecedented control over the electromagnetic (EM) response of any surface. Such feature has given rise to novel design paradigms such as Software-Defined Metamaterials (SDM) and Reconfigurable Intelligent Surfaces (RIS) with multiple groundbreaking applications. However, the development of programmable metasurfaces tailored to the particularities of a potentially large application pool becomes a daunting task because
the design space becomes remarkably large. This paper aims to ease the design process by proposing a methodology that employs a semi-analytical formulation to model the response of a metasurface and, then, derives performance scaling trends as functions of a representative set of design and application-specific variables. Although the methodology is amenable to any EM functionality, this paper explores its use for the case of beam steering at 26 GHz for 5G applications. Conventional beam steering metrics are evaluated as functions of the unit cell size, number of unit cell states, and metasurface size for different incidence and reflection angles. It is shown that metasurfaces 5λ × 5λ or larger with unit cells of λ/3 and four unit cell states ensure good performance overall. Further, it is demonstrated that performance degrades significantly for angles larger than θ > 60o and that, to combat this, extra effort is needed in the development of the unit cell. These performance trends, when combined with power and cost models, will pave the way to optimal
metasurface dimensioning.

Journal Article Type Article
Acceptance Date May 16, 2020
Online Publication Date Jun 5, 2020
Publication Date Jun 5, 2020
Deposit Date Jan 24, 2023
Publicly Available Date Jan 25, 2023
Electronic ISSN 2169-3536
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 8
Pages 105320-105334
DOI https://doi.org/10.1109/access.2020.3000424
Keywords General Engineering; General Materials Science; General Computer Science
Public URL https://nottingham-repository.worktribe.com/output/13465521
Publisher URL https://ieeexplore.ieee.org/document/9109701

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