On the Design of Multibeam Digital Metasurfaces With Multiple Feeds

Abstract

In this paper, an analytical design procedure for multibeam metasurfaces with multiple feeds is presented. Considering arbitrary number of input and output beams, the required amplitude and phase distributions on the metasurface plane are theoretically derived without any optimization process. How the limitation of using only passive metasurfaces poses different challenges in the practical implementation of the amplitude profile is discussed. Then, to resolve these problems, a filtering procedure is proposed which effectively produces the desired output pattern. Moreover, the design method is verified through introducing a reflective metasurface with three input beams and sixteen output beams in arbitrary directions. It utilizes a graphene‐based unit cell providing simultaneous 2/2‐bit phase/amplitude modulation, that is, totally 16 states. For validation purposes, the analytical results are compared with numerical full‐wave simulation ones in which a good agreement between them is observed. In addition, by using the proposed design method, three different practical cases for wave manipulation functionalities, including power control, beam steering, and radar cross section (RCS) reduction capabilities are demonstrated. The presented approach paves the way for efficient design of the metasurfaces for a multitude of applications, including reconfigurable intelligent surfaces and multifunctional surfaces.

The Graphene Application for the Reconfigurable Intelligent Surface (Ris) for the Noma-Ris-Mimo System Design. Phenomenological View

“…Application of metasurfaces in communication problems has a rather long history (see [9][10][11]) and is usually defined as an artificial structure with subwavelength thickness and, as already mentioned, modeled as a Planar Array (PA) of periodic or quasi periodic subwavelength elements, certainly adjusted to determine the PA radiation properties. In [10,11] it was stated that, to achieve reconfigurable properties of the metasurfaces it is reasonable to apply metamaterials, such as graphene with local tuning of its unit cells.…”

“…Application of metasurfaces in communication problems has a rather long history (see [9][10][11]) and is usually defined as an artificial structure with subwavelength thickness and, as already mentioned, modeled as a Planar Array (PA) of periodic or quasi periodic subwavelength elements, certainly adjusted to determine the PA radiation properties. In [10,11] it was stated that, to achieve reconfigurable properties of the metasurfaces it is reasonable to apply metamaterials, such as graphene with local tuning of its unit cells. For this purpose, the "states" of a unit cell must be discretized to "convert" the metamaterial (graphene) into a digital metamaterial and the local states of each unit cell can be locally switched, introducing this metamaterial as almost ideal for RIS implementation, particularly for the terahertz frequency band [9][10][11]].…”

“…In [10,11] it was stated that, to achieve reconfigurable properties of the metasurfaces it is reasonable to apply metamaterials, such as graphene with local tuning of its unit cells. For this purpose, the "states" of a unit cell must be discretized to "convert" the metamaterial (graphene) into a digital metamaterial and the local states of each unit cell can be locally switched, introducing this metamaterial as almost ideal for RIS implementation, particularly for the terahertz frequency band [9][10][11]]. The latter is rather prospective for 6G Wireless Communications [2,3,10,11].…”

“…The latter is mainly motivated by the material published in (see also references therein) and reformulated hereafter according to the phenomenological concept. More concrete material will be presented in the next sections [9,11].…”

Image Processing Using High‐Index Dielectric Metasurfaces Based on Fano Resonance

“…[4][5][6][7][8][9][10][11][12][13][14][15][16] Since MSs provide full control of incident light characteristics including amplitude, phase, and polarization, [2] they can perform various optical functions, such as focusing, polarization, dispersion, and phase modulation, by engineering the shape, size, and arrangement of the nanostructures. [16][17][18] Moreover, they offer a promising platform for analog optical computing, as they can perform mathematical operations on the input images, such as differentiation and integration, without the need for bulky optical components or electronic circuits. Thus, as an alternative to digital computing, analog optical computing promises to solve challenging problems such as large-scale optimization and probabilistic graphical models that have found many practical applications in artificial intelligence, image processing, and social networks.…”