DC Field | Value | Language |
---|---|---|
dc.contributor.author | Noh, Jaebum | - |
dc.contributor.author | Nam, Yong-Hyun | - |
dc.contributor.author | Lee, Sun-Gyu | - |
dc.contributor.author | Lee, In-Gon | - |
dc.contributor.author | Kim, Yongjune | - |
dc.contributor.author | Lee, Jeong-Hae | - |
dc.contributor.author | Rho, Junsuk | - |
dc.date.accessioned | 2023-07-11T04:40:45Z | - |
dc.date.available | 2023-07-11T04:40:45Z | - |
dc.date.created | 2022-11-04 | - |
dc.date.issued | 2022-12 | - |
dc.identifier.issn | 1569-4410 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/117949 | - |
dc.description.abstract | © 2022 Elsevier B.V.Mutual coupling between different states of active unit cells is a challenging factor that has not been considered in the design of reconfigurable transmissive or reflective metasurface antennas. In this work, we propose a gain-predicting deep neural network (GPDNN) that predicts the radiation patterns of a reconfigurable reflective metasurface (RRM) composed of a 12-by-12 one-bit active unit cell array and is used to search for the best combination of unit cell on-or-off states for beam forming. First, the GPDNN is trained to accurately predict the radiation pattern based on the combination of unit cells. Second, it is merged with a search algorithm that retrieves the best on-or-off states near the boundary of the two states determined using the conventional beam-forming calculation method. As proof of concept, the proposed scheme is employed to find the highest realized gain in five directions: (θ, φ) = (0°, 0°), (−60°, 0°), (60°, 0°), (−60°, 90°), and (60°, 90°). The proposed deep neural network–based search algorithm takes 3.27 × 10−7 seconds per design, which is considerably faster than that based on full-wave simulation (1.5 h per design). The accuracy of the proposed method is verified by comparing the predicted results with those of the full-wave simulation. Finally, the best combination of on-or-off states for each beam-forming case is experimentally verified by measuring the radiation pattern. Compared with the conventional design, the maximum gain increases up to 0.771 dB at (θ, φ) = (−60°, 0°), and the side lobe levels decrease substantially in the other cases. | - |
dc.language | English | - |
dc.publisher | Elsevier B.V. | - |
dc.relation.isPartOf | Photonics and Nanostructures - Fundamentals and Applications | - |
dc.title | Reconfigurable reflective metasurface reinforced by optimizing mutual coupling based on a deep neural network | - |
dc.type | Article | - |
dc.identifier.doi | 10.1016/j.photonics.2022.101071 | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | Photonics and Nanostructures - Fundamentals and Applications, v.52 | - |
dc.identifier.wosid | 000885886500001 | - |
dc.citation.title | Photonics and Nanostructures - Fundamentals and Applications | - |
dc.citation.volume | 52 | - |
dc.contributor.affiliatedAuthor | Noh, Jaebum | - |
dc.contributor.affiliatedAuthor | Rho, Junsuk | - |
dc.identifier.scopusid | 2-s2.0-85138150252 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.type.docType | Article | - |
dc.subject.keywordPlus | APERTURE EFFICIENCY | - |
dc.subject.keywordPlus | SURROUNDED-ELEMENT | - |
dc.subject.keywordPlus | REFLECTARRAY | - |
dc.subject.keywordPlus | DESIGN | - |
dc.subject.keywordAuthor | Beam forming | - |
dc.subject.keywordAuthor | Deep neural network | - |
dc.subject.keywordAuthor | Mutual coupling | - |
dc.subject.keywordAuthor | Reconfigurable reflective metasurface | - |
dc.relation.journalWebOfScienceCategory | Nanoscience & Nanotechnology | - |
dc.relation.journalWebOfScienceCategory | Materials Science, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Optics | - |
dc.relation.journalWebOfScienceCategory | Physics, Applied | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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