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Radiation Pattern Prediction for Metasurfaces: A Neural Network-Based Approach

dc.contributor.authorHamidreza Taghvaee
dc.contributor.authorAkshay Jain
dc.contributor.authorXavier Timoneda
dc.contributor.authorChristos Liaskos
dc.contributor.authorSergi Abadal
dc.contributor.authorEduard Alarcón
dc.contributor.authorAlbert Cabellos-Aparicio
dc.contributor.otherNaNoNetworking Center in Catalonia (N3Cat), Universitat Politècnica de Catalunya, 08034 Barcelona, Spain
dc.contributor.otherNaNoNetworking Center in Catalonia (N3Cat), Universitat Politècnica de Catalunya, 08034 Barcelona, Spain
dc.contributor.otherNaNoNetworking Center in Catalonia (N3Cat), Universitat Politècnica de Catalunya, 08034 Barcelona, Spain
dc.contributor.otherFoundation for Research and Technology Hellas, 71110 Heraklion, Greece
dc.contributor.otherNaNoNetworking Center in Catalonia (N3Cat), Universitat Politècnica de Catalunya, 08034 Barcelona, Spain
dc.contributor.otherNaNoNetworking Center in Catalonia (N3Cat), Universitat Politècnica de Catalunya, 08034 Barcelona, Spain
dc.contributor.otherNaNoNetworking Center in Catalonia (N3Cat), Universitat Politècnica de Catalunya, 08034 Barcelona, Spain
dc.date.accessioned2021-04-15T00:03:29Z
dc.date.available2025-10-02T05:28:31Z
dc.date.issued01-04-2021
dc.identifier.issn-
dc.identifier.urihttps://www.mdpi.com/1424-8220/21/8/2765
dc.description.abstractAs the current standardization for the 5G networks nears completion, work towards understanding the potential technologies for the 6G wireless networks is already underway. One of these potential technologies for the 6G networks is reconfigurable intelligent surfaces. They offer unprecedented degrees of freedom towards engineering the wireless channel, i.e., the ability to modify the characteristics of the channel whenever and however required. Nevertheless, such properties demand that the response of the associated metasurface is well understood under all possible operational conditions. While an understanding of the radiation pattern characteristics can be obtained through either analytical models or full-wave simulations, they suffer from inaccuracy and extremely high computational complexity, respectively. Hence, in this paper, we propose a neural network-based approach that enables a fast and accurate characterization of the metasurface response. We analyze multiple scenarios and demonstrate the capabilities and utility of the proposed methodology. Concretely, we show that this method can learn and predict the parameters governing the reflected wave radiation pattern with an accuracy of a full-wave simulation (98.8–99.8%) and the time and computational complexity of an analytical model. The aforementioned result and methodology will be of specific importance for the design, fault tolerance, and maintenance of the thousands of reconfigurable intelligent surfaces that will be deployed in the 6G network environment.
dc.format-
dc.language.isoEN
dc.publisherMDPI AG
dc.relation.uri['https://www.springernature.com/gp/open-research/policies/journal-policies/apc-waiver-countries', 'https://bioelecmed.biomedcentral.com/submission-guidelines', 'https://bioelecmed.biomedcentral.com/about', 'https://bioelecmed.biomedcentral.com/']
dc.rightsCC BY
dc.subject['biotechnology', 'neuroscience', 'molecular medicine', 'biomedical engineering', 'Medical technology', 'R855-855.5']
dc.subject.lccChemical technology
dc.titleRadiation Pattern Prediction for Metasurfaces: A Neural Network-Based Approach
dc.typeArticle
dc.description.keywordsmetasurface
dc.description.keywordsmachine learning
dc.description.keywordsneural networks
dc.description.keywordsbeam steering
dc.description.keywordsradiation pattern
dc.description.keywords5G and beyond
dc.description.pages-
dc.description.doi10.3390/s21082765
dc.title.journalSensors
dc.identifier.e-issn1424-8220
dc.identifier.oaioai:doaj.org/journal:826a9cab2f914134b04821b74a01f5a4
dc.journal.infoVolume 21, Issue 8


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