AESA Antennas using Machine Learning with Reduced Dataset

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dc.contributor.authorZaib, A.
dc.contributor.authorMasood, A. R.
dc.contributor.authorAbdullah, M. A.
dc.contributor.authorKhattak, S.
dc.contributor.authorSaleem, A. B.
dc.contributor.authorUllah, I.
dc.coverage.issue3cs
dc.coverage.volume33cs
dc.date.accessioned2025-04-04T11:12:55Z
dc.date.available2025-04-04T11:12:55Z
dc.date.issued2024-09cs
dc.description.abstractThis paper proposes a deep neural network (DNN)-based approach for radiation pattern synthesis of 8 elements phased array antenna. For this purpose, 181 points of a desired radiation pattern are fed as input to the DNN and phases of array elements are extracted as the outputs. Existing DNN techniques for radiation pattern synthesis are not directly applicable to higher-order arrays as the dataset size grows exponentially with array dimensions. To overcome this bottleneck, we propose novel and efficient methods of generating datasets for DNN. Specifically, by leveraging the constant phase-shift characteristic of the phased array antenna, dataset size is reduced by several orders of magnitude and made independent of the array size. This has considerable advantages in terms of speed and complexity, especially in real-time applications as the DNN can immediately learn and synthesize the desired patterns. The performance of the proposed methods is validated by using an ideal square beam and an optimal array pattern as reference inputs to the DNN. The results generated in MATLAB as well as in CST, demonstrate the effectiveness of the proposed methods in synthesizing the desired radiation patterns.en
dc.formattextcs
dc.format.extent397-405cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationRadioengineering. 2024 vol. 33, iss. 3, s. 397-405. ISSN 1210-2512cs
dc.identifier.doi10.13164/re.2024.0397en
dc.identifier.issn1210-2512
dc.identifier.urihttps://hdl.handle.net/11012/250714
dc.language.isoencs
dc.publisherRadioengineering Societycs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttps://www.radioeng.cz/fulltexts/2024/24_03_0397_0405.pdfcs
dc.rightsCreative Commons Attribution 4.0 International licenseen
dc.rights.accessopenAccessen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectMachine learningen
dc.subjectneural networksen
dc.subjectdeep neural networksen
dc.subjectactive electronically scanned arrayen
dc.subjectphased arrayen
dc.subjectarray patternen
dc.subjectComputer Simulation Technologyen
dc.titleAESA Antennas using Machine Learning with Reduced Dataseten
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta elektrotechniky a komunikačních technologiícs
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