Twenty-Five Years of Advances in Beamforming: From convex and nonconvex optimization to learning techniques

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dc.authoridHeath, Robert/0000-0002-4666-5628en_US
dc.authoridElbir, Ahmet M./0000-0003-4060-3781en_US
dc.authoridElbir, Ahmet M./0000-0003-4060-3781en_US
dc.authoridVorobyov, Sergiy/0000-0001-7249-647Xen_US
dc.authorwosidHeath, Robert/AAY-4148-2020en_US
dc.authorwosidVorobyov, Sergiy A./G-2478-2013en_US
dc.authorwosidElbir, Ahmet M./X-3731-2019en_US
dc.authorwosidElbir, Ahmet M./Q-1265-2015en_US
dc.contributor.authorElbir, Ahmet M.
dc.contributor.authorMishra, Kumar Vijay
dc.contributor.authorVorobyov, Sergiy A.
dc.contributor.authorHeath Jr, Robert W. W.
dc.date.accessioned2024-08-23T16:04:16Z
dc.date.available2024-08-23T16:04:16Z
dc.date.issued2023en_US
dc.departmentDüzce Üniversitesien_US
dc.description.abstractBeamforming is a signal processing technique to steer, shape, and focus an electromagnetic (EM) wave using an array of sensors toward a desired direction. It has been used in many engineering applications, such as radar, sonar, acoustics, astronomy, seismology, medical imaging, and communications. With the advent of multiantenna technologies in, say, radar and communication, there has been a great interest in designing beamformers by exploiting convex or nonconvex optimization methods. Recently, machine learning (ML) is also leveraged for obtaining attractive solutions to more complex beamforming scenarios. This article captures the evolution of beamforming in the last 25 years from convex to nonconvex optimization and optimization to learning approaches. It provides a glimpse into these important signal processing algorithms for a variety of transmit-receive architectures, propagation zones, propagation paths, and multidisciplinary applications.en_US
dc.description.sponsorshipU.S. National Academies of Sciences, Engineering, and Medicine via an Army Research Laboratory Harry Diamond Distinguished Fellowshipen_US
dc.description.sponsorshipKumar Vijay Mishra acknowledges support from the U.S. National Academies of Sciences, Engineering, and Medicine via an Army Research Laboratory Harry Diamond Distinguished Fellowship.en_US
dc.identifier.doi10.1109/MSP.2023.3262366
dc.identifier.endpage131en_US
dc.identifier.issn1053-5888
dc.identifier.issn1558-0792
dc.identifier.issue4en_US
dc.identifier.startpage118en_US
dc.identifier.urihttps://doi.org/10.1109/MSP.2023.3262366
dc.identifier.urihttps://hdl.handle.net/20.500.12684/14141
dc.identifier.volume40en_US
dc.identifier.wosWOS:001004238400012en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Signal Processing Magazineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArray signal processingen_US
dc.subjectShapeen_US
dc.subjectSonar applicationsen_US
dc.subjectSeismologyen_US
dc.subjectSignal processing algorithmsen_US
dc.subjectOptimization methodsen_US
dc.subjectMachine learningen_US
dc.subjectChannel Estimationen_US
dc.subjectRobusten_US
dc.subjectSignalen_US
dc.subjectMismatchen_US
dc.subjectDesignen_US
dc.subjectSystemsen_US
dc.subjectRadaren_US
dc.titleTwenty-Five Years of Advances in Beamforming: From convex and nonconvex optimization to learning techniquesen_US
dc.typeArticleen_US

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