Joint Antenna Selection and Hybrid Beamformer Design Using Unquantized and Quantized Deep Learning Networks

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dc.authoridElbir, Ahmet M./0000-0003-4060-3781
dc.authorwosidElbir, Ahmet M./X-3731-2019
dc.contributor.authorElbir, Ahmet M.
dc.contributor.authorMishra, Kumar Vijay
dc.date.accessioned2021-12-01T18:48:25Z
dc.date.available2021-12-01T18:48:25Z
dc.date.issued2020
dc.department[Belirlenecek]en_US
dc.description.abstractIn millimeter-wave communications, multiple-input-multiple-output (MIMO) systems use large antenna arrays to achieve high gain and spectral efficiency. These massive MIMO systems employ hybrid beamformers to reduce power consumption associated with fully digital beamforming in large arrays. Further savings in cost and power are possible through the use of subarrays. Unlike prior works that resort to large latency methods such as optimization and greedy search for subarray selection, we propose a deep-learning-based approach in order to overcome the complexity issue without causing significant performance loss. We formulate antenna selection and hybrid beamformer design as a classification/prediction problem for convolutional neural networks (CNNs). For antenna selection, the CNN accepts the channel matrix as input and outputs a subarray with optimal spectral efficiency. The resultant subarray channel matrix is then again fed to a CNN to obtain analog and baseband beamformers. We train the CNNs with several noisy channel matrices that have different channel statistics in order to achieve a robust performance at the network output. Numerical experiments show that our CNN framework provides an order better spectral efficiency and is 10 times faster than the conventional techniques. Further investigations with quantized-CNNs show that the proposed network, saved in no more than 5 bits, is also suited for digital mobile devices.en_US
dc.identifier.doi10.1109/TWC.2019.2956146
dc.identifier.endpage1688en_US
dc.identifier.issn1536-1276
dc.identifier.issn1558-2248
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85081747109en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1677en_US
dc.identifier.urihttps://doi.org/10.1109/TWC.2019.2956146
dc.identifier.urihttps://hdl.handle.net/20.500.12684/10529
dc.identifier.volume19en_US
dc.identifier.wosWOS:000521186100016en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Transactions On Wireless Communicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAntenna arraysen_US
dc.subjectPhase shiftersen_US
dc.subjectRadio frequencyen_US
dc.subjectMIMO communicationen_US
dc.subjectReceiving antennasen_US
dc.subjectOptimizationen_US
dc.subjectAntenna selectionen_US
dc.subjectCNNen_US
dc.subjectdeep learningen_US
dc.subjecthybrid beamformingen_US
dc.subjectmassive MIMOen_US
dc.subjectMassive Mimoen_US
dc.subjectChannel Estimationen_US
dc.subjectCombiner Designen_US
dc.subjectPhase Shiftersen_US
dc.subjectPrecoderen_US
dc.subjectArchitecturesen_US
dc.subjectTrackingen_US
dc.subjectPoweren_US
dc.titleJoint Antenna Selection and Hybrid Beamformer Design Using Unquantized and Quantized Deep Learning Networksen_US
dc.typeArticleen_US

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