A Deep Learning Framework for Hybrid Beamforming Without Instantaneous CSI Feedback

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dc.authoridElbir, Ahmet M./0000-0003-4060-3781
dc.authorwosidElbir, Ahmet M./X-3731-2019
dc.contributor.authorElbir, Ahmet Musab
dc.date.accessioned2021-12-01T18:47:07Z
dc.date.available2021-12-01T18:47:07Z
dc.date.issued2020
dc.departmentDÜ, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractHybrid beamformer design plays very crucial role in the next generation millimeter-wave (mm-Wave) massive MIMO (multiple-input multiple-output) systems. Previous works assume the perfect channel state information (CSI) which results heavy feedback overhead. To lower complexity, channel statistics can be utilized such that only infrequent update of the channel information is needed. To reduce the complexity and provide robustness, in this work, we propose a deep learning (DL) framework to deal with both hybrid beamforming and channel estimation. For this purpose, we introduce three deep convolutional neural network (CNN) architectures. We assume that the base station (BS) has the channel statistics only and feeds the channel covariance matrix into a CNN to obtain the hybrid precoders. At the receiver, two CNNs are employed. The first one is used for channel estimation purposes and the another is employed to design the hybrid combiners. The proposed DL framework does not require the instantaneous feedback of the CSI at the BS. We have also investigated the online deployment of DL for channel estimation. We have shown that the proposed approach has higher spectral efficiency with comparison to the conventional techniques. The trained CNN structures do not need to be re-trained due to the changes in the propagation environment such as the deviations in the number of received paths and the fluctuations in the received path angles up to 4 degrees. Also, the proposed DL framework exhibits at least 10 times lower computational complexity as compared to the conventional optimization-based approaches.en_US
dc.identifier.doi10.1109/TVT.2020.3017652
dc.identifier.endpage11755en_US
dc.identifier.issn0018-9545
dc.identifier.issn1939-9359
dc.identifier.issue10en_US
dc.identifier.scopus2-s2.0-85094915932en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage11743en_US
dc.identifier.urihttps://doi.org/10.1109/TVT.2020.3017652
dc.identifier.urihttps://hdl.handle.net/20.500.12684/10149
dc.identifier.volume69en_US
dc.identifier.wosWOS:000584250300019en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorElbir, Ahmet M.
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Transactions On Vehicular Technologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectStandardsen_US
dc.subjectSmoothing methodsen_US
dc.subjectUncertaintyen_US
dc.subjectBills of materialsen_US
dc.subjectTime measurementen_US
dc.subjectDeep learningen_US
dc.subjectonline learningen_US
dc.subjectchannel estimationen_US
dc.subjecthybrid precodingen_US
dc.subjectinstantaneous feedbacken_US
dc.subjectMillimeter-Wave Communicationsen_US
dc.subjectMassive Mimo Systemsen_US
dc.subjectChannel Estimationen_US
dc.subjectAntenna Selectionen_US
dc.subjectCombiner Designen_US
dc.subjectPhase Shiftersen_US
dc.subjectPrecoderen_US
dc.subjectOptimizationen_US
dc.titleA Deep Learning Framework for Hybrid Beamforming Without Instantaneous CSI Feedbacken_US
dc.typeArticleen_US

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