Low-complexity deep learning-based beamforming in MISO systems

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dc.authorid0000-0002-9193-1374
dc.authorid0000-0002-6842-1528
dc.authorid0000-0002-9054-0005
dc.contributor.authorThet, Nann Win Moe
dc.contributor.authorElgammal, Khaled Walid
dc.contributor.authorAteş, Hasan Fehmi
dc.contributor.authorÖzdemir, Mehmet Kemal
dc.date.accessioned2021-08-13T07:16:25Z
dc.date.available2021-08-13T07:16:25Z
dc.date.issued2021
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü
dc.description.abstractThis study proposes a low-complexity deep learning-based beamforming neural network (BFNN) for massive multiple-input single-output (MISO) systems. We adopt an unsupervised learning-based convolutional neural network (CNN) model. The network is trained to obtain an analog phase shifters (PSs)-based beamforming vector of a given user by maximizing the system spectral efficiency (SE) while maintaining the transmitted power constraint. The channel state information (CSI) for millimeter wave (mmWave) channel and signal-to-noise-ratio (SNR) are used as inputs to the network. We also proposed a novel input feeding arrangement to the network and assessed its performance by using different input data representations. Simulation results show that the CNN-BFNN has the lowest complexity compared to a fully connected neural network (FCNN) and the existing conventional algorithms. Furthermore, the CNN model with fast Fourier transform (FFT) input provides the highest SE performance among all other input data representations.
dc.identifier.citationThet, N. W. M., Elgammal, K. W., Ateş, H. F. ve Özdemir, M. K. (2021). Low-complexity deep learning-based beamforming in MISO systems. 29th IEEE Conference on Signal Processing and Communications Applications, SIU. Virtual, Istanbul, 9-11 June 2021. https://dx.doi.org/10.1109/SIU53274.2021.9478043
dc.identifier.doi10.1109/SIU53274.2021.9478043
dc.identifier.isbn9781665436496
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://dx.doi.org/10.1109/SIU53274.2021.9478043
dc.identifier.urihttps://hdl.handle.net/20.500.12511/7810
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof29th IEEE Conference on Signal Processing and Communications Applications, SIUen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectBeamforming Neural Network (BFNN)
dc.subjectConvolutional Neural Network (CNN)
dc.subjectDeep Learning (DL)
dc.subjectMillimeter Wave (mmWave)
dc.subjectMultiple-Input Single-Output (MISO)
dc.titleLow-complexity deep learning-based beamforming in MISO systems
dc.typeConference Object

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