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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.issued2021en_US
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/SIU53274.2021.9478043en_US
dc.identifier.isbn9781665436496
dc.identifier.urihttps://dx.doi.org/10.1109/SIU53274.2021.9478043
dc.identifier.urihttps://hdl.handle.net/20.500.12511/7810
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.en_US
dc.language.isoengen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectBeamforming Neural Network (BFNN)en_US
dc.subjectConvolutional Neural Network (CNN)en_US
dc.subjectDeep Learning (DL)en_US
dc.subjectMillimeter Wave (mmWave)en_US
dc.subjectMultiple-Input Single-Output (MISO)en_US
dc.titleLow-complexity deep learning-based beamforming in MISO systemsen_US
dc.typeconferenceObjecten_US
dc.relation.ispartof29th IEEE Conference on Signal Processing and Communications Applications, SIUen_US
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümüen_US
dc.authorid0000-0002-9193-1374en_US
dc.authorid0000-0002-6842-1528en_US
dc.authorid0000-0002-9054-0005en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1109/SIU53274.2021.9478043en_US


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