Deep Learning Design for Joint Antenna Selection and Hybrid Beamforming in Massive MIMO
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Dosyalar
Tarih
2019
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ieee
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this paper, we propose a deep-learning-based for joint antenna selection and hybrid beamformer design problem in mmWave massive MIMO systems. In this respect, we treat both problems as a classification problem. We design two convolutional neural networks (CNNs) which accept the input as the channel matrix and it yields the output as the optimum antenna subarray. The selected part of channel matrix is fed to the second CNN which gives the output as the analog and baseband beamformers. We evaluate the performance of the proposed approach through numerical simulations and show that our CNN framework provides significantly better performance as compared to the conventional techniques such as orthogonal matching pursuit.
Açıklama
USNC-URSI Radio Science Meeting / IEEE International Symposium on Antennas and Propagation (AP-S) -- JUL 07-12, 2019 -- Atlanta, GA
Anahtar Kelimeler
Kaynak
2019 Ieee International Symposium On Antennas And Propagation And Usnc-Ursi Radio Science Meeting
WoS Q DeÄŸeri
N/A
