A Deep Learning Framework for Hybrid Beamforming Without Instantaneous CSI Feedback
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Dosyalar
Tarih
2020
Yazarlar
Dergi Başlığı
Dergi ISSN
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Yayıncı
Ieee-Inst Electrical Electronics Engineers Inc
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Hybrid 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.
Açıklama
Anahtar Kelimeler
Standards, Smoothing methods, Uncertainty, Bills of materials, Time measurement, Deep learning, online learning, channel estimation, hybrid precoding, instantaneous feedback, Millimeter-Wave Communications, Massive Mimo Systems, Channel Estimation, Antenna Selection, Combiner Design, Phase Shifters, Precoder, Optimization
Kaynak
Ieee Transactions On Vehicular Technology
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
69
Sayı
10
