Performance Enhancement of MmWave MIMO Systems Using Deep Learning Framework

In order to obtain beamforming gains and prevent high pathloss in millimeter wave (mmWave) systems, large number of antennas is employed. Digital precoders are difficult to implement with many antennas because of hardware constraints, while analog precoders have limited performance. In this paper, h...

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Published in:	IEEE access Vol. 9; p. 1
Main Authors:	Faragallah, Osama S., El-sayed, Hala S., El-Mashed, Mohamed G.
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01.01.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Antennas Array signal processing Beamforming Covariance matrix Decoder Decoders Deep learning Digital precoders Hardware HybridPrecodingNet Millimeter waves mmWave MmWave and Spectral Efficiency Neural networks Optimization Performance enhancement Precoding Radio frequency Spectral efficiency
ISSN:	2169-3536, 2169-3536
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Abstract	In order to obtain beamforming gains and prevent high pathloss in millimeter wave (mmWave) systems, large number of antennas is employed. Digital precoders are difficult to implement with many antennas because of hardware constraints, while analog precoders have limited performance. In this paper, hybrid precoding based on a deep learning framework, HybridPrecodingNet, is proposed, which uses large-scale information to predict the parameters of hybrid precoders and decoders. The statistics of the channel covariance matrix are applied to design the hybrid precoders and decoders. The proposed HybridPrecodingNet at the receiver is applied for the channel estimation and design of hybrid decoders. In our proposed framework, the structure of HybridPrecodingNet is trained to learn how to optimize the hybrid precoder and decoder for maximum spectral efficiency. Comparison between different precoding techniques is provided. Results show that HybridPrecodingNet approaches the sub-optimal solution and gives significant spectral efficiency enhancement.
AbstractList	In order to obtain beamforming gains and prevent high pathloss in millimeter wave (mmWave) systems, large number of antennas is employed. Digital precoders are difficult to implement with many antennas because of hardware constraints, while analog precoders have limited performance. In this paper, hybrid precoding based on a deep learning framework, HybridPrecodingNet, is proposed, which uses large-scale information to predict the parameters of hybrid precoders and decoders. The statistics of the channel covariance matrix are applied to design the hybrid precoders and decoders. The proposed HybridPrecodingNet at the receiver is applied for the channel estimation and design of hybrid decoders. In our proposed framework, the structure of HybridPrecodingNet is trained to learn how to optimize the hybrid precoder and decoder for maximum spectral efficiency. Comparison between different precoding techniques is provided. Results show that HybridPrecodingNet approaches the sub-optimal solution and gives significant spectral efficiency enhancement.
Author	El-Mashed, Mohamed G. Faragallah, Osama S. El-sayed, Hala S.
Author_xml	– sequence: 1 givenname: Osama S. surname: Faragallah fullname: Faragallah, Osama S. organization: Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia – sequence: 2 givenname: Hala S. surname: El-sayed fullname: El-sayed, Hala S. organization: Department of Electrical Engineering, Faculty of Engineering, Menoufia University, Shebin El-Kom 32511, Egypt. (e-mail: hall_hhh@yahoo.com) – sequence: 3 givenname: Mohamed G. surname: El-Mashed fullname: El-Mashed, Mohamed G. organization: Department of Electronics and Electrical Communications, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, Egypt
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SubjectTerms	Antennas Array signal processing Beamforming Covariance matrix Decoder Decoders Deep learning Digital precoders Hardware HybridPrecodingNet Millimeter waves mmWave MmWave and Spectral Efficiency Neural networks Optimization Performance enhancement Precoding Radio frequency Spectral efficiency
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Title	Performance Enhancement of MmWave MIMO Systems Using Deep Learning Framework
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