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• W3173681330 abstract "In this paper, we present an energy-efficient joint machine learning based beam-user selection and low complexity hybrid beamforming for the multiuser massive multiple-input multiple-output (MIMO) downlink system. Massive MIMO system is a key enabler of <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink> <tex-math notation=LaTeX>$5^{th}$ </tex-math></inline-formula> generation (5G) technology which is being used in vehicle-to-everything (V2X) communications and other Internet-of-Things (IoT). The large number of antennas at the base-station side causes high power consumption in the radio frequency (RF) chain. Hybrid beamforming techniques are used to reduce the number of RF chains with minimal performance loss. This paper proposes a low complexity orthogonal hybrid beamforming design with the aid of a machine learning based beam-user selection scheme. The <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>Householder</i> (HH) reflectors are used to generate the orthogonal analog beamforming (ABF) matrix. We use a feedforward neural network (FFNN) beam-user selection scheme. The proposed HH ABF + FFNN scheme provides better energy-efficiency (EE) performance in the ill-conditioned massive MIMO channel. It has been shown that the green SE-EE point (41.35 <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>b/s/Hz</i> , 0.8 <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>b/Hz/J</i> ) can be obtained by the proposed HH ABF + FFNN scheme as compared to the (33.77 <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>b/s/Hz</i> , 0.63 <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>b/Hz/J</i> ) from the state-of-the-art Discrete Fourier transform based techniques." @default.
• W3173681330 created "2021-07-05" @default.
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• W3173681330 date "2021-12-01" @default.
• W3173681330 modified "2023-09-25" @default.
• W3173681330 title "Machine Learning Based Beam Selection With Low Complexity Hybrid Beamforming Design for 5G Massive MIMO Systems" @default.
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• W3173681330 doi "https://doi.org/10.1109/tgcn.2021.3093439" @default.
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