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Erschienen in:

10.01.2023

Deep Learning-Based Beamforming for Millimeter-Wave Systems Using Parametric ReLU Activation Function

verfasst von: Alshimaa H. Ismail, Tarek Abed Soliman, Mohamed Rihan, Moawad I. Dessouky

Erschienen in: Wireless Personal Communications | Ausgabe 2/2023

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Abstract

Beamforming design is a crucial stage in millimeter-wave systems with massive antenna arrays. We propose a deep learning network for the design of the precoder and combiner in hybrid architectures. The proposed network employs a parametric rectified linear unit (PReLU) activation function which improves model accuracy with almost no complexity cost compared to other functions. The proposed network accepts practical channel estimation input and can be trained to enhance spectral efficiency considering the hardware limitation of the hybrid design. Simulation shows that the proposed network achieves small performance improvement when compared to the same network with the ReLU activation function.

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Titel: Deep Learning-Based Beamforming for Millimeter-Wave Systems Using Parametric ReLU Activation Function
verfasst von: Alshimaa H. Ismail
Tarek Abed Soliman
Mohamed Rihan
Moawad I. Dessouky
Publikationsdatum: 10.01.2023
Verlag: Springer US
Erschienen in: Wireless Personal Communications / Ausgabe 2/2023
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI: https://doi.org/10.1007/s11277-022-10157-7

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