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Convolutional neural network for 2D adaptive beamforming of phased array antennas with robustness to array imperfections

Published online by Cambridge University Press:  05 July 2021

Tarek Sallam Open the ORCID record for Tarek Sallam [Opens in a new window]  and
Ahmed M. Attiya
Tarek Sallam*
Affiliation:
Faculty of Electronic and Information Engineering, Huaiyin Institute of Technology, Huai'an 223002, Jiangsu, China Faculty of Engineering at Shoubra, Benha University, Cairo, Egypt
Ahmed M. Attiya
Affiliation:
Microwave Engineering Department, Electronics Research Institute (ERI), Cairo, Egypt
*
Author for correspondence: Tarek Sallam, E-mail: tarek.sallam@feng.bu.edu.eg
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Abstract

Achieving robust and fast two-dimensional adaptive beamforming of phased array antennas is a challenging problem due to its high-computational complexity. To address this problem, a deep-learning-based beamforming method is presented in this paper. In particular, the optimum weight vector is computed by modeling the problem as a convolutional neural network (CNN), which is trained with I/O pairs obtained from the optimum Wiener solution. In order to exhibit the robustness of the new technique, it is applied on an 8 × 8 phased array antenna and compared with a shallow (non-deep) neural network namely, radial basis function neural network. The results reveal that the CNN leads to nearly optimal Wiener weights even in the presence of array imperfections.

Keywords

2D adaptive beamformingarray imperfectionsconvolutional neural networkdeep learningphased array antenna

Information

Type
Antenna Design, Modelling and Measurements
Information
International Journal of Microwave and Wireless Technologies , Volume 13 , Issue 10 , December 2021 , pp. 1096 - 1102
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press in association with the European Microwave Association

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