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General Information
ISSN:
1796-2021 (Online); 2374-4367 (Print)
Abbreviated Title:
J. Commun.
Frequency:
Monthly
DOI:
10.12720/jcm
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2022
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Editor-in-Chief
Prof. Maode Ma
College of Engineering, Qatar University, Doha, Qatar
I'm very happy and honored to take on the position of editor-in-chief of JCM, which is a high-quality journal with potential and I'll try my every effort to bring JCM to a next level...
[Read More]
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2024-04-17
Volume 19, No. 3 has been indexed by Scopus.
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2023
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Volume 18, No. 10, October 2023
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JCM 2023 Vol.18(10):629-642
Doi: 10.12720/jcm.18.10.629-642
Application of Machine Learning Techniques in Phased Array Antenna Synthesis: A Comprehensive Mini Review
Mohammad Reza Ghaderi * and Nasrin Amiri
Department of Electrical Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran;
Email: n_amiri@azad.ac.ir (N.A.)
*Correspondence: st_mr_ghaderi@azad.ac.ir (M.R.G.)
Manuscript received May 11, 2023; revised June 28, 2023; accepted July 4, 2023.
Abstract
—With the rapid development of modern communication systems, phased array antennas (PAAs) are widely used in many applications such as radars and 5G networks. In a PAA composed of multiple elements (antennas), beamforming or beam steering can be achieved by adjusting the phase difference in the excitation signals that feed each element of the array, eliminating the need for mechanical antenna movement. The performance quality of the communication systems heavily relies on the precise synthesis of the PAAs. PAA synthesis entails determining the geometric or physical shape of an antenna based on knowledge of its electrical parameters. Conventional methods for PAA synthesis use conventional electromagnetic models embedded in antenna design software’s. However, these models often pose challenges due to resource-intensive computations, lengthy simulation times, and potential high error rates. Machine learning (ML) techniques can be employed to optimize solutions in various telecommunication systems, including PAAs synthesis. In this article, we review and investigate the application of ML techniques in the synthesis of PAAs. The results of this study show that utilizing ML techniques can expedite the design process by threefold, while simultaneously reducing errors and increasing accuracy up to 99%.
Keywords—
phased array antenna, machine learning, deep learning, artificial neural networks
Cite: Mohammad Reza Ghaderi * and Nasrin Amiri, “Application of Machine Learning Techniques in Phased Array Antenna Synthesis: A Comprehensive Mini Review," Journal of Communications vol. 18, no. 10, pp. 629-642, October 2023.
Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License (
CC BY-NC-ND 4.0
), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.
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