AESA Antennas using Machine Learning with Reduced Dataset
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Date
2024-09
Authors
Zaib, A.
Masood, A. R.
Abdullah, M. A.
Khattak, S.
Saleem, A. B.
Ullah, I.
ORCID
Advisor
Referee
Mark
Journal Title
Journal ISSN
Volume Title
Publisher
Radioengineering Society
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Abstract
This paper proposes a deep neural network (DNN)-based approach for radiation pattern synthesis of 8 elements phased array antenna. For this purpose, 181 points of a desired radiation pattern are fed as input to the DNN and phases of array elements are extracted as the outputs. Existing DNN techniques for radiation pattern synthesis are not directly applicable to higher-order arrays as the dataset size grows exponentially with array dimensions. To overcome this bottleneck, we propose novel and efficient methods of generating datasets for DNN. Specifically, by leveraging the constant phase-shift characteristic of the phased array antenna, dataset size is reduced by several orders of magnitude and made independent of the array size. This has considerable advantages in terms of speed and complexity, especially in real-time applications as the DNN can immediately learn and synthesize the desired patterns. The performance of the proposed methods is validated by using an ideal square beam and an optimal array pattern as reference inputs to the DNN. The results generated in MATLAB as well as in CST, demonstrate the effectiveness of the proposed methods in synthesizing the desired radiation patterns.
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Citation
Radioengineering. 2024 vol. 33, iss. 3, s. 397-405. ISSN 1210-2512
https://www.radioeng.cz/fulltexts/2024/24_03_0397_0405.pdf
https://www.radioeng.cz/fulltexts/2024/24_03_0397_0405.pdf
Document type
Peer-reviewed
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Published version
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Language of document
en