Application of machine learning-assisted global optimization for improvement in design and performance of open resonant cavity antenna

Dutta, K., Akinsolu, M. O., Mishra, P. K., Liu, B. and Guha, D. (2024) Application of machine learning-assisted global optimization for improvement in design and performance of open resonant cavity antenna. IEEE Open Journal of Antennas and Propagation, (doi: 10.1109/OJAP.2024.3385675) (Early Online Publication)

Abstract

Open resonant cavity antenna (ORCA) and its recent advances promise attractive features and possible applications, although the designs reported so far are solely based on the classical electromagnetic (EM) theory and general perception of EM circuits. This work explores machine learning (ML)-assisted antenna design techniques aiming to improve and optimize its major radiation parameters over the maximum achievable operating bandwidth. A state-of-the-art method e.g., parallel surrogate model-assisted hybrid differential evolution for antenna synthesis (PSADEA) has been exercised upon a reference ORCA geometry revealing a fascinating outcome. This modifies the shape of the cavity which was not predicted by EM-based analysis as well as promising significant improvement in its radiation properties. The PSADEA-generated design has been experimentally verified indicating 3dB-11dB improvement in sidelobe level along with high broadside gain maintained above 17 dBi over the 18.5% impedance bandwidth of the ORCA. The new design has been theoretically interpreted by the theory of geometrical optics (GO). This investigation demonstrates the potential and possibilities of employing artificial intelligence (AI)-based techniques in antenna design where multiple parameters need to be adjusted simultaneously for the best possible performances.

• Item Type: Articles
• Additional Information: One of the authors (DG) would like to thank the Indian National Academy of Engineering (INAE)/Department of Science and Technology (DST), Government of India for awarding the Abdul Kalum Technology Innovation National Fellowship and associated research grant for antenna developments.
• Status: Early Online Publication
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Liu, Professor Bo
• Authors: Dutta, K., Akinsolu, M. O., Mishra, P. K., Liu, B., and Guha, D.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
• Journal Name: IEEE Open Journal of Antennas and Propagation
• Publisher: IEEE
• ISSN: 2637-6431
• ISSN (Online): 2637-6431
• Published Online: 04 April 2024
• Copyright Holders: Copyright © 2024 The Authors
• First Published: First published in IEEE Open Journal of Antennas and Propagation 2024
• Publisher Policy: Reproduced under a Creative Commons licence