Application of Machine Learning-Assisted Global Optimization for Improvement in Design and Performance of Open Resonant Cavity Antenna
Dutta, Koushik, Akinsolu, Mobayode O., Mishra, Puneet Kumar, Liu, Bo and Guha, Debatosh (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, 5 (3). pp. 693-704. ISSN 2637-6431
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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: | Article |
|---|---|
| Keywords: | Artificial intelligence, machine learning, high gain antenna, optimization technique, resonant cavity antenna. |
| Divisions: | Applied Science, Computing and Engineering |
| Depositing User: | Hayley Dennis |
| Date Deposited: | 13 Nov 2024 15:45 |
| Last Modified: | 14 Nov 2024 14:25 |
| URI: | https://wrexham.repository.guildhe.ac.uk/id/eprint/18243 |
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