Machine Learning Techniques for Multi-Objective Antenna Optimization (Invited)

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Machine Learning Techniques for Multi-Objective Antenna Optimization (Invited)
Title:
Machine Learning Techniques for Multi-Objective Antenna Optimization (Invited)
Journal Title:
2023 IEEE 11th Asia-Pacific Conference on Antennas and Propagation (APCAP)
Keywords:
Publication Date:
21 March 2024
Citation:
Zeng, Y., Qing, X., & Chia, M. Y.-W. (2023, November 22). Machine Learning Techniques for Multi-Objective Antenna Optimization (Invited). 2023 IEEE 11th Asia-Pacific Conference on Antennas and Propagation (APCAP). https://doi.org/10.1109/apcap59480.2023.10469802
Abstract:
Different types of microstrip antennas with non-uniform metasurfaces are designed and optimized using statistical methods with various formulations. The design objectives include impedance matching bandwidth, realized gain, and axial ratio bandwidth for linearly polarized antennas and circularly polarized antenna, respectively. The designed antennas are to operate in the sub-6 GHz frequency band of the 5G New Radio system.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - AME Young Individual Research Grants (YIRG)
Grant Reference no. : A2084c0173
Description:
© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISBN:
979-8-3503-2627-7
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