Terahertz Antenna Design Using Machine Learning Assisted Global Optimization Techniques
Zubair, Muhammad, Akinsolu, Mobayode O., Abohmra, Abdoalbaset, Imran, Muhammad A., Liu, Bo and Abbasi, Qammer H. (2023) Terahertz Antenna Design Using Machine Learning Assisted Global Optimization Techniques. In: 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (USNC-URSI), 23-28 July 2023, Portland, OR, USA.
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Abstract
In the terahertz (THz) frequency band, high path loss necessitates designing the antenna with high-gain characteristics, making it challenging to obtain an optimal design solution. This paper investigates the feasibility of artificial intelligence (AI)-driven antenna design techniques to address the challenges, specifically the Surrogate Model- assisted Differential Evolution for Antenna Synthesis (SADEA-I) algorithm. SADEA-I has been employed for the first time (to the best of our knowledge) to optimize THz antennas. The simulation results show that SADEA-I is more effective than conventional design methodologies. Additionally, it takes only 30 hours to perform the global optimization, and it is a fully automated process and does not require any initial design.
| Item Type: | Conference or Workshop Item (Paper) |
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| Keywords: | Design methodology , Simulation , Patch antennas , Conferences , Machine learning , Directive antennas , Topology Index Terms Global Optimization , Antenna Design , Global Optimization Techniques , Alternative Models , High Gain , |
| Divisions: | Applied Science, Computing and Engineering |
| Depositing User: | Hayley Dennis |
| Date Deposited: | 11 Jul 2024 12:01 |
| Last Modified: | 11 Jul 2024 12:04 |
| URI: | https://glyndwr.repository.guildhe.ac.uk/id/eprint/18189 |
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