An Efficient Method for Complex Antenna Design Based on a Self Adaptive Surrogate Model Assisted Optimization Technique

Liu, Bo, Akinsolu, Mobayode O., Song, Chaoyun, Hua, Qiang, Excell, Peter S, Xu, Qian, Huang, Yi and Imran, Ali Muhammad (2021) An Efficient Method for Complex Antenna Design Based on a Self Adaptive Surrogate Model Assisted Optimization Technique. IEEE Transactions on Antennas and Propagation, 69 (4). pp. 2302-2315. ISSN 1558-2221

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Abstract

Surrogate models are widely used in antenna design for optimization efficiency improvement. Currently, the targeted antennas often have a small number of design variables and specifications, and the surrogate model training time is short. However, modern antennas become increasingly complex, which needs much more design variables and specifications, making the training time become a new bottleneck, i.e., in some cases, even longer than electromagnetic (EM) simulation time. Therefore, a new method, called training cost reduced surrogate model-assisted hybrid differential evolution for complex antenna optimization (TR-SADEA), is presented in this article. The key innovations include: 1) a self-adaptive Gaussian process surrogate modeling method with a significantly reduced training time while mostly maintaining the antenna performance prediction accuracy and 2) a new hybrid surrogate model-assisted antenna optimization framework that reduces the training time and increases the convergence speed. An indoor base station antenna with 2G to 5G cellular bands (45 design variables and 12 specifications) and a 5G outdoor base station antenna (23 design variables and 18 specifications) are used to demonstrate TR-SADEA. Experimental results show that more than 90% of the training time and about 20% iterations (simulations and surrogate modeling) are reduced compared to a state-of-the-art method while obtaining high antenna performance.

Item Type:	Article
Keywords:	5G base station antenna, antenna design, complex antenna, computationally expensive optimization, differential evolution, Gaussian process, radial basis function, surrogate model.
Divisions:	Applied Science, Computing and Engineering
Depositing User:	Hayley Dennis
Date Deposited:	24 Jun 2021 15:58
Last Modified:	24 Jun 2021 15:58
URI:	https://wrexham.repository.guildhe.ac.uk/id/eprint/17786

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