Optimization of a Metasurface Antenna Composed of Dual T-shaped Antenna Elements Based On Machine Learning

Li Zhang, Lijia Chen, Zhuli Yuan, Shengchang Lan
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引用次数: 1

Abstract

In the antenna design process, traditional electromagnetic full-wave simulation requires too much time and computing resources. As an emerging technology, machine learning(ML) is applied in the field of antenna design, with good performance, and can solve the problems in the above-mentioned traditional methods. This paper proposes a metasurface antenna composed of dual T-shaped antenna elements, and uses deep learning methods to optimize its geometric parameters. The resonant frequency of the proposed antenna is 7.9GHz and 13GHz. The gain in the 13GHz can reach 16.58dBi .
基于机器学习的双t形天线元表面天线优化
在天线设计过程中,传统的电磁全波仿真需要耗费过多的时间和计算资源。机器学习(ML)作为一种新兴技术,应用于天线设计领域,具有良好的性能,可以解决上述传统方法存在的问题。本文提出了一种由双t形天线单元组成的超表面天线,并利用深度学习方法对其几何参数进行优化。该天线的谐振频率为7.9GHz和13GHz。13GHz时的增益可达16.58dBi。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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