Design of Neural-Network-Based Mutual Coupling Compensators for Printed Planar Antenna Arrays

被引：1

作者：

Alzahed, Abdelelah M. ^{[1
]}

Antar, Yahia M. M. ^{[1
]}

Mikki, Said ^{[2
]}

机构：

[1] Royal Mil Coll Canada, Dept Elect & Comp Engn, Kingston, ON, Canada

[2] Univ New Haven, Dept Elect & Comp Engn & Comp Sci, West Haven, CT USA

来源：

2020 IEEE INTERNATIONAL SYMPOSIUM ON ANTENNAS AND PROPAGATION AND NORTH AMERICAN RADIO SCIENCE MEETING | 2020年

关键词：

D O I：

10.1109/IEEECONF35879.2020.9330214

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

We propose a methodology to compensate the effect of mutual coupling in planar antenna array arrangements using a novel electromagnetic machine learning (ML) technique. The electromagnetic behavior of the array elements and their mutual coupling interactions are described via the antenna current Green's function (ACGF). A ML artificial neural network (ANN) procedure is then applied and combined with the ACGF to optimize array ports and mitigate for mutual coupling effects.

引用

页码：1035 / 1036

页数：2

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