Ultra-Wideband CPW Fed Band-Notched Monopole Antenna Optimization Using Machine Learning

被引：0

作者：

Ranjan P. ^{[1
]}

Maurya A. ^{[1
]}

Gupta H. ^{[1
]}

Yadav S. ^{[2
]}

Sharma A. ^{[3
]}

机构：

[1] Atal Bihari Vajpayee — Indian Institute of Information Technology & Management, Gwalior

[2] Department of Electronics and Telecommunication Engineering, College of Engineering Roorkee, Roorkee

[3] Department of Electronics and Communication Engineering, Motilal Nehru National Institute of Technology Allahabad, Prayagraj

来源：

Progress In Electromagnetics Research M | 2022年 / 108卷

关键词：

Monopole antennas;

D O I：

10.2528/PIERM21122802

中图分类号：

学科分类号：

摘要：

In this article, a compact Coplanar Waveguide (CPW) fed band-notched monopole antenna is designed and optimized. The unique feature of this article is to provide an approach for designing an antenna in the best way using machine learning techniques. Machine Learning can be used to speed up the antenna design process. There are five algorithms employed: Decision Tree, Random Forest, XGBoost Regression, K-Nearest Neighbor (KNN), and Artificial Neural Network (ANN). Among all the algorithms, KNN gives the best result with accuracy up to 98%. From the obtained result, we can estimate the dimensions of the desired parameters, which could not be done previously by High Frequency Structure Simulator (HFSS) Electromagnetic (EM) simulator. The optimized antenna design is also fabricated and tested, which confirms its frequency range between 2.9 and 21.6 GHz. Stable radiation features in between the operating frequency range makes it suitable for Ultra-Wideband (UWB) applications. © 2022, Electromagnetics Academy. All rights reserved.

引用

页码：27 / 38

页数：11

共 30 条

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