Efficient modelling of compact microstrip antenna using machine learning

• Published in: International journal of electronics and communications Vol. 135; p. 153739
• Main Authors: Sharma, Kanhaiya, Pandey, Ganga Prasad
• Format: Journal Article
• Language: English
• Published: Elsevier GmbH 01.06.2021
• Subjects: Compact antenna; Gaussian Process Regression; Machine learning; Microstrip antenna; Gaussian Process Regression; Compact antenna; Microstrip antenna; Machine learning
• ISSN: 1434-8411; 1618-0399
• DOI: 10.1016/j.aeue.2021.153739

In this article, an application of regression-based machine learning (ML) approaches to compute resonant frequency at dominant mode TM10, slot dimensions of square patch, and patch dimensions of compact microstrip antenna (SPCMA) in the frequency band of 0.4856–7.8476 GHz is presented. In the design process, a squared patch microstrip antenna with two identical slots at the opposite side of a radiating edge of the antenna is loaded. The resonant frequencies of three thousand eight hundred and twenty-two SPCMAs have simulated with CST microwave studio 2019 by varying slot size, the thickness of the material, patch length, and dielectric materials is in accordance with specification of VHF, ULF, L, S, and C band applications. A comparison of 20 regression-based machine learning algorithms including artificial neural network is presented, and it is observed that the Gaussian Process Regression(GPR) model predicts physical or electrical parameters more accurately. The proposed GPR model is validated by fabricating and characterizing a prototype of a microstrip antenna. The fabricated antenna performance is very close to the designed antenna and predicted by GPR.