Machine Learning Based b-Shaped Monopole Antenna for RF Energy Harvesting Applications

Abstract

In this paper, a machine learning (ML) based b-shaped multiband monopole antenna for RF energy harvesting system is presented. ML algorithms are used to optimize the antenna in order to reduce the simulation time and speed up the design process. Geometric parameters on the antenna have been determined as input in the ML model. There are two different output data: the complex and decibel magnitudes of the reflection coefficient (S11) parameters, which have been determined as output parameters. The dataset consists of 1890 samples in total. Initially, attempts were made to predict the decibel magnitude of reflection coefficient parameters using different ML algorithms, and these ML algorithms were compared with each other. Then, for the complex part of the reflection coefficient parameters, a Multi-Output regression model was applied to the real and imaginary values. The best results were obtained with the K-Nearest Neighbors algorithm for the decibel magnitude of reflection coefficient parameters, achieving a 0.14% RMSE value and a 99% R2 value. The Multi-Output Regression algorithm was applied to complex (Real, Imaginary) values, achieving an R2 value of 92.51% and a RMSE value of 0.10.