Machine Learning Hexagonal Monopole Antenna using Linear Regression Algorithm

In this paper Linear Regression algorithm has been used to optimize the side length of monopole antenna designed for use in Wireless Local Area Network. The aim of this paper is to use Machine learning to find the optimum dimension of the antenna for minimum return loss. The dataset obtained from simulation was trained using a one to one strategy that is one label for one feature and this was looped so as to cover the entire dataset. The data was tested on various machine learning algorithms such as linear regression, lasso algorithms, but due to limitation of data size and computational factors the linear regression algorithm worked out the best. The algorithm is implemented in python and agrees with the simulated result.