Intelligent Design Prediction of a Circular Polarized Antenna for CubeSat Application Using Machine Learning Algorithms

Abstract

This paper presents an intelligent design method for a corner-truncated microstrip patch antenna (CTMPA) operating at 32 GHz using various well-known machine learning (ML) techniques. Our objectives are to obtain a gain of >5 dBic across a 10% bandwidth, an axial ratio (AR) of <3 dB, and a return loss of <−10 dB. First, a dataset of 715 full-wave simulated samples is analyzed with four distinct antenna characteristics (viz. features), along with the related computed |S11|, gain, and AR. Using mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and R2 score, 12 ML regression models were examined to compare the training data with the new predicted values. Next, the model that best satisfies our objectives was chosen. Results showed that the artificial neural network (ANN) followed by k-nearest neighbor (KNN) regression produced the lowest error compared to all tested ML models. The design parameters that achieved our intended objectives were computed using the predicted results. The predicted design was validated using a full-wave simulation and a prototype measurement.