Machine Learning-Based Prediction of S11 for a 5G Antenna Using Gaussian Process Regression

Abstract

This paper presents 5G antenna design with an approach machine learning-based to predict the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{S 1 1}$</tex> parameter. The input data, generated using ANSYS HFSS, includes the final geometric parameters such as patch antenna dimensions and slots. Three regression models used, Fourier Series Regression (FSR), Sum Sine Regression (SSR), and Gaussian Process Regression (GPR), were used to predict the S11 parameter. The first model, FSR, achieved a with Coefficient of Determination (R2) of 0.9444, Root Mean Squared Error (RMSE) equal 0.9801, Sum of Squared Errors (SSE) of 108.5365. than the second model SSR, R2 of 0.9131, RMSE of 1.2593, SSE of 169.6838. the final model GPR is the best model for predicting S11 with R2 of 0.9886, RMSE of 0.4374, and SSE of 22.1961. FSR and SSR also showed excellent performance, qualifying them for regular data analysis. The study demonstrates that machine learning can accurately predict antenna performance, offering a reliable alternative to traditional simulation techniques.