Wideband Corrugated Horn Design Based on Machine Learning Technique

Corrugated horn antennas are essential in satellite and space communication systems due to their wide bandwidth, low cross-polarization, low side lobes, and excellent return loss. In this paper, several machine learning algorithms are used and trained on CST Microwave Studio data to predict antenna design parameters. The result values achieve a wideband response below 10 dB and a gain error within <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\pm 2 \text{dBi}$</tex>. These methods offer an efficient initial point for antenna design and reduce development time.