Machine Learning Techniques for Optimizing Design of Double T-Shaped Monopole Antenna
Yashika Sharma, Hao Helen Zhang, Hao Xin
Abstract
In this communication, we propose using modern machine learning (ML) techniques including least absolute shrinkage and selection operator (lasso), artificial neural networks (ANNs), and k-nearest neighbor (kNN) methods for antenna design optimization. The automated techniques are shown to provide an efficient, flexible, and reliable framework to identify optimal design parameters for a reference dual-band double T-shaped monopole antenna to achieve favorite performance in terms of its two bands, i.e., between 2.4 and 3.0 and 5.15 and 5.6 GHz. In this communication, we also present a thorough study and comparative analysis of the results predicted by these ML techniques, with the results obtained from high-frequency structure simulator (HFSS) to verify the accuracy of these techniques.