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In this communication, a metasurface-loaded ceramic-based dual port antenna is designed and raised via artificial neural networks (ANN), k-nearest neighbor (KNN) and XG Boost. With the assistance of these algorithms, a reliable and flexible framework is obtained to predict the optimized design parameters of the proposed radiator. To confirm the accuracy of these ML algorithms, predicted outcomes is compared with outcomes obtained from HFSS and fabricated prototype. The predicted results are very well matched with practical conclusion. The designed radiator operates from 2.55 to 3.2 GHz, along with supporting circularly polarized waves from 2.67 to 3.1 GHz. These appearances make the proposed aerial suitable for the sub-6 GHz frequency band.

Performance prediction of metasurface-loaded ceramic-based MIMO antenna for B41/n41 bands using ML algorithms