Abstract
In this communication, Al2O3 ceramic-based filtenna is designed and inspected. The different parameters of the proposed antenna such as, |S11| and gain, are optimized and predicted using different machine learning (ML) techniques i.e. deep neural network (DNN), random forest, and XG boost. An optimized value obtained from various ML algorithms is compared with the value obtained HFSS EM simulator and experimental result. Good agreement is obtained among all. The proposed radiator works between 2.4 GHz and 2.71 GHz with a gain value of 5.0 dBi. Out of the operating frequency, the gain value decreases drastically i.e. −15 dBi at 2.27 GHz and −21 dB at 2.81 GHz. Omnidirectional types of far-field characteristics in both the principal plane and filtering features make the proposed radiator suitable (B41/n41) Bands.
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