Design of a Wideband Rectangular Dielectric Resonator Antenna Using Deep Learning

A method using deep learning (DL) and genetic algorithm (GA) is proposed for the design of a wideband dielectric resonator antenna (DRA). The three-step approach involves data preparation for network training, construction of the neural network architecture, and optimization of parameters using the genetic algorithm. The trained DL network is capable of fast and accurate predicting the DRA geometry and performance. The DL network was used to design DRAs within a predefined frequency band, achieving an −10dB impedance bandwidth of over 1.7GHz. The trained neural network can predict both the impedance bandwidth and the dimensions of the DRA. Compared to traditional optimization methods, this approach significantly improves the efficiency of DRA design.