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This survey article outlines a comprehensive investigation of research carried out on dielectric resonator antennas (DRAs) in the last three and half decades, in an application‐oriented approach. DRAs have created a remarkable position in antenna engineering for their adept characteristics like high efficiency, low loss, wide bandwidth, compact size, 3‐dimensional modeling flexibility, etc. The use of DRAs for different commercial and defense applications associated with the wireless communication is highlighted in this article. To make a smooth and effective survey article, all the application‐oriented DRAs available in the open literature are classified in five different categories like microwave bands, specific frequency, technology, millimeter‐wave, and miscellaneous types. The ultimate aims of this review article are as follows: (i) highlights the usability of DRAs for different commercial and defense applications, (ii) helpful for the antenna industries/manufacturers to find out the best DRA for any specific application as per their requirement, and (iii) points out research gap in some application domains which will be quite helpful for future antenna researchers. In the authors' opinion, this survey may be helpful to DRA researchers as such a survey process is not available in the open literature.

Artificial neural networks have been getting popularity for predicting various performance parameters of microstrip antennas due to their learning and generalization features. In this letter, a neural-networks-based synthesis model is presented for predicting the "slot-size" on the radiating patch and inserted "air-gap" between the ground plane and the substrate sheet, simultaneously. Different performance parameters like resonance frequencies, gains, directivities, antenna efficiencies, and radiation efficiencies for dual resonance are observed by varying the dimensions of slot and inserted air-gap. For validation, a prototype of microstrip antenna is fabricated using Roger's substrate, and its performance parameters are measured. Measured results show a very good agreement to their predicted and simulated values.

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An efficient and quick approach based on artificial neural networks (ANN) is being applied on different patches of microstrip antennas since last one decade. Different scientists have proposed different neural models for analyzing the different types of microstrip patches like rectangular, triangular, square, circular etc. whereas few models have also been proposed for designing rectangular, triangular and square patches. But in the available literature of microstrip antennas with neural networks no single model has been proposed till date firstly for calculating the radius of the circular patch microstrip antenna (CPMSA) and secondly for calculating more than one parameter like radius of the CPMSA and side-length of the equilateral triangular patch microstrip antenna (ETMSA) simultaneously. In this paper authors have proposed a multi-layered perceptron feed forward neural network model for calculating the radius/side-length of the circular patch/triangular patch microstrip antennas simultaneously. The model has also been validated on some mathematically generated datasets that are not included in training or testing of the model. The results obtained by the proposed model are in conformity and very good in agreement with the experimental results given in the literature. Index Term-Multi-layered perceptron feed forward neural networks, microstrip antennas, radius, circular patch, side-length and triangular patch.

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