Computational intelligence paradigms for UWB antennas: a comprehensive review of analysis, synthesis and optimization

Technological advancement in wireless communication systems has spurred a rapid growth in ultra-wideband (UWB) technology over the last decade. One of the key components of UWB system is the antenna. For modeling the antenna system, several analytical and numerical techniques are available; however, they are extensive computational effort. Computational intelligence (CI) techniques have gained enormous importance in the microwave community recently due to their ability to bypass the tedious and repeated analysis of conventional techniques. Over the last decade, notable research has been carried out on different CI techniques for modeling UWB antennas. In this article, a comprehensive investigation of CI modeling for the analysis, synthesis, and optimization of UWB antennas is presented. Widely used CI techniques in the open literature for UWB antenna modeling are artificial neural network, genetic algorithm, particle swarm optimization, and hybrid CI techniques. Therefore, the published articles are classified into these four CI techniques for an effective survey article. The objectives of this review article are to highlight the CI techniques adopted for modeling of the UWB antennas and to point out the open problems for future researchers. To the best of the author's knowledge, such a review article has not been presented in the open literature.

Automated summary

This article presents a comprehensive investigation of computational intelligence (CI) techniques for modeling ultra-wideband (UWB) antennas. It highlights the importance of CI techniques in the microwave community and provides an overview of widely used CI techniques such as artificial neural network, genetic algorithm, particle swarm optimization, and hybrid CI techniques. The article also identifies open problems for future researchers and emphasizes the novelty of this review article in the open literature.