A comprehensive overview of machine learning-assisted antenna for modern wireless communication

Abstract

In this work, an overview of implementing machine learning (ML) models in antenna design and optimisation has been proposed. This includes deep learning on ML structure, categories, and frameworks to obtain useful and general insights about methods of predicting, collecting, and analysing high throughput fast data using ML techniques. An in-depth overview on the various published research works related to designing and optimising of antennas using ML is proposed, including the different ML- techniques and algorithms that have been used to generate antenna parameters such as S-parameters, radiation pattern, and gain values. However, the designing of modern antennae is still complicated regarding structure, variables, and environmental factors. Moreover, the cost of time and computational resources are unavoidable and unacceptable for most users. To address these challenges, ML methods-based antennas have been developed and applied to improve the reduction in the efficiency and accuracy of antenna modelling. This can be involved methods to rain models on data that can be utilised to predict the antenna performance for a given set of antenna design variables. This work summarises the developed and applied MLs that have been proposed to improve the efficiency and accuracy of antenna modelling