Abstract
As an integral part of the electromagnetic system, antennas are becoming more advanced and versatile than ever before, thus making it necessary to adopt new techniques to enhance their performance. Machine Learning (ML), a branch of artificial intelligence, is a method of data analysis that automates analytical model building with minimal human intervention. The potential for ML to solve unpredictable and non-linear complex challenges is attracting researchers in the field of electromagnetics (EM), especially in antenna and antenna-based systems. Numerous antenna simulations, synthesis, and pattern recognition of radiations as well as non-linear inverse scattering-based object identifications are now leveraging ML techniques. Although the accuracy of ML algorithms depends on the availability of sufficient data and expert handling of the model and hyperparameters, it is gradually becoming the desired solution when researchers are aiming for a cost-effective solution without excessive time consumption. In this context, this paper aims to present an overview of machine learning, and its applications in Electromagnetics, including communication, radar, and sensing. It extensively discusses recent research progress in the development and use of intelligent algorithms for antenna design, synthesis and analysis, electromagnetic inverse scattering, synthetic aperture radar target recognition, and fault detection systems. It also provides limitations of this emerging field of study. The unique aspect of this work is that it surveys the state-of the art and recent advances in ML techniques as applied to EM.
Highlights
IntroductionAmong the different aspects of EM, antenna research has evolved and transformed in totality from the design to end-use application [2,3,4,5,6]
This paper explores different Machine Learning (ML) applications from the perspective of EM, especially antenna design, synthesis, manufacturing, development, and the detection of anomalies
Applying ML models in the design of compact antenna or antenna arrays with high gain, transmission efficiency, and directivity, including suitable material selection, can enrich the effectiveness of the design
Summary
Among the different aspects of EM, antenna research has evolved and transformed in totality from the design to end-use application [2,3,4,5,6]. With the advent of the 5G spectrum, antenna design is by far the most challenging part of the implementation as it is entirely dependent on the end device form factor. This inevitably pushes antenna design for 5G devices to fit the ever-increasing requirements for greater bandwidth, more frequency bands, and superior interference immunity [17,18]. Fault detection in antenna arrays and inverse scatteringbased non-linear problems need sophisticated yet cost-effective solutions, where Machine
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