Application of Machine Learning in Electromagnetics: Mini-Review

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.

Radiation Pattern Synthesis of the Coupled Almost Periodic Antenna Arrays Using Artificial Neural Network ANN Model

“…To restraint the electromagnetic calculation on one unit cell, the dependence on Floquet modes (α or (α, β)) is taken into account [9,11,14,32]. Then, the field components can be therefore expressed in the generalized Fourier series expansions, and the analysis region can be reduced to only one periodicity cell bounded by the known periodic walls, as presented in the figure (2). In this case, the interaction between cells can be taken into consideration using a novel expression of the mutual coupling shown in the previous work [9,11].…”

“…[9,11,14]. In general, smart antenna arrays involve intelligent systems including genetic algorithms and neural networks to synthesize the radiation pattern [2,3]. Many research papers are proving that genetic algorithm (GA) is used basically for sidelobe reduction in antenna pattern synthesis [15,18].…”

“…To obtain the computation time acceleration, the difference in consumption time between the default training and training with the early stopping method (using the Levenberg algorithm) is given in the table below (2). It shows that the time consuming of an early stopping method is less than the time required for a default training (without early stopping).…”

Radiation Pattern Synthesis of the Coupled almost Periodic Antenna Arrays Using an Artificial Neural Network Model

“…In general, smart antenna arrays involve intelligent systems, including genetic algorithms and neural networks, to synthesize the radiation pattern [6][7][8][9][10]. The genetic algorithm (GA) is used basically for sidelobe reduction in antenna pattern synthesis [11][12][13].…”

“…Therefore, to study the spatial electromagnetic behavior of periodic antenna arrays (as explained in the previous section in Equation (6)), another way to proceed is based on the superposition of Floquet states (superposition theorem) in a finite or infinite periodic array:…”