A review on the design and optimization of antennas using machine learning algorithms and techniques

Abstract

This paper presents a focused and comprehensive literature survey on the use of machine learning (ML) in antenna design and optimization. An overview of the conventional computational electromagnetics and numerical methods used to gain physical insight into the design of the antennas is first presented. The major aspects of ML are then presented, with a study of its different learning categories and frameworks. An overview and mathematical briefing of regression models built with ML algorithms is then illustrated, with a focus on those applied in antenna synthesis and analysis. An in‐depth overview on the different research papers discussing the design and optimization of antennas using ML is then reported, covering the different techniques and algorithms applied to generate antenna parameters based on desired radiation characteristics and other antenna specifications. Various investigated antennas are sorted based on antenna type and configuration to assist the readers who wish to work with a specific type of antennas using ML.

Ultra-Wideband CPW Fed Band-Notched Monopole Antenna Optimization Using Machine Learning

“…Machine learning (ML) approaches have been widely investigated and implemented in antenna design in recent years because of their capacity to learn from measured or simulated antenna data via a training procedure and subsequently expedite the entire antenna design process [22]. When multiple parameters must be tuned, or complex structures must be designed, ML approaches have considerable advantages in decreasing significant computing times [23].…”

Application of Machine Learning in Electromagnetics: Mini-Review

“…With the increased complexities, it is important to minimize design time and overhead costs to achieve optimized antenna implementation. ML has contributed to parameter optimization and evolutionary algorithm development in antenna research [21,22]. For instance, the usual synthesis mechanism utilizing a full-wave electromagnetic simulator is gradually being replaced by faster and more cost-effective ML algorithms.…”

Development of a High Gain FSS Reflector Backed Monopole Antenna Using Machine Learning for 5g Applications

“…The purpose of the current paper is to present a novel solution based on Artificial Neural Network (ANN) techniques to overcome the rigorous computational cost in terms of memory and CPU usage and the highly ownership cost of the electromagnetic (EM) simulation software [28][29][30]. Also, it helps on getting the optimum design configuration with no need for rigorous mathematical formulations for either the position of the FSS or the dimension of the ground of the monopole antenna as a function of the optimum performance parameters for the antenna quoted in this work.…”