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

• Published in: International journal of RF and microwave computer-aided engineering Vol. 30; no. 10
• Main Authors: El Misilmani, Hilal M., Naous, Tarek, Al Khatib, Salwa K.
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.10.2020
• Subjects: Algorithms; Antenna design; Antennas; Computational electromagnetics; Design optimization; Literature reviews; Machine learning; neural networks; Numerical methods; Optimization; Regression analysis; Regression models; Scientific papers
• ISSN: 1096-4290; 1099-047X
• DOI: 10.1002/mmce.22356

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.