DNNs as Applied to Electromagnetics, Antennas, and Propagation-A Review

• Published in: IEEE antennas and wireless propagation letters Vol. 18; no. 11; pp. 2225 - 2229
• Main Authors: Massa, Andrea, Marcantonio, Davide, Chen, Xudong, Li, Maokun, Salucci, Marco
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Antennas; Artificial neural networks; Computer architecture; Convolution; convolutional neural networks; deep learning (DL); deep neural networks (DNNs); Direction of arrival; Direction-of-arrival estimation; electromagnetics (EM); Electromagnetism; Engineering Sciences; Human behavior; Inverse scattering; Machine learning; Manganese; Propagation; radar; Remote sensing; remote sensing (RS); Scattering; Signal and Image processing; Software
• ISSN: 1536-1225; 1548-5757
• DOI: 10.1109/LAWP.2019.2916369

A review of the most recent advances in deep learning (DL) as applied to electromagnetics (EM), antennas, and propagation is provided. It is aimed at giving the interested readers and practitioners in EM and related applicative fields some useful insights on the effectiveness and potentialities of deep neural networks (DNNs) as computational tools with unprecedented computational efficiency. The range of considered applications includes forward/inverse scattering, direction-of-arrival estimation, radar and remote sensing, and multi-input/multi-output systems. Appealing DNN-based solutions concerned with localization, human behavior monitoring, and EM compatibility are reported as well. Some final remarks are drawn along with the indications on future trends according to the authors' viewpoint.