Journal
IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS
Volume 18, Issue 11, Pages 2225-2229Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LAWP.2019.2916369
Keywords
Antennas; convolutional neural networks; deep learning (DL); deep neural networks (DNNs); electromagnetics (EM); propagation; radar; remote sensing (RS); scattering
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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.
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