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

Abstract : 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.
Complete list of metadatas

Contributor : Andrea Massa <>
Submitted on : Thursday, November 7, 2019 - 4:47:43 PM
Last modification on : Tuesday, November 12, 2019 - 10:52:18 AM



Andrea Massa, Davide Marcantonio, Xudong Chen, Maokun Li, Marco Salucci. DNNs as Applied to Electromagnetics, Antennas, and Propagation—A Review. IEEE Antennas and Wireless Propagation Letters, Institute of Electrical and Electronics Engineers, 2019, 18 (11), pp.2225-2229. ⟨10.1109/LAWP.2019.2916369⟩. ⟨hal-02354375⟩



Record views