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

TLDR

The review aims to provide EM practitioners with insights into the effectiveness and potential of deep neural networks as computational tools. It surveys DNN applications across forward/inverse scattering, direction‑of‑arrival estimation, radar and remote sensing, and multi‑input/multi‑output systems. The authors highlight promising DNN solutions for localization, human behavior monitoring, and electromagnetic compatibility, and discuss future research directions.

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.