@inproceedings{52a73801e5c14f43b82d18b7c1a3c079,
title = "An Antenna Array Design Method Based on Residual Graph Neural Network",
abstract = "A learning method based on residual graph neural network was proposed to improve the efficiency of antenna array design. Firstly, the geometric parameters of the antenna array are processed into graph representation, which is used as the input features of the graph neural network. After the graph encoding process, the complex-valued fully connected network with residual connections is used to predict the radiation pattern of the antenna array. Based on the residual graph neural network, a seven-element linear array structure is designed to prove its effectiveness.",
keywords = "antenna array, graph neural network, optimization, residual",
author = "Du Hao and Zhang Anxue and Yang Qian and Liao Xuewen",
note = "Publisher Copyright: {\textcopyright} 2023 Applied Computational Electromagnetics Society (ACES).; 2023 International Applied Computational Electromagnetics Society Symposium, ACES-China 2023 ; Conference date: 15-08-2023 Through 18-08-2023",
year = "2023",
doi = "10.23919/ACES-China60289.2023.10249866",
language = "英语",
series = "2023 International Applied Computational Electromagnetics Society Symposium, ACES-China 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2023 International Applied Computational Electromagnetics Society Symposium, ACES-China 2023",
}