@inproceedings{aabe489d1df945ac9a7159aac86340ef,
title = "Synthesis of Refiectarray Based on Deep Learning Technique",
abstract = "In this work, we investigate feasibility of applying deep learning techniques to synthesis of reflectarrays. A deep convolutional neural network is proposed based on AlexNet to predict phase-shift in an reflectarray antenna given a reflected direction. The proposed network takes radiation pattern and beam direction as input, with training and testing data obtained by array theory. After cafefully training, the proposed network demonstrates strong approximation ability and makes correct prediction of phase-shift. Preliminary numerical experiments show that the prediction error of phase-shift can reach below 0.4\%. This paper shows that deep convolutional neural networks can mimic the phase synthesis process of reflectarrays and it has a great potential for real-time phase prediction in more complex problems of array synthesis.",
keywords = "AlexNet, Array Synthesis, Array Theory, Convolutional Neural Network, Deep Learning, Synthesis of Reflectarray",
author = "Tao Shan and Maokun Li and Shenheng Xu and Fan Yang",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 Cross Strait Quad-Regional Radio Science and Wireless Technology Conference, CSQRWC 2018 ; Conference date: 21-07-2018 Through 24-07-2018",
year = "2018",
month = sep,
day = "5",
doi = "10.1109/CSQRWC.2018.8454981",
language = "英语",
series = "2018 Cross Strait Quad-Regional Radio Science and Wireless Technology Conference, CSQRWC 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2018 Cross Strait Quad-Regional Radio Science and Wireless Technology Conference, CSQRWC 2018",
address = "美国",
}