@inproceedings{db43051332064961ba0f8d1bab0229f1,
title = "Coding programmable metasurfaces based on deep learning techniques",
abstract = "In this paper, we propose a coding scheme based on deep learning techniques for complex beam forming with programmable metasurfaces. A fully convolutional neural network is carefully designed that can 'learn' the physics of beam forming from computed data, and make online prediction of the coding matrices. Both numerical and experimental results show that the network can compute coding matrices fulfilling the input requirement in less than one millisecond. This scheme may open a door for real-time complex beam steering with programmable metasurfaces and digital phased arrays.",
keywords = "Beam Forming, Convolutional Neural Network, Deep Learning, Programmable Metasurface",
author = "Tao Shan and Maokun Li",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, APSURSI 2019 ; Conference date: 07-07-2019 Through 12-07-2019",
year = "2019",
month = jul,
doi = "10.1109/APUSNCURSINRSM.2019.8889025",
language = "英语",
series = "2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, APSURSI 2019 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "245--246",
booktitle = "2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, APSURSI 2019 - Proceedings",
address = "美国",
}