@inproceedings{0c71f869011f4ce899c2e8746b9a0057,
title = "Orbital Angular Momentum (OAM) Recognition with Generative Adversarial Network (GAN) based Atmospheric Modeling",
abstract = "We proposed a Generative Adversarial Network (GAN) based atmospheric modeling method which helps with the Orbital angular momentum (OAM) recognition to achieve better accuracy with limited data.",
author = "Chenda Lu and Qinghua Tian and Xiangjun Xin and Lei Zhu and Qi Zhang and Haipeng Yao and Huan Chang and Ran Gao",
note = "Publisher Copyright: {\textcopyright} 2021 OSA.; 2021 Optical Fiber Communications Conference and Exhibition, OFC 2021 ; Conference date: 06-06-2021 Through 11-06-2021",
year = "2021",
month = jun,
language = "English",
series = "2021 Optical Fiber Communications Conference and Exhibition, OFC 2021 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2021 Optical Fiber Communications Conference and Exhibition, OFC 2021 - Proceedings",
address = "United States",
}