@inproceedings{18e78c9de0254c97a5eae5aab3d7a428,
title = "An improvement on the CNN-based OAM Demodulator via Conditional Generative Adversarial Networks",
abstract = "In the paper, an Orbital Angular Momentum (OAM) demodulation method based on Conditional Generative Adversarial Networks(CGAN) is proposed to improve the accuracy of Convolutional Neural Networks (CNN) based demodulator. We train a CGAN on a limited data set, and the discriminator in CGAN is fine-tuned as a new classifier for OAM demodulation. Our numerical simulations demonstrate that the proposed method can improve the accuracy of OAM demodulator from 93.56\% to 98.36\% over 400-m free-space link when the turbulence strength C-n\textasciicircum{}2 equals 4{\texttimes}10-13 m-2/3.",
keywords = "Conditional Generative Adversarial Networks, Deep Learning, OAM demodulation",
author = "Zhe Li and Qinghua Tian and Qi Zhang and Kuo Wang and Feng Tian and Chenda Lu and Leijing Yang and Xiangjun Xin",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 18th International Conference on Optical Communications and Networks, ICOCN 2019 ; Conference date: 05-08-2019 Through 08-08-2019",
year = "2019",
month = aug,
doi = "10.1109/ICOCN.2019.8934809",
language = "English",
series = "2019 18th International Conference on Optical Communications and Networks, ICOCN 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2019 18th International Conference on Optical Communications and Networks, ICOCN 2019",
address = "United States",
}