@inproceedings{dae15af5682f436a8b57610c4586cd9a,
title = "Wasserstein Autoencoder Based End-to-End Learning Strategy of Geometric Shaping for an OAM Mode Division Multiplexing IM/DD Transmission",
abstract = "We propose a Wasserstein Autoencoder based end-to-end geometric shaping scheme for IM/DD OAM-MDM optical fiber communication system. Compared with traditional autoencoder, the BER decreased by up to 28% and 33% with two OAM modes.",
keywords = "Wasserstein autoencoder, geometric shaping, machine learning, orbital angular momentum",
author = "Zhaohui Cheng and Ran Gao and Qi Xu and Fei Wang and Yi Cui and Xiangjun Xin",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023 ; Conference date: 04-11-2023 Through 07-11-2023",
year = "2023",
doi = "10.1109/ACP/POEM59049.2023.10369924",
language = "English",
series = "2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023",
address = "United States",
}