Orbital angular momentum (OAM) recognition with generative adversarial network (GAN) based atmospheric modeling

Chenda Lu; Qinghua Tian; Xiangjun Xin; Lei Zhu; Qi Zhang; Haipeng Yao; Huan Chang; Ran Gao

Orbital angular momentum (OAM) recognition with generative adversarial network (GAN) based atmospheric modeling

Chenda Lu, Qinghua Tian^*, Xiangjun Xin, Lei Zhu, Qi Zhang, Haipeng Yao, Huan Chang, Ran Gao

^*此作品的通讯作者

信息与电子学院

Beijing University of Posts and Telecommunications

科研成果: 期刊稿件 › 会议文章 › 同行评审

摘要

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.

源语言	英语
期刊	Optics InfoBase Conference Papers
出版状态	已出版 - 2021
活动	Optical Fiber Communication Conference, OFC 2021 - Virtual, Online, 美国期限: 6 6月 2021 → 11 6月 2021

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@article{bc20db4e281940a8983a9f38cb1dab10,

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} OSA 2021, {\textcopyright} 2021 The Author(s); Optical Fiber Communication Conference, OFC 2021 ; Conference date: 06-06-2021 Through 11-06-2021",

year = "2021",

language = "English",

journal = "Optics InfoBase Conference Papers",

issn = "2162-2701",

}

TY - JOUR

T1 - Orbital angular momentum (OAM) recognition with generative adversarial network (GAN) based atmospheric modeling

AU - Lu, Chenda

AU - Tian, Qinghua

AU - Xin, Xiangjun

AU - Zhu, Lei

AU - Zhang, Qi

AU - Yao, Haipeng

AU - Chang, Huan

AU - Gao, Ran

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85133808303&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:85133808303

SN - 2162-2701

JO - Optics InfoBase Conference Papers

JF - Optics InfoBase Conference Papers

T2 - Optical Fiber Communication Conference, OFC 2021

Y2 - 6 June 2021 through 11 June 2021

ER -

Orbital angular momentum (OAM) recognition with generative adversarial network (GAN) based atmospheric modeling

摘要

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引用此