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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2021 Optical Fiber Communications Conference and Exhibition, OFC 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781943580866
Publication statusPublished - Jun 2021
Event2021 Optical Fiber Communications Conference and Exhibition, OFC 2021 - San Francisco, United States
Duration: 6 Jun 202111 Jun 2021

Publication series

Name2021 Optical Fiber Communications Conference and Exhibition, OFC 2021 - Proceedings

Conference

Conference2021 Optical Fiber Communications Conference and Exhibition, OFC 2021
Country/TerritoryUnited States
CitySan Francisco
Period6/06/2111/06/21

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