CNN-based mode analysis of orbital angular momentum beams in atmospheric turbulence

Yongchuang Chen; Jincheng Zou; Yaguang Xu; Xudong Yuan; Ronger Lu; RuiZhi Zhao; Xia Feng; Chao Zhang; Yiqiang Qin; Yongyuan Zhu

doi:10.1117/12.2599573

18 June 2021 CNN-based mode analysis of orbital angular momentum beams in atmospheric turbulence

Yongchuang Chen, Jincheng Zou, Yaguang Xu, Xudong Yuan, Ronger Lu, RuiZhi Zhao, Xia Feng, Chao Zhang, Yiqiang Qin, Yongyuan Zhu

Author Affiliations +

Proceedings Volume 11850, First Optics Frontier Conference; 118500T (2021) https://doi.org/10.1117/12.2599573
Event: First Optics Frontier Conference, 2021, Hangzhou, China

Abstract

The atmospheric turbulence disturbs the phase fronts of orbital angular momentum (OAM) beams, which significantly affects the detection of beam modes. In this paper, we propose a method based on the convolutional neural network (CNN) to detect OAM modes in atmospheric turbulence. This method does not require a separate system to suppress the influence of atmospheric turbulence. The propagation of multimode OAM beams in atmospheric turbulence is simulated by setting several random phase screens in the transmission channel. We select three levels of atmospheric turbulence. In all these cases, the predicted error of the trained CNN is lower than 2 ×10^-5 , which indicates that our network can detect the mode distribution in multimode OAM beams efficiently and accurately. We believe that this approach for detecting OAM modes holds great promise for potential applications and will provide widespread benefits for many optical fields.

Citation Download Citation

Yongchuang Chen, Jincheng Zou, Yaguang Xu, Xudong Yuan, Ronger Lu, RuiZhi Zhao, Xia Feng, Chao Zhang, Yiqiang Qin, and Yongyuan Zhu "CNN-based mode analysis of orbital angular momentum beams in atmospheric turbulence", Proc. SPIE 11850, First Optics Frontier Conference, 118500T (18 June 2021); https://doi.org/10.1117/12.2599573

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available