Abstract

Vortex beam carrying orbital angular momentum (OAM) suffers heavily interference from oceanic turbulence (OT) when it is transmitted through underwater wireless optical communication (UWOC) system. Adaptive optics (AO) is up to now the best candidate to improve the UWOC communications quality. In this work, we propose a generative adversarial network (GAN) based AO scheme to compensate for the distortion caused by OT, where the GAN is first trained by numerical simulations to obtain the mapping between the distortion intensity distribution and its corresponding phase screen representating OT, and a predicted phase screen is then given from the trained GAN when the experimental distorted intensity distribution is input. Finally, the predicted phase screen is compensated to the distorted vortex beam. The results show that the proposed scheme can quickly extract data features and accurately output the predicted phase screen. The distorted intensity pattern of the vortex beam is well compensated even in strong turbulence, where the intermodal crosstalk of different OAM modes is alleviated and the transmission probability of OAM mode is promoted significantly.

Diffractive deep neural network based adaptive optics scheme for vortex beam in oceanic turbulence

Haichao Zhan, Yixiang Peng, Bing Chen, Le Wang, Wennai Wang, and Shengmei Zhao
Opt. Express 30(13) 23305-23317 (2022)

Performance analysis of adaptive optics with a phase retrieval algorithm in orbital-angular-momentum-based oceanic turbulence links

Huan Chang, Xiaoli Yin, Xiaozhou Cui, Xiao-Zheng Chen, Yuan-Zhi Su, Jian-Xin Ma, Yong-Jun Wang, Lijia Zhang, and Xiangjun Xin
Appl. Opt. 58(22) 6085-6090 (2019)

Hybrid opto-electronic deep neural network based orbital angular momentum mode recognition scheme in oceanic turbulence

Haichao Zhan, Le Wang, Wennai Wang, and Shengmei Zhao
J. Opt. Soc. Am. B 40(1) 187-193 (2023)

Experimental recognition of vortex beams in oceanic turbulence combining the Gerchberg–Saxton algorithm and convolutional neural network

Wen-Qi Fan, Feng-Lin Gao, Fu-Chan Xue, Jing-Jing Guo, Ya Xiao, and Yong-Jian Gu
Appl. Opt. 63(4) 982-989 (2024)

Deep learning based atmospheric turbulence compensation for orbital angular momentum beam distortion and communication

Junmin Liu, Peipei Wang, Xiaoke Zhang, Yanliang He, Xinxing Zhou, Huapeng Ye, Ying Li, Shixiang Xu, Shuqing Chen, and Dianyuan Fan
Opt. Express 27(12) 16671-16688 (2019)