Deep-learning-assisted optical communication with discretized state space of structured light

doi:10.1088/1674-1056/ad8553

中国物理B ›› 2024, Vol. 33 ›› Issue (12): 120304-120304.doi: 10.1088/1674-1056/ad8553

Deep-learning-assisted optical communication with discretized state space of structured light

Minyang Zhang(张敏洋)¹, Dong-Xu Chen(陈东旭)^2,3,†, Pengxiang Ruan(阮鹏祥)¹, Jun Liu(刘俊)¹, Dong-Zhi Fu(付栋之)⁴, Jun-Long Zhao(赵军龙)², and Chui-Ping Yang(杨垂平)^5,‡

1 School of Science, Jiangsu University of Science and Technology, Zhenjiang 212003, China;
2 Quantum Information Research Center, Shangrao Normal University, Shangrao 334001, China;
3 Jiangxi Province Key Laboratory of Applied Optical Technology (2024SSY03051), Shangrao Normal University, Shangrao 334001, China;
4 School of Cable Engineering, Henan Institute of Technology, Xinxiang 453003, China;
5 School of Physics, Hangzhou Normal University, Hangzhou 311121, China

收稿日期:2024-08-06 修回日期:2024-10-08 接受日期:2024-10-10 发布日期:2024-12-03
通讯作者: Dong-Xu Chen, Chui-Ping Yang E-mail:chendx@sru.edu.cn;yangcp@hznu.edu.cn
基金资助:
Project supported by the National Natural Science Foundation of China (Grant Nos. 12204312, 12104190, and U21A20436), the Natural Science Foundation of Jiangxi Province, China (Grant Nos. 20224BAB211014 and 20232BAB201042), the Natural Science Foundation of Jiangsu Province, China (Grant No. BK20210874), the General Project of Natural Science Research in Colleges and Universities of Jiangsu Province, China (Grant No. 20KJB140008), the Innovation Program for Quantum Science and Technology (Grant No. 2021ZD0301705), and the China Postdoctoral Science Foundation (Grant No. 2021M702628).

Deep-learning-assisted optical communication with discretized state space of structured light

Minyang Zhang(张敏洋)¹, Dong-Xu Chen(陈东旭)^2,3,†, Pengxiang Ruan(阮鹏祥)¹, Jun Liu(刘俊)¹, Dong-Zhi Fu(付栋之)⁴, Jun-Long Zhao(赵军龙)², and Chui-Ping Yang(杨垂平)^5,‡

1 School of Science, Jiangsu University of Science and Technology, Zhenjiang 212003, China;
2 Quantum Information Research Center, Shangrao Normal University, Shangrao 334001, China;
3 Jiangxi Province Key Laboratory of Applied Optical Technology (2024SSY03051), Shangrao Normal University, Shangrao 334001, China;
4 School of Cable Engineering, Henan Institute of Technology, Xinxiang 453003, China;
5 School of Physics, Hangzhou Normal University, Hangzhou 311121, China

Received:2024-08-06 Revised:2024-10-08 Accepted:2024-10-10 Published:2024-12-03
Contact: Dong-Xu Chen, Chui-Ping Yang E-mail:chendx@sru.edu.cn;yangcp@hznu.edu.cn
Supported by:
Project supported by the National Natural Science Foundation of China (Grant Nos. 12204312, 12104190, and U21A20436), the Natural Science Foundation of Jiangxi Province, China (Grant Nos. 20224BAB211014 and 20232BAB201042), the Natural Science Foundation of Jiangsu Province, China (Grant No. BK20210874), the General Project of Natural Science Research in Colleges and Universities of Jiangsu Province, China (Grant No. 20KJB140008), the Innovation Program for Quantum Science and Technology (Grant No. 2021ZD0301705), and the China Postdoctoral Science Foundation (Grant No. 2021M702628).

摘要/Abstract

摘要： The rich structure of transverse spatial modes of structured light has facilitated their extensive applications in quantum information and optical communication. The Laguerre-Gaussian (LG) modes, which carry a well-defined orbital angular momentum (OAM), consist of a complete orthogonal basis describing the transverse spatial modes of light. The application of OAM in free-space optical communication is restricted due to the experimentally limited OAM numbers and the complex OAM recognition methods. Here, we present a novel method that uses the advanced deep learning technique for LG modes recognition. By discretizing the spatial modes of structured light, we turn the OAM state regression into classification. A proof-of-principle experiment is also performed, showing that our method effectively categorizes OAM states with small training samples and the accuracy exceeds 99% from three-dimensional (3D) to fifteen-dimensional (15D) space. By assigning each category a classical information, we further apply our approach to an image transmission task, achieving a transmission accuracy of 99.58%, which demonstrates the ability to encode large data with low OAM number. This work opens up a new avenue for achieving high-capacity optical communication with low OAM number based on structured light.

关键词: orbital angular momentum, machine learning, optical communication

Abstract: The rich structure of transverse spatial modes of structured light has facilitated their extensive applications in quantum information and optical communication. The Laguerre-Gaussian (LG) modes, which carry a well-defined orbital angular momentum (OAM), consist of a complete orthogonal basis describing the transverse spatial modes of light. The application of OAM in free-space optical communication is restricted due to the experimentally limited OAM numbers and the complex OAM recognition methods. Here, we present a novel method that uses the advanced deep learning technique for LG modes recognition. By discretizing the spatial modes of structured light, we turn the OAM state regression into classification. A proof-of-principle experiment is also performed, showing that our method effectively categorizes OAM states with small training samples and the accuracy exceeds 99% from three-dimensional (3D) to fifteen-dimensional (15D) space. By assigning each category a classical information, we further apply our approach to an image transmission task, achieving a transmission accuracy of 99.58%, which demonstrates the ability to encode large data with low OAM number. This work opens up a new avenue for achieving high-capacity optical communication with low OAM number based on structured light.

Key words: orbital angular momentum, machine learning, optical communication

中图分类号: (Quantum information)

03.67.-a

42.50.Tx (Optical angular momentum and its quantum aspects) 42.79.Sz (Optical communication systems, multiplexers, and demultiplexers?)

Minyang Zhang(张敏洋), Dong-Xu Chen(陈东旭), Pengxiang Ruan(阮鹏祥), Jun Liu(刘俊), Dong-Zhi Fu(付栋之), Jun-Long Zhao(赵军龙), and Chui-Ping Yang(杨垂平). Deep-learning-assisted optical communication with discretized state space of structured light[J]. 中国物理B, 2024, 33(12): 120304-120304.

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Deep-learning-assisted optical communication with discretized state space of structured light

Deep-learning-assisted optical communication with discretized state space of structured light

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