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

• Published in: Chinese physics B Vol. 33; no. 12; pp. 120304 - 201
• Main Authors: Zhang, Minyang, Chen, Dong-Xu, Ruan, Pengxiang, Liu, Jun, Fu, Dong-Zhi, Zhao, Jun-Long, Yang, Chui-Ping
• Format: Journal Article
• Language: English
• Published: Chinese Physical Society and IOP Publishing Ltd 01.12.2024
• Subjects: machine learning; optical communication; orbital angular momentum; machine learning; orbital angular momentum; optical communication
• ISSN: 1674-1056; 2058-3834
• DOI: 10.1088/1674-1056/ad8553

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.