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arXiv · 2403.09462

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

Minyang Zhang·Dong-Xu Chen·Pengxiang Ruan·Jun Liu·Jun-Long Zhao·Chui-Ping Yang
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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 high accuracy. By assigning each category a classical information, we further apply our approach to an image transmission task, demonstrating 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.

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Minyang Zhang, Dong-Xu Chen, Pengxiang Ruan, Jun Liu, Jun-Long Zhao, Chui-Ping Yang. 2024-03-14. Deep-learning-assisted optical communication with discretized state space of structured light. https://arxiv.org/abs/2403.09462

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