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Physics > Optics
Title: Deep-learning-assisted optical communication with discretized state space of structured light
(Submitted on 14 Mar 2024 (v1), last revised 19 Apr 2024 (this version, v2))
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.
Submission history
From: Dong-Xu Chen [view email][v1] Thu, 14 Mar 2024 15:03:09 GMT (1321kb,D)
[v2] Fri, 19 Apr 2024 05:21:03 GMT (4808kb,D)
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