Deep-learning-assisted optical communication with discretized state space of structured light
arxiv(2024)
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 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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