  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.
