  Orbital angular momentum of light is regarded as a valuable resource in
quantum technology, especially in quantum communication and quantum sensing and
ranging. However, the OAM state of light is susceptible to undesirable
experimental conditions such as propagation distance and phase distortions,
which hinders the potential for the realistic implementation of relevant
technologies. In this article, we exploit an enhanced deep learning neural
network to identify different OAM modes of light at multiple propagation
distances with phase distortions. Specifically, our trained deep learning
neural network can efficiently identify the vortex beam's topological charge
and propagation distance with 97% accuracy. Our technique has important
implications for OAM based communication and sensing protocols.

Machine Learning;Signal Processing;Optics