  Multiplexing multiple orbital angular momentum (OAM) modes of light has the
potential to increase data capacity in optical communication. However, the
distribution of such modes over long distances remains challenging. Free-space
transmission is strongly influenced by atmospheric turbulence and light
scattering, while the wave distortion induced by the mode dispersion in fibers
disables OAM demultiplexing in fiber-optic communications. Here, a
deep-learning-based approach is developed to recover the data from scattered
OAM channels without measuring any phase information. Over a 1-km-long standard
multimode fiber, the method is able to identify different OAM modes with an
accuracy of more than 99.9% in parallel demultiplexing of 24 scattered OAM
channels. To demonstrate the transmission quality, color images are encoded in
multiplexed twisted light and our method achieves decoding the transmitted data
with an error rate of 0.13%. Our work shows the artificial intelligence
algorithm could benefit the use of OAM multiplexing in commercial fiber
networks and high-performance optical communication in turbulent environments.
