In this research, we propose the application of a deep-learning method for recognizing fractional orbital angular momentum (OAM) beams distorted by atmospheric turbulence (AT). In order to acquire data sets for model training and testing, we first simulate the propagation of fractional OAM beams through a 1000-m optical channel. Here, AT effects in the channel are modeled with five random phase screens equally spaced. Then, the effects of turbulence strength and OAM mode spacing on the recognition accuracy are analyzed. In particular, we compare the recognition accuracy for three types of OAM-encoding schemes, two single-mode sets (integer and fractional OAM) and one multiplexed fractional mode set. Despite the strong distortion, our designed model shows the ability to recognize transmitted OAM modes with high accuracy and high resolution. In addition to this, we investigate the generalization ability of deep learning to accommodate unknown turbulence environments.
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