Abstract
Accurate recognition of orbital angular momentum (OAM) modes is a major challenge for OAM-based optical communications over atmospheric turbulence channels. The turbulence-induced distortions cause difficulties for the receiver to distinguish between adjacent OAM modes. Deep learning, such as convolutional neural networks (CNN), has been a promising technique in solving this problem. To improve the recognition performance, we propose a vortex modulation method that can magnify the subtle differences between closely adjacent OAM states. This allows the CNN to capture the image features more effectively and to recognize the topological charges more accurately. Numerical results show high recognition accuracy for both integer topological charges and fractional ones even under strong turbulence intensity and long propagation distance, which demonstrate the utility of the proposed method.
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