Atmospheric turbulence-distorted OAM mode recognition via multi-scale dilated convolution with multi-level feature fusion

Vortex beams with orbital angular momentum (OAM) significantly enhance system capacity, and high-precision recognition of OAM mode through atmospheric turbulence (AT) channels can markedly improve the information transmission capability of free-space optical communication systems. In this paper, with a cylindrical lens-assisted distinguish between positive and negative OAM, a reliable neural network combining multi-scale dilated convolution (MSDC) unit and multi-level feature fusion (MLFF) module is proposed to detect high order AT-distorted OAM modes. The network fully exploits the features in light-intensity images to achieve a highest recognition accuracy of 99.4% for mode-orders from -20 to +20 in a hybrid ATs dataset ( C n 2 = 5×10 −16 , 5×10 −14 , 5×10 −12 m -2/3 ), and almost 96% even in strong turbulence. Experimental results on accuracy, efficiency, reliability, and robustness demonstrate that the proposed method excels and provides a trustworthy solution for complex AT-distorted OAM mode recognition.