Deep learning-based robust OAM mode recognition in atmospheric turbulence
yuxin bi, Lei Xin, Xingang ZHUANG, Zhongming Yang · Applied Optics · 2026
Vortex beams carrying orbital angular momentum (OAM) offer a promising approach to increasing channel capacity in free-space optical (FSO) communication by enabling multiplexing through topological charge (TC) states. However, atmospheric turbulence introduces significant distortions, posing challenges for accurate OAM recognition. In this paper, we propose ARNet, a deep-learning-based network that combines channel-attention-enhanced residual blocks with dynamic L2 regularization to enhance the robustness of OAM identification under turbulence. Experiments demonstrate that ARNet achieves 95.52% recognition accuracy for OAM states ranging from −5 to +5 under strong turbulence ( C n 2 =5×10 −13 m −2/3 ) conditions. The proposed method significantly outperforms traditional interference-based techniques, demonstrating enhanced resilience and potential for practical FSO system deployment in turbulent environments.