Coherence shaping for optical vortices: a coherence shift keying scheme enabled by deep learning for optical communication

To meet rapidly growing communication demands, researchers have focused on structured light-based shift keying techniques. However, higher-order modes are prone to large diffraction divergence and are easily perturbed. In this study, we experimentally demonstrate what we believe to be a novel coherence shaping method for petal-like structures of optical vortices, enabling the generation of non-diffraction interference states between completely coherent and incoherent states. In addition, we propose a coherence shift keying (CSK) scheme enabled by deep learning, and a well-trained model can achieve a high recognition accuracy (>0.997) of interference states under practical conditions, including complex environments. Further experimental validation has confirmed that the minimum achievable visibility-level bandwidth is 0.02. This study provides a new, to the best of our knowledge, platform for low-order structured light mode-based high-capacity and encrypted shift keying communication systems.