Atmospheric Turbulence Transmission Characteristics and Deep Learning Identification of a Novel Bessel-Bessel-Gaussian Vortex Beam

In the free-space optical communication (FSO) domain, orbital angular momentum (OAM) beams are vulnerable to disturbances from composite channels, including atmospheric turbulence and diffuse media, creating significant challenges for accurate mode identification. The present study investigates the transmission characteristics and mode recognition issues of a novel Bessel-Bessel-Gaussian (BBG) vortex beam after traversing a composite channel comprising atmospheric turbulence and diffuse media. Integrating the Fresnel diffraction model with the von Kalman turbulence model enables the numerical simulation of the optical field evolution of BBG beams under varying turbulence intensities and transmission distances. Simulation results in constructing a dataset comprising 9,600 distorted spot images. The evaluation of the recognition performance of multiple deep learning models, including traditional convolutional neural networks (VGG16, ResNet50) and advanced Transformer architectures (Swin-Transformer), is facilitated by this dataset. The simulation results demonstrate that BBG beams exhibit superior shape retention capabilities in complex channels due to their unique double-Bessel function structure. Concerning model performance, the Swin-Transformer model, benefiting from its efficient sliding-window self-attention mechanism, achieved a recognition accuracy of 98.2% under severe conditions of strong turbulence and 1 km transmission distance, significantly outperforming ResNet50 (94.8%) and VGG16 (82%). This study corroborates the practical application of BBG beams as highly robust information carriers. It validates the Swin-Transformer architecture's superiority as an efficient demodulation tool under complex channel conditions, providing a novel solution for constructing high-capacity, highly reliable OAM-multiplexed FSO systems.