Enhancing information transmission in FSO-OAM systems through multiscale interpretable neural networks under turbulent conditions

Abstract

The paper proposes a solution to improve the information transmission efficiency of FSO-OAM systems under turbulent conditions by combining a multiscale interpretable neural network model, 4RK-MSNN. We use a multiscale structure to design the overall architecture of the neural network, which enables the comprehensive analysis of information in different dimensions. Based on the fourth-order Runge-Kutta correlation theory, a core network module, 4RK, is constructed, which can be explained in terms of dynamical systems. The 4RK-MSNN model, which couples the multiscale structure and the 4RK module, has a lower number of parameters, allowing for layered feature extraction in an interpretable framework. This facilitates low-cost, rapid sharing and transmission of feature information at different scales. The proposed solution is validated by transmitting image data under different turbulence intensities and transmission distances. The results indicate the feasibility of the proposed information transfer system. After adding redundant training data, the 4RK-MSNN model significantly improves the quality of the transmitted data and maintains satisfactory results even under strong turbulence and long distances.