Machine Learning-Based Beam Pointing Error Reduction for Satellite-Ground FSO Links

Free space optical (FSO) communication, which has the potential to meet the demand for high-data-rate communications between satellites and ground stations, requires accurate alignment between the transmitter and receiver to establish a line-of-sight channel link. In this paper, we propose a machine learning (ML)-based approach to reduce beam pointing errors in FSO satellite-to-ground communications subjected to satellite vibration and weak atmospheric turbulence. ML models are utilized to find the optimal gain, which plays a crucial role in reducing pointing error displacement in a closed-loop FSO system. In designing the FSO environment, we employ several system model parameters, including control and system matrix components of the transmitter and receiver, noise parameters for the optical channel, irradiance, and the scintillation index of the signal. To predict the gain matrix of the closed-loop system, ML methods, such as tree-based algorithms, and a 1D convolutional neural network (Conv1D) are applied. Experimental results show that the Conv1D model outperforms other ML methods in gain value prediction, helping to maintain the beam position centered on the receiver aperture, minimizing beam pointing errors. When constructing a closed-loop system based on the Conv1D model, the error variance of the pointing error displacement was obtained as 0.012 and 0.015 in clear weather and light fog conditions, respectively. In addition, this research analyzes the impact of input features in a closed-loop FSO system, and compares the pointing error performance of the closed-loop setup to the conventional open-loop setup under weak turbulence.