Deep learning-based prediction of atmospheric turbulence toward satellite-to-ground laser communication
Haoran Yu, Lang Li, Yifu Hou, Yalin Li, Ci Yin, Chunqing Gao, Shiyao Fu
Abstract
Atmospheric turbulence is one of the key factors that affect the stability and performance of satellite-to-ground laser communication (SGLC). Predicting turbulence could provide a decisive strategy for the SGLC system to ensure communication performance and is thus of great significance. In this Letter, we proposed a hybrid multi-step prediction method for atmospheric turbulence. In the proof-of-concept experiment, we collected Fried parameters (representing turbulence strength) along the SGLC link continuously for more than 3 months at the Miyun satellite ground station, near Beijing, China, and then trained the model for prediction. The favorable experimental results illustrate that the proposal can achieve 4-h prediction of turbulence Fried parameter at a resolution of 10 min, with performance increase of 7.54%, evaluated by mean absolute percentage error (MAPE).