Estimation of atmospheric turbulence intensity based on deep learning

Abstract

The free space optical communication system will inevitably be affected by atmospheric turbulence when working, which will increase the bit error rate at the receiving end, and seriously affects the communication quality, so it is necessary to analyze the characteristics of atmospheric turbulence. In the paper, the powerful feature extraction and data processing capabilities of the convolutional neural network is used to estimate the atmospheric turbulence refractive index structure constant C2n , and the influence of transmission distance, beam multiplexing technology and beam mode is analyzed on the estimation effect. The results show that this method can effectively estimate C2n , and the error can be controlled within a reasonable range.