Predicting the orbital angular momentum of atmospheric turbulence for OAM-based free-space optical communication

Abstract

Spatial modes of light are susceptible to distortion, particularly by the presence of turbulence in atmospheric free-space links. The scattering of one mode to another disrupts the orthogonality among distinct orbital angular momentum (OAM) modes, leading to modal crosstalk between multiple channels. To enhance the performance of OAM-multiplexed free-space optical (FSO) communication, a convolutional neural network (CNN)-based turbulent OAM approach is proposed for compensating turbulence, with a specific focus on predicting the OAM of turbulence itself. An operator approach is utilized to extract the OAM component of atmospheric turbulence and the CNN is trained to predict the turbulent OAM coefficients. By employing the proposed network, the received power of the OAM-based FSO link can be improved by more than 10 dB under weak to strong turbulence conditions. Compared to Zernike modes, the turbulent OAM modes characterize most of the turbulence information using only a small number of orders. After compensation, when the strong turbulence strength D/r0 = 4, the received power of the transmitted beams with turbulent OAM improves by 4 dB over that with Zernike. Additionally, the crosstalk of multiplexed channels with turbulent OAM is reduced by 10 dB over that with Zernike under varying turbulence conditions.