Research on Orbital Angular Momentum Multiplexing Communication System Based on Neural Network Inversion of Phase

Abstract

An adaptive optical wavefront recovery method based on a residual attention network is proposed for the degradation of an Orbital Angular Momentum multiplexing communication system performance caused by atmospheric turbulence in free-space optical communication. To prevent the degeneration phenomenon of neural networks, the residual network is used as the backbone network, and a multi-scale residual hybrid attention network is constructed. Distributed feature extraction by convolutional kernels at different scales is used to enhance the network’s ability to represent light intensity image features. The attention mechanism is used to improve the recognition rate of the network for broken light spot features. The network loss function is designed by combining realistic evaluation indexes so as to obtain Zernike coefficients that match the actual wavefront aberration. Simulation experiments are carried out for different atmospheric turbulence intensity conditions, and the results show that the residual attention network can reconstruct the turbulent phase quickly and accurately. The peaks to valleys of the recovered residual aberrations were between 0.1 and 0.3 rad, and the root means square was between 0.02 and 0.12 rad. The results obtained by the residual attention network are better than those of the conventional network at different SNRs.