Diffractive deep neural network based adaptive optics scheme for vortex beam in oceanic turbulence

Abstract

Vortex beam carrying orbital angular momentum (OAM) is disturbed by oceanic turbulence (OT) when propagating in underwater wireless optical communication (UWOC) system. Adaptive optics (AO) is a powerful technique used to compensate for distortion and improve the performance of the UWOC system. In this work, we propose a diffractive deep neural network (DDNN) based AO scheme to compensate for the distortion caused by OT, where the DDNN is trained to obtain the mapping between the distortion intensity distribution of the vortex beam and its corresponding phase screen representing OT. In the experiment, the distorted vortex beam is input into the DDNN model where the diffractive layers are solidified and fabricated, and the intensity distribution of the modulated light field of the vortex beam can be recorded. The experiment results show that the proposed scheme can extract quickly the characteristics of the intensity pattern of the distorted vortex beam, and the predicted compensation phase screen can correct the distortion caused by OT in time. The mode purity of the compensated vortex beam is significantly improved, even with a strong OT. Our scheme may provide a new avenue for AO techniques, and is expected to promote the communication quality of UWOC system immediately.