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

TL;DR

This paper introduces a diffractive deep neural network-based adaptive optics scheme to correct vortex beam distortions caused by oceanic turbulence in underwater optical communication, significantly improving mode purity and system performance.

Contribution

It presents a novel DDNN-based adaptive optics method for real-time distortion compensation in underwater vortex beam communication systems.

Findings

The DDNN accurately predicts phase screens from distorted intensity patterns.

The scheme significantly improves vortex beam mode purity under strong oceanic turbulence.

Real-time distortion correction enhances underwater optical communication quality.

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 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 representating OT. The intensity pattern of the distorted vortex beam obtained in the experiment is input to the DDNN model, and the predicted phase screen can be used to compensate the distortion in real time. The experiment results show that the proposed scheme can extract quickly the characteristics of the intensity pattern of the distorted vortex beam, and output accurately the predicted phase screen. 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.