论文标题
衍射深神经网络基于海洋湍流中涡流束的自适应光学方案
Diffractive deep neural network based adaptive optics scheme for vortex beam in oceanic turbulence
论文作者
论文摘要
在水下无线光学通信（UWOC）系统中传播时，携带轨道角动量（OAM）携带轨道角动量（OAM）会受到海洋湍流（OT）的干扰。自适应光学元件（AO）用于补偿失真并改善UWOC系统的性能。在这项工作中，我们提出了一个基于衍射的深神经网络（DDNN）的AO方案，以补偿由OT引起的失真，其中训练了DDNN，以获得涡流束的失真强度分布与其相应相位屏幕的映射。在实验中获得的扭曲涡流束的强度模式输入了DDNN模型，并且可以使用预测的相屏幕实时补偿失真。实验结果表明，所提出的方案可以快速提取扭曲的涡流束的强度模式的特征，并准确输出预测的相屏幕。即使使用强大的OT，也可以显着提高补偿涡流束的模式纯度。我们的计划可能为AO技术提供新的途径，并有望促进UWOC系统的沟通质量。
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.
