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.

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Orbital angular momentum (OAM) has been widely used in underwater wireless optical communication (UWOC) systems due to the mutual orthogonality between modes. However, wavefront distortion caused by oceanic turbulence (OT) on the OAM mode seriously affects its mode recognition and communication quality. In this work, we propose a hybrid opto-electronic deep neural network (HOEDNN) based OAM mode recognition scheme. The HOEDNN model consists of a diffractive DNN (DDNN) and convolutional neural network (CNN), where the DDNN is trained to obtain the mapping between intensity patterns of a distorted OAM mode and intensity distributions without OT interference, and the CNN is used to recognize the output of the DDNN. The diffractive layers of the trained DDNN model are solidified, fabricated, and loaded into a spatial light modulator, and the results recorded by a charge-coupled device camera are processed and fed into the trained CNN model. The results show that the proposed scheme can overcome the interference of OT to OAM modes and recognize accurately azimuthal and radial indices. The OAM mode recognition scheme based on HOEDNN has potential application value in UWOC systems.

Orbital angular momentum (OAM) has the characteristics of mutual orthogonality between modes, and been applied to underwater wireless optical communication (UWOC) system to increase the channel capacity. In this work, we propose a diffractive deep neural network (DDNN) based OAM mode recognition scheme, where the DDNN is trained to capture the features of the intensity distribution of the OAM modes and output the corresponding azimuthal indices and radial indices. The results show that the proposed scheme can recognize the azimuthal indices and radial indices of the OAM modes accurately and quickly. In addition, the proposed scheme can resist weak oceanic turbulence (OT), and exhibit excellent ability to recognize OAM modes in a strong OT environment. The DDNN-based OAM mode recognition scheme has potential application value to UWOC system.