Hybrid opto-electronic deep neural network based orbital angular momentum mode recognition scheme in oceanic turbulence

Abstract

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.