Multidimensional Information Assisted Deep Learning Realizing Flexible Recognition of Vortex Beam Modes
Jiale Zhao, Zijing Zhang, Yiming Li, Longzhu Cen
Abstract
Due to countless orthogonal eigenstates, light beams with orbital angular momentum(OAM) have a large potential information capacity. Recently, deep learning has been extensively applied in recognition of OAM mode. However, previous deep learning methods require a constant distance between laser and receiver. The accuracy will drop quickly if the distance of testing set deviates from the training set. Previous deep learning methods also have difficulty distinguishing OAM modes with positive and negative topological charges. In order to further exploit the huge potential of the countless dimension of state space, we proposed multidimensional information assisted deep learning flexible recognition (MIADLFR) method to make use of both intensity and angular spectrum information for the first time to achieve recognition of OAM modes unlimited by the sign of TC and distance with high accuracy. Also, MIADLFR can reduce the computational complexity significantly and requires much smaller training set.