Multidimensional Information Assisted Deep Learning Realizing Flexible Recognition of Vortex Beam Modes

TL;DR

This paper introduces a novel deep learning method that uses multidimensional information, including spectrum data, to recognize vortex beam modes regardless of distance and topological charge sign, enhancing optical communication capabilities.

Contribution

The paper presents the first use of multidimensional data, including spectrum, in deep learning for vortex beam recognition, overcoming previous limitations related to distance and charge sign.

Findings

Recognition of OAM modes is achieved regardless of distance.

The method can distinguish positive and negative topological charges.

Enhanced capacity for optical communication using vortex beams.

Abstract

Because of the unlimited range of state space, orbital angular momentum (OAM) as a new degree of freedom of light has attracted great attention in optical communication field. Recently there are a number of researches applying deep learning on recognition of OAM modes through atmospheric turbulence. However, there are several limitations in previous deep learning recognition methods. They all require a constant distance between the laser and receiver, which makes them clumsy and not practical. As far as we know, previous deep learning methods cannot sort vortex beams with positive and negative topological charges, which can reduce information capacity. A Multidimensional Information Assisted Deep Learning Flexible Recognition (MIADLFR) method is proposed in this letter. In MIADLR we utilize not only the intensity profile, also spectrum information to recognize OAM modes unlimited by distance and sign of topological charge (TC). As far as we know, we first make use of multidimensional information to recognize OAM modes and we first utilize spectrum information to recognize OAM modes. Recognition of OAM modes unlimited by distance and sign of TC achieved by MIADLFR method can make optical communication and detection by OAM light much more attractive.