Machine learning identification of fractional-order vortex beam diffraction process

TL;DR

This paper presents a deep learning approach using an improved ResNet model to accurately recognize fractional-order vortex beam modes and propagation distances under turbulent atmospheric conditions, enhancing optical communication reliability.

Contribution

The study introduces a novel ResNet-based deep learning method for recognizing FOAM modes in diffraction conditions, considering atmospheric turbulence effects, which surpasses traditional recognition techniques.

Findings

Achieved 99.69% recognition accuracy for FOAM modes under turbulence.

Successfully distinguished ultra-fine FOAM modes and propagation distances.

Demonstrated robustness of the method in turbulent atmospheric environments.

Abstract

Fractional-order vortex beams possess fractional orbital angular momentum (FOAM) modes, which theoretically have the potential to increase transmission capacity infinitely. Therefore, they have significant application prospects in the fields of measurement, optical communication and micro-particle manipulation. However, when fractional-order vortex beams propagate in free space, the discontinuity of the helical phase makes them susceptible to diffraction in practical applications, thereby affecting the accuracy of OAM mode recognition and severely limiting the use of FOAM-based optical communication. Achieving machine learning recognition of fractional-order vortex beams under diffraction conditions is currently an urgent and unreported issue. Based on ResNet, a deep learning (DL) method of accurately recognizing the propagation distance and topological charge of fractional-order vortex beam diffraction process is proposed in this work. Utilizing both experimentally measured and numerically simulated intensity distributions, a dataset of vortex beam diffraction intensity patterns in atmospheric turbulence environments is created. An improved 101-layer ResNet structure based on transfer learning is employed to achieve accurate and efficient recognition of the FOAM model at different propagation distances. Experimental results show that the proposed method can accurately recognize FOAM modes with a propagation distance of 100 cm, a spacing of 5 cm, and a mode spacing of 0.1 under turbulent conditions, with an accuracy of 99.69%. This method considers the effect of atmospheric turbulence during spatial transmission, allowing the recognition scheme to achieve high accuracy even in special environments. It has the ability to distinguish ultra-fine FOAM modes and propagation distances, which cannot be achieved by traditional methods.