Measuring OAM by the hybrid scheme of interference and convolutional neural network

Abstract. The atmospheric turbulence can cause wavefront distortion when vortex beam carrying orbital angular momentum (OAM) propagates in free space. This brings challenges to the recognition of OAM modes. To realize effective recognition of multichannel vortex beams in atmospheric turbulence, a hybrid interference-convolutional neural network (CNN) scheme is proposed. Here, we compare two different approaches to identify the topological charges under different turbulence levels: the first is based on CNN only and the second is the hybrid scheme of interference and CNN. The simulation shows that the recognition performance of multiple vortex beams under different turbulence levels is improved by our hybrid scheme. Compared with the traditional CNN-based method, the interference-CNN scheme can further identify the sign of topological charge. Moreover, we generalize its feasibility through different kinds of vortex beams with a radial index of p  ≠  0. This provides a versatile tool for large-capacity optical communication based on OAM modes.

[1]  Yi-dong Liu,et al.  Synergy Effect of Dually Superposed Orbital Angular Momentum States in Atmospheric Turbulence , 2017, IEEE Photonics Journal.

[2]  Vasilis Ntziachristos,et al.  Transmission of vector vortex beams in dispersive media , 2020, Advanced Photonics.

[3]  Min Zhang,et al.  Joint atmospheric turbulence detection and adaptive demodulation technique using the CNN for the OAM-FSO communication. , 2018, Optics express.

[4]  S. Barnett,et al.  Interferometric methods to measure orbital and spin, or the total angular momentum of a single photon. , 2004, Physical review letters.

[5]  W. C. Soares,et al.  Unveiling a truncated optical lattice associated with a triangular aperture using light's orbital angular momentum. , 2010, Physical review letters.

[6]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[7]  S. M. Zhao,et al.  Aberration corrections for free-space optical communications in atmosphere turbulence using orbital angular momentum states. , 2012, Optics Express.

[8]  Gabriel Molina-Terriza,et al.  Management of the angular momentum of light: preparation of photons in multidimensional vector states of angular momentum. , 2002, Physical review letters.

[9]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Kishan Dholakia,et al.  Measuring the orbital angular momentum of partially coherent optical vortices through singularities in their cross-spectral density functions. , 2012, Optics letters.

[11]  Qiang Xu,et al.  Calculating the torque of the optical vortex tweezer to the ellipsoidal micro-particles , 2015 .

[12]  J. Hou,et al.  Beam-holding property analysis of the perfect optical vortex beam transmitting in atmospheric turbulence , 2020 .

[13]  X. Xin,et al.  Bit error rate performance analysis for the orbital angular momentum of a multiplexed optical communication system based on multistaircase spiral phase plates , 2020, Laser Physics Letters.

[14]  S. Barnett,et al.  Free-space information transfer using light beams carrying orbital angular momentum. , 2004, Optics express.

[15]  J. P. Woerdman,et al.  Orbital angular momentum of light and the transformation of Laguerre-Gaussian laser modes. , 1992, Physical review. A, Atomic, molecular, and optical physics.

[16]  Jianlin Zhao,et al.  Generation of polarization and phase singular beams in fibers and fiber lasers , 2021 .

[17]  Saikat Guha,et al.  Ultimate channel capacity of free-space optical communications (Invited) , 2005 .

[18]  Qu Shi-liang,et al.  Rotational motions of optically trapped microscopic particles by a vortex femtosecond laser , 2012 .

[19]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[20]  Timothy Doster,et al.  Machine learning approach to OAM beam demultiplexing via convolutional neural networks. , 2017, Applied optics.

[21]  Silvia Carrasco,et al.  Digital spiral imaging. , 2005, Optics express.

[22]  Xianfeng Chen,et al.  Superhigh-Resolution Recognition of Optical Vortex Modes Assisted by a Deep-Learning Method. , 2019, Physical review letters.

[23]  Giovanni Milione,et al.  The Resilience of Hermite– and Laguerre–Gaussian Modes in Turbulence , 2019, Journal of Lightwave Technology.

[24]  Maxime Irene Dedo,et al.  Efficient Recognition of the Propagated Orbital Angular Momentum Modes in Turbulences With the Convolutional Neural Network , 2019, IEEE Photonics Journal.

[25]  Maxime Irene Dedo,et al.  OAM mode recognition based on joint scheme of combining the Gerchberg–Saxton (GS) algorithm and convolutional neural network (CNN) , 2020 .

[26]  A. Zeilinger,et al.  Twisted light transmission over 143 km , 2016, Proceedings of the National Academy of Sciences.

[27]  Yihua Bai,et al.  Measuring high orbital angular momentum of vortex beams with an improved multipoint interferometer , 2020 .

[28]  Joseph F. Murray,et al.  Convolutional Networks Can Learn to Generate Affinity Graphs for Image Segmentation , 2010, Neural Computation.

[29]  Feng Tian,et al.  Turbo-coded 16-ary OAM shift keying FSO communication system combining the CNN-based adaptive demodulator. , 2018, Optics express.

[30]  Manzhu Ke,et al.  Particle manipulation with acoustic vortex beam induced by a brass plate with spiral shape structure , 2016 .

[31]  Chunqing Gao,et al.  Influences of atmospheric turbulence effects on the orbital angular momentum spectra of vortex beams , 2016 .

[32]  A. Willner,et al.  Terabit free-space data transmission employing orbital angular momentum multiplexing , 2012, Nature Photonics.

[33]  Carmelo Rosales-Guzmán,et al.  Manipulation of Orbital-Angular-Momentum Spectrum Using Pinhole Plates , 2019 .

[34]  Yangjian Cai,et al.  Partially coherent vortex beams: Fundamentals and applications , 2020, Science China Physics, Mechanics & Astronomy.

[35]  W Sibbett,et al.  Controlled Rotation of Optically Trapped Microscopic Particles , 2001, Science.

[36]  Yoshihiko Arita,et al.  Optical trapping with structured light: a review , 2021, Advanced Photonics.

[37]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..