Information transmission through parallel multi-task-based recognition of high-resolution multiplexed orbital angular momentum

Abstract

Orbital angular momentums (OAMs) greatly enhance the channel capacity in free-space optical communication. However, demodulation of superposed OAM to recognize them separately is always difficult, especially upon multiplexing more OAMs. In this work, we report a directly recognition of multiplexed fractional OAM modes, without separating them, at a resolution of 0.1 with high accuracy, using a multi-task deep learning (MTDL) model, which has not been reported before. Namely, two-mode, four-mode, and eight-mode superposed OAM beams, experimentally generated with a hologram carrying both phase and amplitude information, are well recognized by the suitable MTDL model. Two applications in information transmission are presented: the first is for 256-ary OAM shift keying via multiplexed fractional OAMs; the second is for OAM division multiplexed information transmission in an eightfold speed. The encouraging results will expand the capacity in future free-space optical communication.

Graphical abstract

Keywords

information transmission / orbital angular momentum / multi-task deep learning / holographic multiplexing / structured light

References

[1] A. E. Willner , H. Huang , Y. Yan , Y. Ren , N. Ahmed , G. Xie , C. Bao , L. Li , Y. Cao , Z. Zhao , J. Wang , M. P. J. Lavery , M. Tur , S. Ramachandran , A. F. Molisch , N. Ashrafi , S. Ashrafi . Optical communications using orbital angular momentum beams. Adv. Opt. Photonics, 2015, 7(1): 66

[2] H. Rubinsztein-Dunlop , A. Forbes , M. V. Berry , M. R. Dennis , D. L. Andrews , M. Mansuripur , C. Denz , C. Alpmann , P. Banzer , T. Bauer , E. Karimi , L. Marrucci , M. Padgett , M. Ritsch-Marte , N. M. Litchinitser , N. P. Bigelow , C. Rosales-Guzmán , A. Belmonte , J. P. Torres , T. W. Neely , M. Baker , R. Gordon , A. B. Stilgoe , J. Romero , A. G. White , R. Fickler , A. E. Willner , G. Xie , B. McMorran , A. M. Weiner . Roadmap on structured light. J. Opt., 2017, 19(1): 013001

[3] L. Allen , M. Beijersbergen , R. Spreeuw , J. Woerdman . Orbital angular momentum of light and transformation of Laguerre‒Gaussian laser modes. Phys. Rev. A, 1992, 45(11): 8185

[4] J. P. Yin , W. J. Gao , Y. F. Zhu . Generation of dark hollow beams and their applications. Prog. Opt., 2003, 45(11): 119

[5] M.J. Padgett, Orbital angular momentum 25 years on, Opt. Express 25(10), 11265 (2017) (Invited)

[6] Y. J. Shen , X. J. Wang , Z. W. Xie , C. J. Min , X. Fu , Q. Liu , M. L. Gong , X. C. Yuan . Optical vortices 30 years on: OAM manipulation from topological charge to multiple singularities. Light Sci. Appl., 2019, 8(1): 90

[7] A. Forbes , S. Ramachandran , Q. W. Zhan . Photonic angular momentum: Progress and perspectives. Nanophotonics, 2022, 11(4): 625

[8] S. J. Li , Z. Y. Li , G. S. Huang , X. B. Liu , R. Q. Li , X. Y. Cao . Digital coding transmissive metasurface for multi-OAM-beam. Front. Phys., 2022, 17(6): 62501

[9] L. Jin , Y. W. Huang , Z. W. Jin , R. C. Devlin , Z. G. Dong , S. T. Mei , M. H. Jiang , W. T. Chen , Z. Wei , H. Liu , J. H. Teng , A. Danner , X. P. Li , S. M. Xiao , S. Zhang , C. Y. Yu , J. K. W. Yang , F. Capasso , C. W. Qiu . Dielectric multi-momentum meta-transformer in the visible. Nat. Commun., 2019, 10(1): 4789

[10] X. Y. Fang , H. J. Wang , H. C. Yang , Z. L. Ye , Y. M. Wang , Y. Zhang , X. P. Hu , S. N. Zhu , M. Xiao . Multichannel nonlinear holography in a two-dimensional nonlinear photonic crystal. Phys. Rev. A, 2020, 102(4): 043506

[11] J. J. Guo , Y. P. Zhang , H. Ye , L. Y. Wang , P. C. Chen , D. J. Mao , C. Z. Xie , Z. H. Chen , X. W. Wu , M. Xiao , Y. Zhang . Spatially structured-mode multiplexing holography for high-capacity security encryption. ACS Photonics, 2023, 10(3): 757

[12] A. E. Willner , Z. Zhao , C. Liu , R. Zhang , H. Song , K. Pang , K. Manukyan , H. Song , X. Su , G. Xie , Y. Ren , Y. Yan , M. Tur , A. F. Molisch , R. W. Boyd , H. Zhou , N. Hu , A. Minoofar , H. Huang . Perspectives on advances in high-capacity, free-space communications using multiplexing of orbital-angular-momentum beams. APL Photonics, 2021, 6(3): 030901

[13] J. Lin , X. C. Yuan , S. H. Tao , R. E. Burge . Multiplexing free-space optical signals using superimposed collinear orbital angular momentum states. Appl. Opt., 2007, 46(21): 4680

[14] J. Wang , J. Y. Yang , I. M. Fazal , N. Ahmed , Y. Yan , H. Huang , Y. X. Ren , Y. Yue , S. Dolinar , M. Tur , A. E. Willner . Terabit free-space data transmission employing orbital angular momentum multiplexing. Nat. Photonics, 2012, 6(7): 488

[15] N. Bozinovic , Y. Yue , Y. Ren , M. Tur , P. Kristensen , H. Huang , A. E. Willner , S. Ramachandran . Terabit-scale orbital angular momentum mode division multiplexing in fibers. Science, 2013, 340(6140): 1545

[16] G. Vallone , V. D’Ambrosio , A. Sponselli , S. Slussarenko , L. Marrucci , F. Sciarrino , P. Villoresi . Free-space quantum key distribution by rotation-invariant twisted photons. Phys. Rev. Lett., 2014, 113(6): 060503

[17] H.HuangG.XieY.YanN.AhmedY.RenY.YueD.RogawskiM.J. WillnerB.I. ErkmenK.M. BirnbaumS.J. DolinarM.P. J. LaveryM.J. PadgettM.TurA.E. Willner, 100 Tbit/s free-space data link enabled by three-dimensional multiplexing of orbital angular momentum, polarization, and wave-length, Opt. Lett. 39(2), 197 (2014)

[18] M. Krenn , R. Fickler , M. Fink , J. Handsteiner , M. Malik , T. Scheidl , R. Ursin , A. Zeilinger . Communication with spatially modulated light through turbulent air across Vienna. New J. Phys., 2014, 16(11): 113028

[19] A. J. Willner , Y. Ren , G. Xie , Z. Zhao , Y. Cao , L. Li , N. Ahmed , Z. Wang , Y. Yan , P. Liao , C. Liu , M. Mirhosseini , R. W. Boyd , M. Tur , A. E. Willner . Experimental demonstration of 20 Gbit/s data encoding and 2 ns channel hopping using orbital angular momentum modes. Opt. Lett., 2015, 40(24): 5810

[20] M. Krenn , J. Handsteiner , M. Fink , R. Fickler , R. Ursin , M. Malik , A. Zeilinger . Twisted light transmission over 143 km. Proc. Natl. Acad. Sci. USA, 2016, 113(48): 13648

[21] H. Zhang , J. Zeng , X. Y. Lu , Z. Y. Wang , C. L. Zhao , Y. J. Cai . Review on fractional vortex beam. Nanophotonics, 2022, 11(2): 241

[22] Z. W. Liu , S. Yan , H. G. Liu , X. F. Chen . Superhigh-resolution recognition of optical vortex modes assisted by a deep-learning method. Phys. Rev. Lett., 2019, 123(18): 183902

[23] Y. Na , D. K. Ko . Adaptive demodulation by deep-learning-based identification of fractional orbital angular momentum modes with structural distortion due to atmospheric turbulence. Sci. Rep., 2021, 11(1): 23505

[24] J. Leach , M. Padgett , S. Barnett , S. Franke-Arnold , J. Courtial . Measuring the orbital angular momentum of a single photon. Phys. Rev. Lett., 2002, 88(25): 257901

[25] G. Berkhout , M. Lavery , J. Courtial , M. Beijersbergen , M. Padgett . Efficient sorting of orbital angular momentum states of light. Phys. Rev. Lett., 2010, 105(15): 153601

[26] M. Mirhosseini , M. Malik , Z. Shi , R. Boyd . Efficient separation of the orbital angular momentum eigenstates of light. Nat. Commun., 2013, 4(1): 2781

[27] Y. H. Wen , I. Chremmos , Y. J. Chen , J. B. Zhu , Y. F. Zhang , S. Y. Yu . Spiral transformation for high-resolution and efficient sorting of optical vortex modes. Phys. Rev. Lett., 2018, 120(19): 193904

[28] G. Gibson , J. Courtial , M. J. Padgett , M. Vasnetsov , V. Pas’ko , S. M. Barnett , S. Franke-Arnold . Free-space information transfer using light beams carrying orbital angular momentum. Opt. Express, 2004, 12(22): 5448

[29] T. Doster , A. T. Watnik . Machine learning approach to OAM beam demultiplexing via convolutional neural networks. Appl. Opt., 2017, 56(12): 3386

[30] S. Lohani , E. M. Knutson , M. O’Donnell , S. D. Huver , R. T. Glasser . On the use of deep neural networks in optical communications. Appl. Opt., 2018, 57(15): 4180

[31] S. Park , L. Cattell , J. Nichols , A. Watnik , T. Doster , G. Rohde . De-multiplexing vortex modes in optical communications using transport-based pattern recognition. Opt. Express, 2018, 26(4): 4004

[32] J. Li , M. Zhang , D. S. Wang . Adaptive demodulator using machine learning for orbital angular momentum shift keying. IEEE Photonics Technol. Lett., 2017, 29(17): 1455

[33] Q. S. Zhao , S. Q. Hao , Y. Wang , L. Wang , X. F. Wan , C. L. Xu . Mode detection of misaligned orbital angular momentum beams based on convolutional neural network. Appl. Opt., 2018, 57(35): 10152

[34] S. Lohani , R. T. Glasser . Turbulence correction with artificial neural networks. Opt. Lett., 2018, 43(11): 2611

[35] Q. H. Tian , Z. Li , K. Hu , L. Zhu , X. L. Pan , Q. Zhang , Y. J. Wang , F. Tian , X. L. Yin , X. J. Xin . Turbo-coded 16-ary OAM shift keying FSO communication system combining the CNN-based adaptive demodulator. Opt. Express, 2018, 26(21): 27849

[36] J. Li , M. Zhang , D. S. Wang , S. J. Wu , Y. Y. Zhan . Joint atmospheric turbulence detection and adaptive demodulation technique using the CNN for the OAM‒FSO communication. Opt. Express, 2018, 26(8): 10494

[37] Y. Z. Shi , Z. H. Ma , H. Y. Chen , Y. G. Ke , Y. Chen , X. X. Zhou . High-resolution recognition of FOAM modes via an improved EfficientNet V2 based convolutional neural network. Front. Phys., 2024, 19(3): 32205

[38] H.LuanD.LinK.LiW.MengM.GuX.Fang, 768-ary Laguerre‒Gaussian-mode shift keying free-space optical communication based on convolutional neural networks, Opt. Express 29(13), 19807 (2021)

[39] A. Maurer , M. Pontil , B. Paredes . The benefit of multitask representation learning. J. Mach. Learn. Res., 2016, 17(1): 2853

[40] Z. X. Mao , H. Y. Yu , M. Xia , S. Z. Pan , D. Wu , Y. L. Yin , Y. Xia , J. P. Yin . Broad bandwidth and highly efficient recognition of optical vortex modes achieved by the neural-network approach. Phys. Rev. Appl., 2020, 13(3): 034063

[41] J. Davis , D. Cottrell , J. Campos , M. Yzuel , I. Moreno . Encoding amplitude information onto phase-only filters. Appl. Opt., 1999, 38(23): 5004

[42] E. Bolduc , N. Bent , E. Santamato , E. Karimi , R. W. Boyd . Exact solution to simultaneous intensity and phase encryption with a single phase-only hologram. Opt. Lett., 2013, 38(18): 3546

[43] K.ThungC.Wee, A brief review on multi-task learning, Multimedia Tools Appl. 77(22), 29705 (2018)

[44] Y. Zhang , Q. Yang . An overview of multi-task learning. Natl. Sci. Rev., 2018, 5(1): 30

[45] C.SzegedyL.WeiJ.YangqingP.SermanetS.ReedD.AnguelovD.ErhanV.VanhouckeA.Rabinovich, Going deeper with convolutions, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, 1 (2015)

[46] S.XieR.B. GirshickP.DollárZ.TuK.He, Aggregated residual transformations for deep neural networks, in: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 5987 (2017)

[47] R.CollobertJ.Weston, A unified architecture for natural language processing: deep neural networks with multitask learning, in: Proceedings of the 25th International Conference on Machine Learning, Association for Computing Machinery: Helsinki, Finland, 160–167 (2008)

[48] X. Cui , W. Zhang , U. Finkler , G. Saon , M. Picheny , D. Kung . Distributed training of deep neural network acoustic models for automatic speech recognition: A comparison of current training strategies. IEEE Signal Process. Mag., 2020, 37(3): 39

[49] R.B. GirshickR.C. N. N. Fast, IEEE International Conference on Computer Vision (ICCV), 1440 (2015)

[50] B. L. Li , H. T. Luan , K. Y. Li , Q. Y. Chen , W. J. Meng , K. Cheng , M. Gu , X. Y. Fang . Orbital angular momentum optical communications enhanced by artificial intelligence. J. Opt., 2022, 24(9): 094003

[51] Z. S. Wan , H. Wang , Q. Liu , X. Fu , Y. J. Shen . Ultra-degree-of-freedom structured light for ultracapacity information carriers. ACS Photonics, 2023, 10(7): 2149

Declarations

The authors declare that they have no competing interests and there are no conflicts.

Acknowledgements

Financial supports are from the National Natural Science Foundation of China (Grant Nos. 12174115, 91836103, and 11834003).

RIGHTS & PERMISSIONS

2024 Higher Education Press