Abstract

A set of laser beams carrying orbital angular momentum is designed with the objective of establishing an effective underwater communication link. Messages are constructed using unique Laguerre–Gauss beams, which can be combined to represent four bits of information. We report on the experimental results where the beams are transmitted through highly turbid water, reaching approximately 12 attenuation lengths. We measured the signal-to-noise ratio in each test scenario to provide characterization of the underwater environment. A convolutional neural network was developed to decode the received images with the objective of successfully classifying messages quickly. We demonstrate near-perfect classification in all scenarios, provided the training set includes some images taken under the same underwater conditions.

© 2020 Optical Society of America

Full Article  |  PDF Article
OSA Recommended Articles
Designing laser beams carrying OAM for a high-performance underwater communication system

Svetlana Avramov-Zamurovic, Abbie T. Watnik, James R. Lindle, and K. Peter Judd
J. Opt. Soc. Am. A 37(5) 876-887 (2020)

Propagation of modulated optical beams carrying orbital angular momentum in turbid water

Brandon Cochenour, Kaitlyn Morgan, Keith Miller, Eric Johnson, Kaitlin Dunn, and Linda Mullen
Appl. Opt. 55(31) C34-C38 (2016)

Propagation of orbital angular momentum modes carried by hollow vortex Gaussian beams in anisotropic atmospheric turbulence

Zonghua Hu, Huilong Liu, Jing Xia, Aga He, Zhenhua Du, Yuzhao Li, Zeyu Li, Tingting Chen, Hongbo Li, and Yanfei Lü
J. Opt. Soc. Am. A 37(9) 1404-1410 (2020)

References

  • View by:
  • |
  • |
  • |

  1. A. E. Willner, Z. Zhao, Y. Ren, L. Li, G. Xie, H. Song, C. Liu, R. Zhang, C. Bao, and K. Pang, “Underwater optical communications using orbital angular momentum-based spatial division multiplexing,” Opt. Commun. 408, 21–25 (2018).
    [Crossref]
  2. I. F. Akyildiz, D. Pompili, and T. Melodia, “Underwater acoustic sensor networks: research challenges,” Ad Hoc Netw. 3, 257–279 (2005).
    [Crossref]
  3. E. Felemban, F. K. Shaikh, U. M. Qureshi, A. A. Shelkh, and S. B. Qaisar, “Underwater sensor network applications: a comprehensive survey,” Int. J. Distrib. Sens. Netw. 11, 896832 (2015).
    [Crossref]
  4. H. M. Oubei, C. Shen, A. Kammoun, E. Zedini, K. Park, X. Sun, G. Liu, C. H. Kang, T. K. Ng, M.-S. Alouini, and B. S. Ooi, “Light based underwater wireless communications,” Jpn. J. Appl. Phys. 57, 08PA06 (2018).
    [Crossref]
  5. H. Kaushal and G. Kaddoum, “Underwater optical wireless communication,” IEEE Access 4, 1518–1547 (2016).
    [Crossref]
  6. S. R. Restaino, W. Hou, A. Kanaev, S. Matt, and C. Font, “Adaptive optics correction of a laser beam propagating underwater,” Proc. SPIE 9083, 90830R (2014).
    [Crossref]
  7. M. P. J. Lavery, D. J. Robertson, A. Sponselli, J. Courtial, N. K. Steinhoff, G. A. Tyler, A. E. Willner, and M. J. Padgett, “Efficient measurement of an optical orbital-angular-momentum spectrum comprising more than 50 states,” New J. Phys. 15, 013024 (2013).
    [Crossref]
  8. M. Cheng, L. Guo, J. Li, Q. Huang, Q. Cheng, and D. Zhang, “Propagation of an optical vortex carried by a partially coherent Laguerre–Gaussian beam in turbulent ocean,” Appl. Opt. 55, 4642–4648 (2016).
    [Crossref]
  9. A. W. Jantzi, M. G. Cockrell, L. K. Rumbaugh, and W. D. Jemison, “Mixed numerical and analytical method for investigating orbital angular momentum beam scattering in turbid water,” Opt. Eng. 58, 043104 (2019).
    [Crossref]
  10. L. Gong, Q. Zhao, H. Zhang, X. Hu, K. Huang, J. Yang, and Y. Li, “Optical orbital-angular-momentum-multiplexed data transmission under high scattering,” Light Sci. Appl. 8, 27 (2019).
    [Crossref]
  11. W. B. Wang, R. Gozali, L. Shi, L. Lindwasser, and R. R. Alfano, “Deep transmission of Laguerre–Gaussian vortex beams through turbid scattering media,” Opt. Lett. 41, 2069–2072 (2016).
    [Crossref]
  12. B. Cochenour, K. Morgan, K. Miller, E. Johnson, K. Dunn, and L. Mullen, “Propagation of modulated optical beams carrying OAM in turbid water,” Appl. Opt. 55, C34–C38 (2016).
    [Crossref]
  13. K. S. Morgan, J. K. Miller, B. M. Cochenour, W. Li, Y. Li, R. J. Watkins, and E. G. Johnson, “Free space propagation of concentric vortices through underwater turbid environments,” J. Opt. 18, 104004 (2016).
    [Crossref]
  14. C. Shen, O. Alkhazragi, X. Sun, Y. Guo, T. K. Ng, and B. S. Ooi, “Laser-based visible light communications and underwater wireless optical communications: a device perspective,” Proc. SPIE 10939, 109390E (2019).
    [Crossref]
  15. Y. Ren, L. Li, Z. Wang, S. M. Kamali, E. Arbabi, A. Arbabi, Z. Zhao, G. Xie, Y. Cao, N. Ahmed, Y. Yan, C. Liu, A. J. Willner, S. Ashrafi, M. Tur, A. Faraon, and A. E. Willner, “Orbital angular momentum-based space division multiplexing for high-capacity underwater optical communications,” Sci. Rep. 6, 33306 (2016).
    [Crossref]
  16. M. K. Karahroudi, S. A. Moosavi, A. Mobashery, B. Parmoon, and H. Saghafifar, “Performance evaluation of perfect optical vortices transmission in an underwater optical communication system,” Appl. Opt. 57, 9148–9154 (2018).
    [Crossref]
  17. S. Lohani, E. M. Knutson, M. O’Donnell, S. D. Huver, and R. T. Glasser, “On the use of deep neural networks in optical communications,” Appl. Opt. 57, 4180–4190 (2018).
    [Crossref]
  18. T. Doster and A. T. Watnik, “Machine learning approach to OAM beam demultiplexing via convolutional neural networks,” Appl. Opt. 56, 3386–3396 (2017).
    [Crossref]
  19. T. Doster and A. T. Watnik, “Measuring multiplexed OAM modes with convolutional neural networks,” in Lasers Congress (ASSL, LSC, LAC) (Optical Society of America, 2016), paper LTh3B.2.
  20. J. Li, M. Zhang, D. Wang, S. Wu, and Y. Zhan, “Joint atmospheric turbulence detection and adaptive demodulation technique using the CNN for the OAM-FSO communication,” Opt. Express 26, 10494–10508 (2018).
    [Crossref]
  21. J. Li, X. M. Zhang, and D. Wang, “Adaptive demodulator using machine learning for orbital angular momentum shift keying,” IEEE Photon. Technol. Lett. 29, 1455–1458 (2017).
    [Crossref]
  22. X. Cui, X. Yin, H. Chang, Y. Guo, Z. Zheng, Z. Sun, G. Liu, and Y. Wang, “Analysis of an adaptive orbital angular momentum shift keying decoder based on machine learning under oceanic turbulence channels,” Opt. Commun. 429, 138–143 (2018).
    [Crossref]
  23. M. Khairi, T. Mohd, S. Ibrahim, M. A. M. Yunus, M. Faramarzi, and Z. Yusuf, “Artificial neural network approach for predicting the water turbidity level using optical tomography,” Arab. J. Sci. Eng. 41, 3369–3379 (2016).
    [Crossref]
  24. P. L. Neary, A. T. Watnik, K. P. Judd, J. R. Lindle, and N. S. Flann, “Machine learning-based signal degradation models for attenuated underwater optical communication OAM beams,” Opt. Commun. 474, 126058 (2020).
    [Crossref]
  25. A. Trichili, C. B. Issaid, B. S. Ooi, and M. S. Alouini, “A CNN based structured light communication scheme for internet of underwater things applications,” submitted to IEEE Internet Things J. (2020).
    [Crossref]
  26. X. Cui, X. Yin, H. Chang, H. Liao, X. Chen, X. Xin, and Y. Wang, “Experimental study of machine-learning-based orbital angular momentum shift keying decoders in optical underwater channels,” Opt. Commun. 452, 116–123 (2019).
    [Crossref]
  27. S. Avramov-Zamurovic, A. Watnik, J. R. Lindle, and K. P. Judd, “Designing laser beams carrying OAM for a high performance underwater communication system,” J. Opt. Soc. Am. A. 37, 876–887 (2020).
    [Crossref]
  28. G. Gbur, Singular Optics (CRC Press, 2016).
  29. R. L. Nowack, “A tale of two beams: an elementary overview of Gaussian beams and Bessel beams,” Stud. Geophys. Geod. 56, 355–372 (2012).
    [Crossref]
  30. J. S. Jaffe, “A historical perspective on underwater optical imaging,” in Proceedings of the MTS/IEEE OCEANS, Bergen, Norway (2013), pp. 1–3.
  31. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, Massachusetts, USA (2015), pp. 1–9.

2020 (2)

P. L. Neary, A. T. Watnik, K. P. Judd, J. R. Lindle, and N. S. Flann, “Machine learning-based signal degradation models for attenuated underwater optical communication OAM beams,” Opt. Commun. 474, 126058 (2020).
[Crossref]

S. Avramov-Zamurovic, A. Watnik, J. R. Lindle, and K. P. Judd, “Designing laser beams carrying OAM for a high performance underwater communication system,” J. Opt. Soc. Am. A. 37, 876–887 (2020).
[Crossref]

2019 (4)

X. Cui, X. Yin, H. Chang, H. Liao, X. Chen, X. Xin, and Y. Wang, “Experimental study of machine-learning-based orbital angular momentum shift keying decoders in optical underwater channels,” Opt. Commun. 452, 116–123 (2019).
[Crossref]

A. W. Jantzi, M. G. Cockrell, L. K. Rumbaugh, and W. D. Jemison, “Mixed numerical and analytical method for investigating orbital angular momentum beam scattering in turbid water,” Opt. Eng. 58, 043104 (2019).
[Crossref]

L. Gong, Q. Zhao, H. Zhang, X. Hu, K. Huang, J. Yang, and Y. Li, “Optical orbital-angular-momentum-multiplexed data transmission under high scattering,” Light Sci. Appl. 8, 27 (2019).
[Crossref]

C. Shen, O. Alkhazragi, X. Sun, Y. Guo, T. K. Ng, and B. S. Ooi, “Laser-based visible light communications and underwater wireless optical communications: a device perspective,” Proc. SPIE 10939, 109390E (2019).
[Crossref]

2018 (6)

M. K. Karahroudi, S. A. Moosavi, A. Mobashery, B. Parmoon, and H. Saghafifar, “Performance evaluation of perfect optical vortices transmission in an underwater optical communication system,” Appl. Opt. 57, 9148–9154 (2018).
[Crossref]

S. Lohani, E. M. Knutson, M. O’Donnell, S. D. Huver, and R. T. Glasser, “On the use of deep neural networks in optical communications,” Appl. Opt. 57, 4180–4190 (2018).
[Crossref]

H. M. Oubei, C. Shen, A. Kammoun, E. Zedini, K. Park, X. Sun, G. Liu, C. H. Kang, T. K. Ng, M.-S. Alouini, and B. S. Ooi, “Light based underwater wireless communications,” Jpn. J. Appl. Phys. 57, 08PA06 (2018).
[Crossref]

A. E. Willner, Z. Zhao, Y. Ren, L. Li, G. Xie, H. Song, C. Liu, R. Zhang, C. Bao, and K. Pang, “Underwater optical communications using orbital angular momentum-based spatial division multiplexing,” Opt. Commun. 408, 21–25 (2018).
[Crossref]

J. Li, M. Zhang, D. Wang, S. Wu, and Y. Zhan, “Joint atmospheric turbulence detection and adaptive demodulation technique using the CNN for the OAM-FSO communication,” Opt. Express 26, 10494–10508 (2018).
[Crossref]

X. Cui, X. Yin, H. Chang, Y. Guo, Z. Zheng, Z. Sun, G. Liu, and Y. Wang, “Analysis of an adaptive orbital angular momentum shift keying decoder based on machine learning under oceanic turbulence channels,” Opt. Commun. 429, 138–143 (2018).
[Crossref]

2017 (2)

J. Li, X. M. Zhang, and D. Wang, “Adaptive demodulator using machine learning for orbital angular momentum shift keying,” IEEE Photon. Technol. Lett. 29, 1455–1458 (2017).
[Crossref]

T. Doster and A. T. Watnik, “Machine learning approach to OAM beam demultiplexing via convolutional neural networks,” Appl. Opt. 56, 3386–3396 (2017).
[Crossref]

2016 (7)

Y. Ren, L. Li, Z. Wang, S. M. Kamali, E. Arbabi, A. Arbabi, Z. Zhao, G. Xie, Y. Cao, N. Ahmed, Y. Yan, C. Liu, A. J. Willner, S. Ashrafi, M. Tur, A. Faraon, and A. E. Willner, “Orbital angular momentum-based space division multiplexing for high-capacity underwater optical communications,” Sci. Rep. 6, 33306 (2016).
[Crossref]

W. B. Wang, R. Gozali, L. Shi, L. Lindwasser, and R. R. Alfano, “Deep transmission of Laguerre–Gaussian vortex beams through turbid scattering media,” Opt. Lett. 41, 2069–2072 (2016).
[Crossref]

B. Cochenour, K. Morgan, K. Miller, E. Johnson, K. Dunn, and L. Mullen, “Propagation of modulated optical beams carrying OAM in turbid water,” Appl. Opt. 55, C34–C38 (2016).
[Crossref]

K. S. Morgan, J. K. Miller, B. M. Cochenour, W. Li, Y. Li, R. J. Watkins, and E. G. Johnson, “Free space propagation of concentric vortices through underwater turbid environments,” J. Opt. 18, 104004 (2016).
[Crossref]

M. Cheng, L. Guo, J. Li, Q. Huang, Q. Cheng, and D. Zhang, “Propagation of an optical vortex carried by a partially coherent Laguerre–Gaussian beam in turbulent ocean,” Appl. Opt. 55, 4642–4648 (2016).
[Crossref]

H. Kaushal and G. Kaddoum, “Underwater optical wireless communication,” IEEE Access 4, 1518–1547 (2016).
[Crossref]

M. Khairi, T. Mohd, S. Ibrahim, M. A. M. Yunus, M. Faramarzi, and Z. Yusuf, “Artificial neural network approach for predicting the water turbidity level using optical tomography,” Arab. J. Sci. Eng. 41, 3369–3379 (2016).
[Crossref]

2015 (1)

E. Felemban, F. K. Shaikh, U. M. Qureshi, A. A. Shelkh, and S. B. Qaisar, “Underwater sensor network applications: a comprehensive survey,” Int. J. Distrib. Sens. Netw. 11, 896832 (2015).
[Crossref]

2014 (1)

S. R. Restaino, W. Hou, A. Kanaev, S. Matt, and C. Font, “Adaptive optics correction of a laser beam propagating underwater,” Proc. SPIE 9083, 90830R (2014).
[Crossref]

2013 (1)

M. P. J. Lavery, D. J. Robertson, A. Sponselli, J. Courtial, N. K. Steinhoff, G. A. Tyler, A. E. Willner, and M. J. Padgett, “Efficient measurement of an optical orbital-angular-momentum spectrum comprising more than 50 states,” New J. Phys. 15, 013024 (2013).
[Crossref]

2012 (1)

R. L. Nowack, “A tale of two beams: an elementary overview of Gaussian beams and Bessel beams,” Stud. Geophys. Geod. 56, 355–372 (2012).
[Crossref]

2005 (1)

I. F. Akyildiz, D. Pompili, and T. Melodia, “Underwater acoustic sensor networks: research challenges,” Ad Hoc Netw. 3, 257–279 (2005).
[Crossref]

Ahmed, N.

Y. Ren, L. Li, Z. Wang, S. M. Kamali, E. Arbabi, A. Arbabi, Z. Zhao, G. Xie, Y. Cao, N. Ahmed, Y. Yan, C. Liu, A. J. Willner, S. Ashrafi, M. Tur, A. Faraon, and A. E. Willner, “Orbital angular momentum-based space division multiplexing for high-capacity underwater optical communications,” Sci. Rep. 6, 33306 (2016).
[Crossref]

Akyildiz, I. F.

I. F. Akyildiz, D. Pompili, and T. Melodia, “Underwater acoustic sensor networks: research challenges,” Ad Hoc Netw. 3, 257–279 (2005).
[Crossref]

Alfano, R. R.

Alkhazragi, O.

C. Shen, O. Alkhazragi, X. Sun, Y. Guo, T. K. Ng, and B. S. Ooi, “Laser-based visible light communications and underwater wireless optical communications: a device perspective,” Proc. SPIE 10939, 109390E (2019).
[Crossref]

Alouini, M. S.

A. Trichili, C. B. Issaid, B. S. Ooi, and M. S. Alouini, “A CNN based structured light communication scheme for internet of underwater things applications,” submitted to IEEE Internet Things J. (2020).
[Crossref]

Alouini, M.-S.

H. M. Oubei, C. Shen, A. Kammoun, E. Zedini, K. Park, X. Sun, G. Liu, C. H. Kang, T. K. Ng, M.-S. Alouini, and B. S. Ooi, “Light based underwater wireless communications,” Jpn. J. Appl. Phys. 57, 08PA06 (2018).
[Crossref]

Anguelov, D.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, Massachusetts, USA (2015), pp. 1–9.

Arbabi, A.

Y. Ren, L. Li, Z. Wang, S. M. Kamali, E. Arbabi, A. Arbabi, Z. Zhao, G. Xie, Y. Cao, N. Ahmed, Y. Yan, C. Liu, A. J. Willner, S. Ashrafi, M. Tur, A. Faraon, and A. E. Willner, “Orbital angular momentum-based space division multiplexing for high-capacity underwater optical communications,” Sci. Rep. 6, 33306 (2016).
[Crossref]

Arbabi, E.

Y. Ren, L. Li, Z. Wang, S. M. Kamali, E. Arbabi, A. Arbabi, Z. Zhao, G. Xie, Y. Cao, N. Ahmed, Y. Yan, C. Liu, A. J. Willner, S. Ashrafi, M. Tur, A. Faraon, and A. E. Willner, “Orbital angular momentum-based space division multiplexing for high-capacity underwater optical communications,” Sci. Rep. 6, 33306 (2016).
[Crossref]

Ashrafi, S.

Y. Ren, L. Li, Z. Wang, S. M. Kamali, E. Arbabi, A. Arbabi, Z. Zhao, G. Xie, Y. Cao, N. Ahmed, Y. Yan, C. Liu, A. J. Willner, S. Ashrafi, M. Tur, A. Faraon, and A. E. Willner, “Orbital angular momentum-based space division multiplexing for high-capacity underwater optical communications,” Sci. Rep. 6, 33306 (2016).
[Crossref]

Avramov-Zamurovic, S.

S. Avramov-Zamurovic, A. Watnik, J. R. Lindle, and K. P. Judd, “Designing laser beams carrying OAM for a high performance underwater communication system,” J. Opt. Soc. Am. A. 37, 876–887 (2020).
[Crossref]

Bao, C.

A. E. Willner, Z. Zhao, Y. Ren, L. Li, G. Xie, H. Song, C. Liu, R. Zhang, C. Bao, and K. Pang, “Underwater optical communications using orbital angular momentum-based spatial division multiplexing,” Opt. Commun. 408, 21–25 (2018).
[Crossref]

Cao, Y.

Y. Ren, L. Li, Z. Wang, S. M. Kamali, E. Arbabi, A. Arbabi, Z. Zhao, G. Xie, Y. Cao, N. Ahmed, Y. Yan, C. Liu, A. J. Willner, S. Ashrafi, M. Tur, A. Faraon, and A. E. Willner, “Orbital angular momentum-based space division multiplexing for high-capacity underwater optical communications,” Sci. Rep. 6, 33306 (2016).
[Crossref]

Chang, H.

X. Cui, X. Yin, H. Chang, H. Liao, X. Chen, X. Xin, and Y. Wang, “Experimental study of machine-learning-based orbital angular momentum shift keying decoders in optical underwater channels,” Opt. Commun. 452, 116–123 (2019).
[Crossref]

X. Cui, X. Yin, H. Chang, Y. Guo, Z. Zheng, Z. Sun, G. Liu, and Y. Wang, “Analysis of an adaptive orbital angular momentum shift keying decoder based on machine learning under oceanic turbulence channels,” Opt. Commun. 429, 138–143 (2018).
[Crossref]

Chen, X.

X. Cui, X. Yin, H. Chang, H. Liao, X. Chen, X. Xin, and Y. Wang, “Experimental study of machine-learning-based orbital angular momentum shift keying decoders in optical underwater channels,” Opt. Commun. 452, 116–123 (2019).
[Crossref]

Cheng, M.

Cheng, Q.

Cochenour, B.

Cochenour, B. M.

K. S. Morgan, J. K. Miller, B. M. Cochenour, W. Li, Y. Li, R. J. Watkins, and E. G. Johnson, “Free space propagation of concentric vortices through underwater turbid environments,” J. Opt. 18, 104004 (2016).
[Crossref]

Cockrell, M. G.

A. W. Jantzi, M. G. Cockrell, L. K. Rumbaugh, and W. D. Jemison, “Mixed numerical and analytical method for investigating orbital angular momentum beam scattering in turbid water,” Opt. Eng. 58, 043104 (2019).
[Crossref]

Courtial, J.

M. P. J. Lavery, D. J. Robertson, A. Sponselli, J. Courtial, N. K. Steinhoff, G. A. Tyler, A. E. Willner, and M. J. Padgett, “Efficient measurement of an optical orbital-angular-momentum spectrum comprising more than 50 states,” New J. Phys. 15, 013024 (2013).
[Crossref]

Cui, X.

X. Cui, X. Yin, H. Chang, H. Liao, X. Chen, X. Xin, and Y. Wang, “Experimental study of machine-learning-based orbital angular momentum shift keying decoders in optical underwater channels,” Opt. Commun. 452, 116–123 (2019).
[Crossref]

X. Cui, X. Yin, H. Chang, Y. Guo, Z. Zheng, Z. Sun, G. Liu, and Y. Wang, “Analysis of an adaptive orbital angular momentum shift keying decoder based on machine learning under oceanic turbulence channels,” Opt. Commun. 429, 138–143 (2018).
[Crossref]

Doster, T.

T. Doster and A. T. Watnik, “Machine learning approach to OAM beam demultiplexing via convolutional neural networks,” Appl. Opt. 56, 3386–3396 (2017).
[Crossref]

T. Doster and A. T. Watnik, “Measuring multiplexed OAM modes with convolutional neural networks,” in Lasers Congress (ASSL, LSC, LAC) (Optical Society of America, 2016), paper LTh3B.2.

Dunn, K.

Erhan, D.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, Massachusetts, USA (2015), pp. 1–9.

Faramarzi, M.

M. Khairi, T. Mohd, S. Ibrahim, M. A. M. Yunus, M. Faramarzi, and Z. Yusuf, “Artificial neural network approach for predicting the water turbidity level using optical tomography,” Arab. J. Sci. Eng. 41, 3369–3379 (2016).
[Crossref]

Faraon, A.

Y. Ren, L. Li, Z. Wang, S. M. Kamali, E. Arbabi, A. Arbabi, Z. Zhao, G. Xie, Y. Cao, N. Ahmed, Y. Yan, C. Liu, A. J. Willner, S. Ashrafi, M. Tur, A. Faraon, and A. E. Willner, “Orbital angular momentum-based space division multiplexing for high-capacity underwater optical communications,” Sci. Rep. 6, 33306 (2016).
[Crossref]

Felemban, E.

E. Felemban, F. K. Shaikh, U. M. Qureshi, A. A. Shelkh, and S. B. Qaisar, “Underwater sensor network applications: a comprehensive survey,” Int. J. Distrib. Sens. Netw. 11, 896832 (2015).
[Crossref]

Flann, N. S.

P. L. Neary, A. T. Watnik, K. P. Judd, J. R. Lindle, and N. S. Flann, “Machine learning-based signal degradation models for attenuated underwater optical communication OAM beams,” Opt. Commun. 474, 126058 (2020).
[Crossref]

Font, C.

S. R. Restaino, W. Hou, A. Kanaev, S. Matt, and C. Font, “Adaptive optics correction of a laser beam propagating underwater,” Proc. SPIE 9083, 90830R (2014).
[Crossref]

Gbur, G.

G. Gbur, Singular Optics (CRC Press, 2016).

Glasser, R. T.

Gong, L.

L. Gong, Q. Zhao, H. Zhang, X. Hu, K. Huang, J. Yang, and Y. Li, “Optical orbital-angular-momentum-multiplexed data transmission under high scattering,” Light Sci. Appl. 8, 27 (2019).
[Crossref]

Gozali, R.

Guo, L.

Guo, Y.

C. Shen, O. Alkhazragi, X. Sun, Y. Guo, T. K. Ng, and B. S. Ooi, “Laser-based visible light communications and underwater wireless optical communications: a device perspective,” Proc. SPIE 10939, 109390E (2019).
[Crossref]

X. Cui, X. Yin, H. Chang, Y. Guo, Z. Zheng, Z. Sun, G. Liu, and Y. Wang, “Analysis of an adaptive orbital angular momentum shift keying decoder based on machine learning under oceanic turbulence channels,” Opt. Commun. 429, 138–143 (2018).
[Crossref]

Hou, W.

S. R. Restaino, W. Hou, A. Kanaev, S. Matt, and C. Font, “Adaptive optics correction of a laser beam propagating underwater,” Proc. SPIE 9083, 90830R (2014).
[Crossref]

Hu, X.

L. Gong, Q. Zhao, H. Zhang, X. Hu, K. Huang, J. Yang, and Y. Li, “Optical orbital-angular-momentum-multiplexed data transmission under high scattering,” Light Sci. Appl. 8, 27 (2019).
[Crossref]

Huang, K.

L. Gong, Q. Zhao, H. Zhang, X. Hu, K. Huang, J. Yang, and Y. Li, “Optical orbital-angular-momentum-multiplexed data transmission under high scattering,” Light Sci. Appl. 8, 27 (2019).
[Crossref]

Huang, Q.

Huver, S. D.

Ibrahim, S.

M. Khairi, T. Mohd, S. Ibrahim, M. A. M. Yunus, M. Faramarzi, and Z. Yusuf, “Artificial neural network approach for predicting the water turbidity level using optical tomography,” Arab. J. Sci. Eng. 41, 3369–3379 (2016).
[Crossref]

Issaid, C. B.

A. Trichili, C. B. Issaid, B. S. Ooi, and M. S. Alouini, “A CNN based structured light communication scheme for internet of underwater things applications,” submitted to IEEE Internet Things J. (2020).
[Crossref]

Jaffe, J. S.

J. S. Jaffe, “A historical perspective on underwater optical imaging,” in Proceedings of the MTS/IEEE OCEANS, Bergen, Norway (2013), pp. 1–3.

Jantzi, A. W.

A. W. Jantzi, M. G. Cockrell, L. K. Rumbaugh, and W. D. Jemison, “Mixed numerical and analytical method for investigating orbital angular momentum beam scattering in turbid water,” Opt. Eng. 58, 043104 (2019).
[Crossref]

Jemison, W. D.

A. W. Jantzi, M. G. Cockrell, L. K. Rumbaugh, and W. D. Jemison, “Mixed numerical and analytical method for investigating orbital angular momentum beam scattering in turbid water,” Opt. Eng. 58, 043104 (2019).
[Crossref]

Jia, Y.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, Massachusetts, USA (2015), pp. 1–9.

Johnson, E.

Johnson, E. G.

K. S. Morgan, J. K. Miller, B. M. Cochenour, W. Li, Y. Li, R. J. Watkins, and E. G. Johnson, “Free space propagation of concentric vortices through underwater turbid environments,” J. Opt. 18, 104004 (2016).
[Crossref]

Judd, K. P.

S. Avramov-Zamurovic, A. Watnik, J. R. Lindle, and K. P. Judd, “Designing laser beams carrying OAM for a high performance underwater communication system,” J. Opt. Soc. Am. A. 37, 876–887 (2020).
[Crossref]

P. L. Neary, A. T. Watnik, K. P. Judd, J. R. Lindle, and N. S. Flann, “Machine learning-based signal degradation models for attenuated underwater optical communication OAM beams,” Opt. Commun. 474, 126058 (2020).
[Crossref]

Kaddoum, G.

H. Kaushal and G. Kaddoum, “Underwater optical wireless communication,” IEEE Access 4, 1518–1547 (2016).
[Crossref]

Kamali, S. M.

Y. Ren, L. Li, Z. Wang, S. M. Kamali, E. Arbabi, A. Arbabi, Z. Zhao, G. Xie, Y. Cao, N. Ahmed, Y. Yan, C. Liu, A. J. Willner, S. Ashrafi, M. Tur, A. Faraon, and A. E. Willner, “Orbital angular momentum-based space division multiplexing for high-capacity underwater optical communications,” Sci. Rep. 6, 33306 (2016).
[Crossref]

Kammoun, A.

H. M. Oubei, C. Shen, A. Kammoun, E. Zedini, K. Park, X. Sun, G. Liu, C. H. Kang, T. K. Ng, M.-S. Alouini, and B. S. Ooi, “Light based underwater wireless communications,” Jpn. J. Appl. Phys. 57, 08PA06 (2018).
[Crossref]

Kanaev, A.

S. R. Restaino, W. Hou, A. Kanaev, S. Matt, and C. Font, “Adaptive optics correction of a laser beam propagating underwater,” Proc. SPIE 9083, 90830R (2014).
[Crossref]

Kang, C. H.

H. M. Oubei, C. Shen, A. Kammoun, E. Zedini, K. Park, X. Sun, G. Liu, C. H. Kang, T. K. Ng, M.-S. Alouini, and B. S. Ooi, “Light based underwater wireless communications,” Jpn. J. Appl. Phys. 57, 08PA06 (2018).
[Crossref]

Karahroudi, M. K.

Kaushal, H.

H. Kaushal and G. Kaddoum, “Underwater optical wireless communication,” IEEE Access 4, 1518–1547 (2016).
[Crossref]

Khairi, M.

M. Khairi, T. Mohd, S. Ibrahim, M. A. M. Yunus, M. Faramarzi, and Z. Yusuf, “Artificial neural network approach for predicting the water turbidity level using optical tomography,” Arab. J. Sci. Eng. 41, 3369–3379 (2016).
[Crossref]

Knutson, E. M.

Lavery, M. P. J.

M. P. J. Lavery, D. J. Robertson, A. Sponselli, J. Courtial, N. K. Steinhoff, G. A. Tyler, A. E. Willner, and M. J. Padgett, “Efficient measurement of an optical orbital-angular-momentum spectrum comprising more than 50 states,” New J. Phys. 15, 013024 (2013).
[Crossref]

Li, J.

Li, L.

A. E. Willner, Z. Zhao, Y. Ren, L. Li, G. Xie, H. Song, C. Liu, R. Zhang, C. Bao, and K. Pang, “Underwater optical communications using orbital angular momentum-based spatial division multiplexing,” Opt. Commun. 408, 21–25 (2018).
[Crossref]

Y. Ren, L. Li, Z. Wang, S. M. Kamali, E. Arbabi, A. Arbabi, Z. Zhao, G. Xie, Y. Cao, N. Ahmed, Y. Yan, C. Liu, A. J. Willner, S. Ashrafi, M. Tur, A. Faraon, and A. E. Willner, “Orbital angular momentum-based space division multiplexing for high-capacity underwater optical communications,” Sci. Rep. 6, 33306 (2016).
[Crossref]

Li, W.

K. S. Morgan, J. K. Miller, B. M. Cochenour, W. Li, Y. Li, R. J. Watkins, and E. G. Johnson, “Free space propagation of concentric vortices through underwater turbid environments,” J. Opt. 18, 104004 (2016).
[Crossref]

Li, Y.

L. Gong, Q. Zhao, H. Zhang, X. Hu, K. Huang, J. Yang, and Y. Li, “Optical orbital-angular-momentum-multiplexed data transmission under high scattering,” Light Sci. Appl. 8, 27 (2019).
[Crossref]

K. S. Morgan, J. K. Miller, B. M. Cochenour, W. Li, Y. Li, R. J. Watkins, and E. G. Johnson, “Free space propagation of concentric vortices through underwater turbid environments,” J. Opt. 18, 104004 (2016).
[Crossref]

Liao, H.

X. Cui, X. Yin, H. Chang, H. Liao, X. Chen, X. Xin, and Y. Wang, “Experimental study of machine-learning-based orbital angular momentum shift keying decoders in optical underwater channels,” Opt. Commun. 452, 116–123 (2019).
[Crossref]

Lindle, J. R.

P. L. Neary, A. T. Watnik, K. P. Judd, J. R. Lindle, and N. S. Flann, “Machine learning-based signal degradation models for attenuated underwater optical communication OAM beams,” Opt. Commun. 474, 126058 (2020).
[Crossref]

S. Avramov-Zamurovic, A. Watnik, J. R. Lindle, and K. P. Judd, “Designing laser beams carrying OAM for a high performance underwater communication system,” J. Opt. Soc. Am. A. 37, 876–887 (2020).
[Crossref]

Lindwasser, L.

Liu, C.

A. E. Willner, Z. Zhao, Y. Ren, L. Li, G. Xie, H. Song, C. Liu, R. Zhang, C. Bao, and K. Pang, “Underwater optical communications using orbital angular momentum-based spatial division multiplexing,” Opt. Commun. 408, 21–25 (2018).
[Crossref]

Y. Ren, L. Li, Z. Wang, S. M. Kamali, E. Arbabi, A. Arbabi, Z. Zhao, G. Xie, Y. Cao, N. Ahmed, Y. Yan, C. Liu, A. J. Willner, S. Ashrafi, M. Tur, A. Faraon, and A. E. Willner, “Orbital angular momentum-based space division multiplexing for high-capacity underwater optical communications,” Sci. Rep. 6, 33306 (2016).
[Crossref]

Liu, G.

X. Cui, X. Yin, H. Chang, Y. Guo, Z. Zheng, Z. Sun, G. Liu, and Y. Wang, “Analysis of an adaptive orbital angular momentum shift keying decoder based on machine learning under oceanic turbulence channels,” Opt. Commun. 429, 138–143 (2018).
[Crossref]

H. M. Oubei, C. Shen, A. Kammoun, E. Zedini, K. Park, X. Sun, G. Liu, C. H. Kang, T. K. Ng, M.-S. Alouini, and B. S. Ooi, “Light based underwater wireless communications,” Jpn. J. Appl. Phys. 57, 08PA06 (2018).
[Crossref]

Liu, W.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, Massachusetts, USA (2015), pp. 1–9.

Lohani, S.

Matt, S.

S. R. Restaino, W. Hou, A. Kanaev, S. Matt, and C. Font, “Adaptive optics correction of a laser beam propagating underwater,” Proc. SPIE 9083, 90830R (2014).
[Crossref]

Melodia, T.

I. F. Akyildiz, D. Pompili, and T. Melodia, “Underwater acoustic sensor networks: research challenges,” Ad Hoc Netw. 3, 257–279 (2005).
[Crossref]

Miller, J. K.

K. S. Morgan, J. K. Miller, B. M. Cochenour, W. Li, Y. Li, R. J. Watkins, and E. G. Johnson, “Free space propagation of concentric vortices through underwater turbid environments,” J. Opt. 18, 104004 (2016).
[Crossref]

Miller, K.

Mobashery, A.

Mohd, T.

M. Khairi, T. Mohd, S. Ibrahim, M. A. M. Yunus, M. Faramarzi, and Z. Yusuf, “Artificial neural network approach for predicting the water turbidity level using optical tomography,” Arab. J. Sci. Eng. 41, 3369–3379 (2016).
[Crossref]

Moosavi, S. A.

Morgan, K.

Morgan, K. S.

K. S. Morgan, J. K. Miller, B. M. Cochenour, W. Li, Y. Li, R. J. Watkins, and E. G. Johnson, “Free space propagation of concentric vortices through underwater turbid environments,” J. Opt. 18, 104004 (2016).
[Crossref]

Mullen, L.

Neary, P. L.

P. L. Neary, A. T. Watnik, K. P. Judd, J. R. Lindle, and N. S. Flann, “Machine learning-based signal degradation models for attenuated underwater optical communication OAM beams,” Opt. Commun. 474, 126058 (2020).
[Crossref]

Ng, T. K.

C. Shen, O. Alkhazragi, X. Sun, Y. Guo, T. K. Ng, and B. S. Ooi, “Laser-based visible light communications and underwater wireless optical communications: a device perspective,” Proc. SPIE 10939, 109390E (2019).
[Crossref]

H. M. Oubei, C. Shen, A. Kammoun, E. Zedini, K. Park, X. Sun, G. Liu, C. H. Kang, T. K. Ng, M.-S. Alouini, and B. S. Ooi, “Light based underwater wireless communications,” Jpn. J. Appl. Phys. 57, 08PA06 (2018).
[Crossref]

Nowack, R. L.

R. L. Nowack, “A tale of two beams: an elementary overview of Gaussian beams and Bessel beams,” Stud. Geophys. Geod. 56, 355–372 (2012).
[Crossref]

O’Donnell, M.

Ooi, B. S.

C. Shen, O. Alkhazragi, X. Sun, Y. Guo, T. K. Ng, and B. S. Ooi, “Laser-based visible light communications and underwater wireless optical communications: a device perspective,” Proc. SPIE 10939, 109390E (2019).
[Crossref]

H. M. Oubei, C. Shen, A. Kammoun, E. Zedini, K. Park, X. Sun, G. Liu, C. H. Kang, T. K. Ng, M.-S. Alouini, and B. S. Ooi, “Light based underwater wireless communications,” Jpn. J. Appl. Phys. 57, 08PA06 (2018).
[Crossref]

A. Trichili, C. B. Issaid, B. S. Ooi, and M. S. Alouini, “A CNN based structured light communication scheme for internet of underwater things applications,” submitted to IEEE Internet Things J. (2020).
[Crossref]

Oubei, H. M.

H. M. Oubei, C. Shen, A. Kammoun, E. Zedini, K. Park, X. Sun, G. Liu, C. H. Kang, T. K. Ng, M.-S. Alouini, and B. S. Ooi, “Light based underwater wireless communications,” Jpn. J. Appl. Phys. 57, 08PA06 (2018).
[Crossref]

Padgett, M. J.

M. P. J. Lavery, D. J. Robertson, A. Sponselli, J. Courtial, N. K. Steinhoff, G. A. Tyler, A. E. Willner, and M. J. Padgett, “Efficient measurement of an optical orbital-angular-momentum spectrum comprising more than 50 states,” New J. Phys. 15, 013024 (2013).
[Crossref]

Pang, K.

A. E. Willner, Z. Zhao, Y. Ren, L. Li, G. Xie, H. Song, C. Liu, R. Zhang, C. Bao, and K. Pang, “Underwater optical communications using orbital angular momentum-based spatial division multiplexing,” Opt. Commun. 408, 21–25 (2018).
[Crossref]

Park, K.

H. M. Oubei, C. Shen, A. Kammoun, E. Zedini, K. Park, X. Sun, G. Liu, C. H. Kang, T. K. Ng, M.-S. Alouini, and B. S. Ooi, “Light based underwater wireless communications,” Jpn. J. Appl. Phys. 57, 08PA06 (2018).
[Crossref]

Parmoon, B.

Pompili, D.

I. F. Akyildiz, D. Pompili, and T. Melodia, “Underwater acoustic sensor networks: research challenges,” Ad Hoc Netw. 3, 257–279 (2005).
[Crossref]

Qaisar, S. B.

E. Felemban, F. K. Shaikh, U. M. Qureshi, A. A. Shelkh, and S. B. Qaisar, “Underwater sensor network applications: a comprehensive survey,” Int. J. Distrib. Sens. Netw. 11, 896832 (2015).
[Crossref]

Qureshi, U. M.

E. Felemban, F. K. Shaikh, U. M. Qureshi, A. A. Shelkh, and S. B. Qaisar, “Underwater sensor network applications: a comprehensive survey,” Int. J. Distrib. Sens. Netw. 11, 896832 (2015).
[Crossref]

Rabinovich, A.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, Massachusetts, USA (2015), pp. 1–9.

Reed, S.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, Massachusetts, USA (2015), pp. 1–9.

Ren, Y.

A. E. Willner, Z. Zhao, Y. Ren, L. Li, G. Xie, H. Song, C. Liu, R. Zhang, C. Bao, and K. Pang, “Underwater optical communications using orbital angular momentum-based spatial division multiplexing,” Opt. Commun. 408, 21–25 (2018).
[Crossref]

Y. Ren, L. Li, Z. Wang, S. M. Kamali, E. Arbabi, A. Arbabi, Z. Zhao, G. Xie, Y. Cao, N. Ahmed, Y. Yan, C. Liu, A. J. Willner, S. Ashrafi, M. Tur, A. Faraon, and A. E. Willner, “Orbital angular momentum-based space division multiplexing for high-capacity underwater optical communications,” Sci. Rep. 6, 33306 (2016).
[Crossref]

Restaino, S. R.

S. R. Restaino, W. Hou, A. Kanaev, S. Matt, and C. Font, “Adaptive optics correction of a laser beam propagating underwater,” Proc. SPIE 9083, 90830R (2014).
[Crossref]

Robertson, D. J.

M. P. J. Lavery, D. J. Robertson, A. Sponselli, J. Courtial, N. K. Steinhoff, G. A. Tyler, A. E. Willner, and M. J. Padgett, “Efficient measurement of an optical orbital-angular-momentum spectrum comprising more than 50 states,” New J. Phys. 15, 013024 (2013).
[Crossref]

Rumbaugh, L. K.

A. W. Jantzi, M. G. Cockrell, L. K. Rumbaugh, and W. D. Jemison, “Mixed numerical and analytical method for investigating orbital angular momentum beam scattering in turbid water,” Opt. Eng. 58, 043104 (2019).
[Crossref]

Saghafifar, H.

Sermanet, P.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, Massachusetts, USA (2015), pp. 1–9.

Shaikh, F. K.

E. Felemban, F. K. Shaikh, U. M. Qureshi, A. A. Shelkh, and S. B. Qaisar, “Underwater sensor network applications: a comprehensive survey,” Int. J. Distrib. Sens. Netw. 11, 896832 (2015).
[Crossref]

Shelkh, A. A.

E. Felemban, F. K. Shaikh, U. M. Qureshi, A. A. Shelkh, and S. B. Qaisar, “Underwater sensor network applications: a comprehensive survey,” Int. J. Distrib. Sens. Netw. 11, 896832 (2015).
[Crossref]

Shen, C.

C. Shen, O. Alkhazragi, X. Sun, Y. Guo, T. K. Ng, and B. S. Ooi, “Laser-based visible light communications and underwater wireless optical communications: a device perspective,” Proc. SPIE 10939, 109390E (2019).
[Crossref]

H. M. Oubei, C. Shen, A. Kammoun, E. Zedini, K. Park, X. Sun, G. Liu, C. H. Kang, T. K. Ng, M.-S. Alouini, and B. S. Ooi, “Light based underwater wireless communications,” Jpn. J. Appl. Phys. 57, 08PA06 (2018).
[Crossref]

Shi, L.

Song, H.

A. E. Willner, Z. Zhao, Y. Ren, L. Li, G. Xie, H. Song, C. Liu, R. Zhang, C. Bao, and K. Pang, “Underwater optical communications using orbital angular momentum-based spatial division multiplexing,” Opt. Commun. 408, 21–25 (2018).
[Crossref]

Sponselli, A.

M. P. J. Lavery, D. J. Robertson, A. Sponselli, J. Courtial, N. K. Steinhoff, G. A. Tyler, A. E. Willner, and M. J. Padgett, “Efficient measurement of an optical orbital-angular-momentum spectrum comprising more than 50 states,” New J. Phys. 15, 013024 (2013).
[Crossref]

Steinhoff, N. K.

M. P. J. Lavery, D. J. Robertson, A. Sponselli, J. Courtial, N. K. Steinhoff, G. A. Tyler, A. E. Willner, and M. J. Padgett, “Efficient measurement of an optical orbital-angular-momentum spectrum comprising more than 50 states,” New J. Phys. 15, 013024 (2013).
[Crossref]

Sun, X.

C. Shen, O. Alkhazragi, X. Sun, Y. Guo, T. K. Ng, and B. S. Ooi, “Laser-based visible light communications and underwater wireless optical communications: a device perspective,” Proc. SPIE 10939, 109390E (2019).
[Crossref]

H. M. Oubei, C. Shen, A. Kammoun, E. Zedini, K. Park, X. Sun, G. Liu, C. H. Kang, T. K. Ng, M.-S. Alouini, and B. S. Ooi, “Light based underwater wireless communications,” Jpn. J. Appl. Phys. 57, 08PA06 (2018).
[Crossref]

Sun, Z.

X. Cui, X. Yin, H. Chang, Y. Guo, Z. Zheng, Z. Sun, G. Liu, and Y. Wang, “Analysis of an adaptive orbital angular momentum shift keying decoder based on machine learning under oceanic turbulence channels,” Opt. Commun. 429, 138–143 (2018).
[Crossref]

Szegedy, C.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, Massachusetts, USA (2015), pp. 1–9.

Trichili, A.

A. Trichili, C. B. Issaid, B. S. Ooi, and M. S. Alouini, “A CNN based structured light communication scheme for internet of underwater things applications,” submitted to IEEE Internet Things J. (2020).
[Crossref]

Tur, M.

Y. Ren, L. Li, Z. Wang, S. M. Kamali, E. Arbabi, A. Arbabi, Z. Zhao, G. Xie, Y. Cao, N. Ahmed, Y. Yan, C. Liu, A. J. Willner, S. Ashrafi, M. Tur, A. Faraon, and A. E. Willner, “Orbital angular momentum-based space division multiplexing for high-capacity underwater optical communications,” Sci. Rep. 6, 33306 (2016).
[Crossref]

Tyler, G. A.

M. P. J. Lavery, D. J. Robertson, A. Sponselli, J. Courtial, N. K. Steinhoff, G. A. Tyler, A. E. Willner, and M. J. Padgett, “Efficient measurement of an optical orbital-angular-momentum spectrum comprising more than 50 states,” New J. Phys. 15, 013024 (2013).
[Crossref]

Vanhoucke, V.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, Massachusetts, USA (2015), pp. 1–9.

Wang, D.

J. Li, M. Zhang, D. Wang, S. Wu, and Y. Zhan, “Joint atmospheric turbulence detection and adaptive demodulation technique using the CNN for the OAM-FSO communication,” Opt. Express 26, 10494–10508 (2018).
[Crossref]

J. Li, X. M. Zhang, and D. Wang, “Adaptive demodulator using machine learning for orbital angular momentum shift keying,” IEEE Photon. Technol. Lett. 29, 1455–1458 (2017).
[Crossref]

Wang, W. B.

Wang, Y.

X. Cui, X. Yin, H. Chang, H. Liao, X. Chen, X. Xin, and Y. Wang, “Experimental study of machine-learning-based orbital angular momentum shift keying decoders in optical underwater channels,” Opt. Commun. 452, 116–123 (2019).
[Crossref]

X. Cui, X. Yin, H. Chang, Y. Guo, Z. Zheng, Z. Sun, G. Liu, and Y. Wang, “Analysis of an adaptive orbital angular momentum shift keying decoder based on machine learning under oceanic turbulence channels,” Opt. Commun. 429, 138–143 (2018).
[Crossref]

Wang, Z.

Y. Ren, L. Li, Z. Wang, S. M. Kamali, E. Arbabi, A. Arbabi, Z. Zhao, G. Xie, Y. Cao, N. Ahmed, Y. Yan, C. Liu, A. J. Willner, S. Ashrafi, M. Tur, A. Faraon, and A. E. Willner, “Orbital angular momentum-based space division multiplexing for high-capacity underwater optical communications,” Sci. Rep. 6, 33306 (2016).
[Crossref]

Watkins, R. J.

K. S. Morgan, J. K. Miller, B. M. Cochenour, W. Li, Y. Li, R. J. Watkins, and E. G. Johnson, “Free space propagation of concentric vortices through underwater turbid environments,” J. Opt. 18, 104004 (2016).
[Crossref]

Watnik, A.

S. Avramov-Zamurovic, A. Watnik, J. R. Lindle, and K. P. Judd, “Designing laser beams carrying OAM for a high performance underwater communication system,” J. Opt. Soc. Am. A. 37, 876–887 (2020).
[Crossref]

Watnik, A. T.

P. L. Neary, A. T. Watnik, K. P. Judd, J. R. Lindle, and N. S. Flann, “Machine learning-based signal degradation models for attenuated underwater optical communication OAM beams,” Opt. Commun. 474, 126058 (2020).
[Crossref]

T. Doster and A. T. Watnik, “Machine learning approach to OAM beam demultiplexing via convolutional neural networks,” Appl. Opt. 56, 3386–3396 (2017).
[Crossref]

T. Doster and A. T. Watnik, “Measuring multiplexed OAM modes with convolutional neural networks,” in Lasers Congress (ASSL, LSC, LAC) (Optical Society of America, 2016), paper LTh3B.2.

Willner, A. E.

A. E. Willner, Z. Zhao, Y. Ren, L. Li, G. Xie, H. Song, C. Liu, R. Zhang, C. Bao, and K. Pang, “Underwater optical communications using orbital angular momentum-based spatial division multiplexing,” Opt. Commun. 408, 21–25 (2018).
[Crossref]

Y. Ren, L. Li, Z. Wang, S. M. Kamali, E. Arbabi, A. Arbabi, Z. Zhao, G. Xie, Y. Cao, N. Ahmed, Y. Yan, C. Liu, A. J. Willner, S. Ashrafi, M. Tur, A. Faraon, and A. E. Willner, “Orbital angular momentum-based space division multiplexing for high-capacity underwater optical communications,” Sci. Rep. 6, 33306 (2016).
[Crossref]

M. P. J. Lavery, D. J. Robertson, A. Sponselli, J. Courtial, N. K. Steinhoff, G. A. Tyler, A. E. Willner, and M. J. Padgett, “Efficient measurement of an optical orbital-angular-momentum spectrum comprising more than 50 states,” New J. Phys. 15, 013024 (2013).
[Crossref]

Willner, A. J.

Y. Ren, L. Li, Z. Wang, S. M. Kamali, E. Arbabi, A. Arbabi, Z. Zhao, G. Xie, Y. Cao, N. Ahmed, Y. Yan, C. Liu, A. J. Willner, S. Ashrafi, M. Tur, A. Faraon, and A. E. Willner, “Orbital angular momentum-based space division multiplexing for high-capacity underwater optical communications,” Sci. Rep. 6, 33306 (2016).
[Crossref]

Wu, S.

Xie, G.

A. E. Willner, Z. Zhao, Y. Ren, L. Li, G. Xie, H. Song, C. Liu, R. Zhang, C. Bao, and K. Pang, “Underwater optical communications using orbital angular momentum-based spatial division multiplexing,” Opt. Commun. 408, 21–25 (2018).
[Crossref]

Y. Ren, L. Li, Z. Wang, S. M. Kamali, E. Arbabi, A. Arbabi, Z. Zhao, G. Xie, Y. Cao, N. Ahmed, Y. Yan, C. Liu, A. J. Willner, S. Ashrafi, M. Tur, A. Faraon, and A. E. Willner, “Orbital angular momentum-based space division multiplexing for high-capacity underwater optical communications,” Sci. Rep. 6, 33306 (2016).
[Crossref]

Xin, X.

X. Cui, X. Yin, H. Chang, H. Liao, X. Chen, X. Xin, and Y. Wang, “Experimental study of machine-learning-based orbital angular momentum shift keying decoders in optical underwater channels,” Opt. Commun. 452, 116–123 (2019).
[Crossref]

Yan, Y.

Y. Ren, L. Li, Z. Wang, S. M. Kamali, E. Arbabi, A. Arbabi, Z. Zhao, G. Xie, Y. Cao, N. Ahmed, Y. Yan, C. Liu, A. J. Willner, S. Ashrafi, M. Tur, A. Faraon, and A. E. Willner, “Orbital angular momentum-based space division multiplexing for high-capacity underwater optical communications,” Sci. Rep. 6, 33306 (2016).
[Crossref]

Yang, J.

L. Gong, Q. Zhao, H. Zhang, X. Hu, K. Huang, J. Yang, and Y. Li, “Optical orbital-angular-momentum-multiplexed data transmission under high scattering,” Light Sci. Appl. 8, 27 (2019).
[Crossref]

Yin, X.

X. Cui, X. Yin, H. Chang, H. Liao, X. Chen, X. Xin, and Y. Wang, “Experimental study of machine-learning-based orbital angular momentum shift keying decoders in optical underwater channels,” Opt. Commun. 452, 116–123 (2019).
[Crossref]

X. Cui, X. Yin, H. Chang, Y. Guo, Z. Zheng, Z. Sun, G. Liu, and Y. Wang, “Analysis of an adaptive orbital angular momentum shift keying decoder based on machine learning under oceanic turbulence channels,” Opt. Commun. 429, 138–143 (2018).
[Crossref]

Yunus, M. A. M.

M. Khairi, T. Mohd, S. Ibrahim, M. A. M. Yunus, M. Faramarzi, and Z. Yusuf, “Artificial neural network approach for predicting the water turbidity level using optical tomography,” Arab. J. Sci. Eng. 41, 3369–3379 (2016).
[Crossref]

Yusuf, Z.

M. Khairi, T. Mohd, S. Ibrahim, M. A. M. Yunus, M. Faramarzi, and Z. Yusuf, “Artificial neural network approach for predicting the water turbidity level using optical tomography,” Arab. J. Sci. Eng. 41, 3369–3379 (2016).
[Crossref]

Zedini, E.

H. M. Oubei, C. Shen, A. Kammoun, E. Zedini, K. Park, X. Sun, G. Liu, C. H. Kang, T. K. Ng, M.-S. Alouini, and B. S. Ooi, “Light based underwater wireless communications,” Jpn. J. Appl. Phys. 57, 08PA06 (2018).
[Crossref]

Zhan, Y.

Zhang, D.

Zhang, H.

L. Gong, Q. Zhao, H. Zhang, X. Hu, K. Huang, J. Yang, and Y. Li, “Optical orbital-angular-momentum-multiplexed data transmission under high scattering,” Light Sci. Appl. 8, 27 (2019).
[Crossref]

Zhang, M.

Zhang, R.

A. E. Willner, Z. Zhao, Y. Ren, L. Li, G. Xie, H. Song, C. Liu, R. Zhang, C. Bao, and K. Pang, “Underwater optical communications using orbital angular momentum-based spatial division multiplexing,” Opt. Commun. 408, 21–25 (2018).
[Crossref]

Zhang, X. M.

J. Li, X. M. Zhang, and D. Wang, “Adaptive demodulator using machine learning for orbital angular momentum shift keying,” IEEE Photon. Technol. Lett. 29, 1455–1458 (2017).
[Crossref]

Zhao, Q.

L. Gong, Q. Zhao, H. Zhang, X. Hu, K. Huang, J. Yang, and Y. Li, “Optical orbital-angular-momentum-multiplexed data transmission under high scattering,” Light Sci. Appl. 8, 27 (2019).
[Crossref]

Zhao, Z.

A. E. Willner, Z. Zhao, Y. Ren, L. Li, G. Xie, H. Song, C. Liu, R. Zhang, C. Bao, and K. Pang, “Underwater optical communications using orbital angular momentum-based spatial division multiplexing,” Opt. Commun. 408, 21–25 (2018).
[Crossref]

Y. Ren, L. Li, Z. Wang, S. M. Kamali, E. Arbabi, A. Arbabi, Z. Zhao, G. Xie, Y. Cao, N. Ahmed, Y. Yan, C. Liu, A. J. Willner, S. Ashrafi, M. Tur, A. Faraon, and A. E. Willner, “Orbital angular momentum-based space division multiplexing for high-capacity underwater optical communications,” Sci. Rep. 6, 33306 (2016).
[Crossref]

Zheng, Z.

X. Cui, X. Yin, H. Chang, Y. Guo, Z. Zheng, Z. Sun, G. Liu, and Y. Wang, “Analysis of an adaptive orbital angular momentum shift keying decoder based on machine learning under oceanic turbulence channels,” Opt. Commun. 429, 138–143 (2018).
[Crossref]

Ad Hoc Netw. (1)

I. F. Akyildiz, D. Pompili, and T. Melodia, “Underwater acoustic sensor networks: research challenges,” Ad Hoc Netw. 3, 257–279 (2005).
[Crossref]

Appl. Opt. (5)

Arab. J. Sci. Eng. (1)

M. Khairi, T. Mohd, S. Ibrahim, M. A. M. Yunus, M. Faramarzi, and Z. Yusuf, “Artificial neural network approach for predicting the water turbidity level using optical tomography,” Arab. J. Sci. Eng. 41, 3369–3379 (2016).
[Crossref]

IEEE Access (1)

H. Kaushal and G. Kaddoum, “Underwater optical wireless communication,” IEEE Access 4, 1518–1547 (2016).
[Crossref]

IEEE Photon. Technol. Lett. (1)

J. Li, X. M. Zhang, and D. Wang, “Adaptive demodulator using machine learning for orbital angular momentum shift keying,” IEEE Photon. Technol. Lett. 29, 1455–1458 (2017).
[Crossref]

Int. J. Distrib. Sens. Netw. (1)

E. Felemban, F. K. Shaikh, U. M. Qureshi, A. A. Shelkh, and S. B. Qaisar, “Underwater sensor network applications: a comprehensive survey,” Int. J. Distrib. Sens. Netw. 11, 896832 (2015).
[Crossref]

J. Opt. (1)

K. S. Morgan, J. K. Miller, B. M. Cochenour, W. Li, Y. Li, R. J. Watkins, and E. G. Johnson, “Free space propagation of concentric vortices through underwater turbid environments,” J. Opt. 18, 104004 (2016).
[Crossref]

J. Opt. Soc. Am. A. (1)

S. Avramov-Zamurovic, A. Watnik, J. R. Lindle, and K. P. Judd, “Designing laser beams carrying OAM for a high performance underwater communication system,” J. Opt. Soc. Am. A. 37, 876–887 (2020).
[Crossref]

Jpn. J. Appl. Phys. (1)

H. M. Oubei, C. Shen, A. Kammoun, E. Zedini, K. Park, X. Sun, G. Liu, C. H. Kang, T. K. Ng, M.-S. Alouini, and B. S. Ooi, “Light based underwater wireless communications,” Jpn. J. Appl. Phys. 57, 08PA06 (2018).
[Crossref]

Light Sci. Appl. (1)

L. Gong, Q. Zhao, H. Zhang, X. Hu, K. Huang, J. Yang, and Y. Li, “Optical orbital-angular-momentum-multiplexed data transmission under high scattering,” Light Sci. Appl. 8, 27 (2019).
[Crossref]

New J. Phys. (1)

M. P. J. Lavery, D. J. Robertson, A. Sponselli, J. Courtial, N. K. Steinhoff, G. A. Tyler, A. E. Willner, and M. J. Padgett, “Efficient measurement of an optical orbital-angular-momentum spectrum comprising more than 50 states,” New J. Phys. 15, 013024 (2013).
[Crossref]

Opt. Commun. (4)

A. E. Willner, Z. Zhao, Y. Ren, L. Li, G. Xie, H. Song, C. Liu, R. Zhang, C. Bao, and K. Pang, “Underwater optical communications using orbital angular momentum-based spatial division multiplexing,” Opt. Commun. 408, 21–25 (2018).
[Crossref]

X. Cui, X. Yin, H. Chang, Y. Guo, Z. Zheng, Z. Sun, G. Liu, and Y. Wang, “Analysis of an adaptive orbital angular momentum shift keying decoder based on machine learning under oceanic turbulence channels,” Opt. Commun. 429, 138–143 (2018).
[Crossref]

P. L. Neary, A. T. Watnik, K. P. Judd, J. R. Lindle, and N. S. Flann, “Machine learning-based signal degradation models for attenuated underwater optical communication OAM beams,” Opt. Commun. 474, 126058 (2020).
[Crossref]

X. Cui, X. Yin, H. Chang, H. Liao, X. Chen, X. Xin, and Y. Wang, “Experimental study of machine-learning-based orbital angular momentum shift keying decoders in optical underwater channels,” Opt. Commun. 452, 116–123 (2019).
[Crossref]

Opt. Eng. (1)

A. W. Jantzi, M. G. Cockrell, L. K. Rumbaugh, and W. D. Jemison, “Mixed numerical and analytical method for investigating orbital angular momentum beam scattering in turbid water,” Opt. Eng. 58, 043104 (2019).
[Crossref]

Opt. Express (1)

Opt. Lett. (1)

Proc. SPIE (2)

S. R. Restaino, W. Hou, A. Kanaev, S. Matt, and C. Font, “Adaptive optics correction of a laser beam propagating underwater,” Proc. SPIE 9083, 90830R (2014).
[Crossref]

C. Shen, O. Alkhazragi, X. Sun, Y. Guo, T. K. Ng, and B. S. Ooi, “Laser-based visible light communications and underwater wireless optical communications: a device perspective,” Proc. SPIE 10939, 109390E (2019).
[Crossref]

Sci. Rep. (1)

Y. Ren, L. Li, Z. Wang, S. M. Kamali, E. Arbabi, A. Arbabi, Z. Zhao, G. Xie, Y. Cao, N. Ahmed, Y. Yan, C. Liu, A. J. Willner, S. Ashrafi, M. Tur, A. Faraon, and A. E. Willner, “Orbital angular momentum-based space division multiplexing for high-capacity underwater optical communications,” Sci. Rep. 6, 33306 (2016).
[Crossref]

Stud. Geophys. Geod. (1)

R. L. Nowack, “A tale of two beams: an elementary overview of Gaussian beams and Bessel beams,” Stud. Geophys. Geod. 56, 355–372 (2012).
[Crossref]

Other (5)

J. S. Jaffe, “A historical perspective on underwater optical imaging,” in Proceedings of the MTS/IEEE OCEANS, Bergen, Norway (2013), pp. 1–3.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, Massachusetts, USA (2015), pp. 1–9.

G. Gbur, Singular Optics (CRC Press, 2016).

A. Trichili, C. B. Issaid, B. S. Ooi, and M. S. Alouini, “A CNN based structured light communication scheme for internet of underwater things applications,” submitted to IEEE Internet Things J. (2020).
[Crossref]

T. Doster and A. T. Watnik, “Measuring multiplexed OAM modes with convolutional neural networks,” in Lasers Congress (ASSL, LSC, LAC) (Optical Society of America, 2016), paper LTh3B.2.

Cited By

OSA participates in Crossref's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (7)

Fig. 1.
Fig. 1. Numerically simulated patterns for the 16 superpositions of the beams $\{{{\textbf{U}}_{{\textbf{0}},{\textbf{1}}}}\,{{\textbf{U}}_{{\textbf{1}},{\textbf{4}}}}\,{{\textbf{U}}_{{\textbf{0}}, - {\textbf{6}}}}\,{{\textbf{U}}_{{\textbf{1}},{\textbf{9}}}}\}$ making up the alphabet. Binary switching is shown in the top left corner for each transmitted message. Figures 35 in this paper follow the same order of the messages.
Fig. 2.
Fig. 2. Experimental setup. LS, laser source; ${{\rm{M}}_1} - {{\rm{M}}_8}$, mirrors to direct the beam; ${{\rm{L}}_1}$ and ${{\rm{L}}_2}$, lenses to shape the beam; MI, mechanical iris; SLM, spatial light modulator; ND, neutral density filter to attenuate beam intensity in front of the camera lens; C, camera; W, water tank with scatterers; ${{\rm{F}}_1} - {{\rm{F}}_6}$, fans used for mechanical agitation.
Fig. 3.
Fig. 3. Exponential dependence of SNR on the measured attention. Note that the Beer–Lambert law gives the exponential dependence of the attenuation and thus the linear fit on the log scale in the plot.
Fig. 4.
Fig. 4. Beam set $\{{{\textbf{U}}_{{\textbf{0}},{\textbf{1}}}}\,{{\textbf{U}}_{{\textbf{1}},{\textbf{4}}}}\,{{\textbf{U}}_{{\textbf{0}}, - {\textbf{6}}}}\,{{\textbf{U}}_{{\textbf{1}},{\textbf{9}}}}\}$ propagated in quiescent water with stationary scatterers. Attenuation length is 4 (Scenario A). Each message is represented by a single image, as it is used in the classifying algorithm. Note that the graininess observed is an artefact of the camera when imaging short laser pulses.
Fig. 5.
Fig. 5. Beam set $\{{{\textbf{U}}_{{\textbf{0}},{\textbf{1}}}}\,{{\textbf{U}}_{{\textbf{1}},{\textbf{4}}}}\,{{\textbf{U}}_{{\textbf{0}}, - {\textbf{6}}}}\,{{\textbf{U}}_{{\textbf{1}},{\textbf{9}}}}\}$ propagated through water with moving scatterers (turbid water). Attenuation length is 12 (Scenario F). Each message is represented by a single image, as it is used in the classifying algorithm.
Fig. 6.
Fig. 6. Confusion matrix for a specific example: training on Scenario A and classifying the images from Scenario E. The column labels are the correct answer; the row labels are the classifier’s output.
Fig. 7.
Fig. 7. Test image of beam 2 taken in Scenario E, which was misclassified as beam 14 (left). Compare this with the training data: a training image beam 2 taken in Scenario A (center) versus a training image of beam 14 taken in Scenario A (right).

Tables (4)

Tables Icon

Table 1. Experimental Conditionsa

Tables Icon

Table 2. Neural Network Architecture

Tables Icon

Table 3. Summary of the Classification Results

Tables Icon

Table 4. D i s t a n c e b e t w e e n t h e I m a g e s = ( R s q u a r e v a l u e f r o m t h e c o r r e l a t i o n ) 2 + ( i n t e n s i t y s i m i l a r i t y ) 2  a

Equations (3)

Equations on this page are rendered with MathJax. Learn more.

U n , m ( ρ , θ , z ) = I 0 w 0 w ( z ) e ρ 2 w ( z ) 2 e i 2 π λ ρ 2 2 ( z 2 + R 2 ) e i 2 π λ z × 2 n ! π w 0 2 ( n + | m | ) ! ( 2 ρ w ( z ) ) | m | × L n | m | ( 2 ρ 2 w ( z ) 2 ) e i ( 2 n + | m | + 1 ) arctan z R + i m θ ,
A L = l n ( P o / P ) ,
S N R = m e a n ( S B ) s t d ( B ) .