Abstract

In this paper, a novel turbo-coded 16-ary orbital angular momentum - shift keying-free space optical (OAM-SK-FSO) communication system combining a convolutional neural network (CNN) based adaptive demodulator under strong atmospheric turbulence is proposed for the first time. The feasibility of the scheme is verified by transmitting a 256-grayscale two-dimensional digital image. The bit error ratio (BER) performance of the system is investigated and the effect of different factors such as turbulence strength, propagation distance, code rate, length of random interleaver and length of bit interleaver is also taken into account. An advanced encoder/decoder structure and mapping scheme are applied to diminish the influence of CNN misclassification and reduce the BER effectively. With the optimal encoder/decoder structure and CNN model settings, the BER varies from 0 to 4.89×104 when the propagation distance increases from 200m to 1000m for a given turbulence strength Cn2 equals 5×1014m2/3. For a determined propagation distance equals 400m, the BER ranges from 0 to 4.01×104 when Cn2increases from 1×1015m2/3 to 4×1013m2/3. Our numerical simulations demonstrate that the proposed system can provide better BER performance under strong atmospheric turbulence and conditions when the classification ability of CNN is limited.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2018 (5)

Q. Tian, L. Zhu, Y. Wang, Q. Zhang, B. Liu, and X. Xin, “The Propagation Properties of a Longitudinal Orbital Angular Momentum Multiplexing System in Atmospheric Turbulence,” IEEE Photonics J. 10(1), 1–16 (2018).
[Crossref]

X. Cui, X. Yin, H. Chang, Z. Sun, Y. Wang, Q. Tian, G. Wu, and X. Xin, “Analysis of the orbital angular momentum spectrum for Laguerre–Gaussian beams under moderate-to-strong marine-atmospheric turbulent channels,” Opt. Commun. 426, 471–476 (2018).
[Crossref]

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[Crossref] [PubMed]

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[Crossref] [PubMed]

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(8), 10494–10508 (2018).
[Crossref] [PubMed]

2017 (5)

T. Doster and A. T. Watnik, “Machine learning approach to OAM beam demultiplexing via convolutional neural networks,” Appl. Opt. 56(12), 3386–3396 (2017).
[Crossref] [PubMed]

J. Li, M. Zhang, and D. Wang, “Adaptive Demodulator Using Machine Learning for Orbital Angular Momentum Shift Keying,” IEEE Photonics Technol. Lett. 29(17), 1455–1458 (2017).
[Crossref]

H. Chang, X. Yin, X. Cui, Z. Zhang, J. Ma, G. Wu, L. Zhang, and X. Xin, “Adaptive optics compensation of orbital angular momentum beams with a modified Gerchberg–Saxton-based phase retrieval algorithm,” Opt. Commun. 405, 271–275 (2017).
[Crossref]

C. Kai, P. Huang, F. Shen, H. Zhou, and Z. Guo, “Orbital Angular Momentum Shift Keying Based Optical Communication System,” IEEE Photonics J. 9(2), 1–10 (2017).
[Crossref]

W. Wang, P. Wang, T. Cao, H. Tian, Y. Zhang, and L. Guo, “Performance Investigation of Underwater Wireless Optical Communication System Using M -ary OAMSK Modulation Over Oceanic Turbulence,” IEEE Photonics J. 9(5), 1–15 (2017).
[Crossref]

2016 (3)

M. Krenn, J. Handsteiner, M. Fink, R. Fickler, R. Ursin, M. Malik, and A. Zeilinger, “Twisted light transmission over 143 km,” Proc. Natl. Acad. Sci. U.S.A. 113(48), 13648–13653 (2016).
[Crossref] [PubMed]

S. Zhao, L. Wang, L. Zou, L. Gong, W. Cheng, B. Zheng, and H. Chen, “Both channel coding and wavefront correction on the turbulence mitigation of optical communications using orbital angular momentum multiplexing,” Opt. Commun. 376, 92–98 (2016).
[Crossref]

D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis, “Mastering the game of Go with deep neural networks and tree search,” Nature 529(7587), 484–489 (2016).
[Crossref] [PubMed]

2015 (2)

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015).
[Crossref] [PubMed]

T. Lei, M. Zhang, Y. Li, P. Jia, G. N. Liu, X. Xu, Z. Li, C. Min, J. Lin, C. Yu, H. Niu, and X. Yuan, “Massive individual orbital angular momentum channels for multiplexing enabled by Dammann gratings,” Light Sci. Appl. 4(3), e257 (2015).
[Crossref]

2014 (2)

2013 (2)

2012 (3)

J. Wang, J.-Y. Yang, I. M. Fazal, N. Ahmed, Y. Yan, H. Huang, Y. Ren, Y. Yue, S. Dolinar, M. Tur, and A. E. Willner, “Terabit free-space data transmission employing orbital angular momentum multiplexing,” Nat. Photonics 6(7), 488–496 (2012).
[Crossref]

A. Krizhevsky, I. Sutskever, and G. Hinton, “ImageNet classification with deep convolutional neural networks,” Adv. Neural Inf. Process. 25, 1106–1114 (2012).

G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. Mohamed, N. Jaitly, A. Senior, and P. Vincent Vanhoucke, “Nguyen, T. N. Sainath, and B. Kingsbury, “Deep neural networks for acoustic modeling in speech recognition-The shared views of four research groups,” IEEE Signal Process. Mag. 29(6), 82–97 (2012).
[Crossref]

2011 (1)

2010 (1)

2009 (1)

2004 (1)

1998 (1)

R. M. Pyndiah, “Near-optimum decoding of product codes: block turbo codes,” IEEE Trans. Commun. 46(8), 1003–1010 (1998).
[Crossref]

1996 (1)

C. Berrou and A. Glavieux, “Near optimum error correcting coding and decoding: turbo-codes,” IEEE Trans. Commun. 44(10), 1261–1271 (1996).
[Crossref]

1988 (1)

Ahmed, N.

Alwageedy, H.

S. Peng, H. Jiangy, H. Wangy, H. Alwageedy, and Y.-D. Yaoy, “Modulation Classification Using Convolutional Neural Network Based Deep Learning Model,” in Wireless and Optical Communication Conference (IEEE, 2017), pp. 1–5.

Antonoglou, I.

D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis, “Mastering the game of Go with deep neural networks and tree search,” Nature 529(7587), 484–489 (2016).
[Crossref] [PubMed]

Arabaci, M.

Bao, C.

Barnett, S.

Bengio, Y.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015).
[Crossref] [PubMed]

Berrou, C.

C. Berrou and A. Glavieux, “Near optimum error correcting coding and decoding: turbo-codes,” IEEE Trans. Commun. 44(10), 1261–1271 (1996).
[Crossref]

Boyd, R. W.

Cao, T.

W. Wang, P. Wang, T. Cao, H. Tian, Y. Zhang, and L. Guo, “Performance Investigation of Underwater Wireless Optical Communication System Using M -ary OAMSK Modulation Over Oceanic Turbulence,” IEEE Photonics J. 9(5), 1–15 (2017).
[Crossref]

Cattell, L.

Chang, H.

X. Cui, X. Yin, H. Chang, Z. Sun, Y. Wang, Q. Tian, G. Wu, and X. Xin, “Analysis of the orbital angular momentum spectrum for Laguerre–Gaussian beams under moderate-to-strong marine-atmospheric turbulent channels,” Opt. Commun. 426, 471–476 (2018).
[Crossref]

H. Chang, X. Yin, X. Cui, Z. Zhang, J. Ma, G. Wu, L. Zhang, and X. Xin, “Adaptive optics compensation of orbital angular momentum beams with a modified Gerchberg–Saxton-based phase retrieval algorithm,” Opt. Commun. 405, 271–275 (2017).
[Crossref]

Chen, H.

S. Zhao, L. Wang, L. Zou, L. Gong, W. Cheng, B. Zheng, and H. Chen, “Both channel coding and wavefront correction on the turbulence mitigation of optical communications using orbital angular momentum multiplexing,” Opt. Commun. 376, 92–98 (2016).
[Crossref]

Cheng, W.

S. Zhao, L. Wang, L. Zou, L. Gong, W. Cheng, B. Zheng, and H. Chen, “Both channel coding and wavefront correction on the turbulence mitigation of optical communications using orbital angular momentum multiplexing,” Opt. Commun. 376, 92–98 (2016).
[Crossref]

B. Zheng, B. Wang, L. Gong, S. Zhao, W. Cheng, X. Dong, and Y. Sheng, “Improving the Atmosphere Turbulence Tolerance in Holographic Ghost Imaging System by Channel Coding,” J. Lightwave Technol. 31(17), 2823–2828 (2013).
[Crossref]

Courtial, J.

Cui, X.

X. Cui, X. Yin, H. Chang, Z. Sun, Y. Wang, Q. Tian, G. Wu, and X. Xin, “Analysis of the orbital angular momentum spectrum for Laguerre–Gaussian beams under moderate-to-strong marine-atmospheric turbulent channels,” Opt. Commun. 426, 471–476 (2018).
[Crossref]

H. Chang, X. Yin, X. Cui, Z. Zhang, J. Ma, G. Wu, L. Zhang, and X. Xin, “Adaptive optics compensation of orbital angular momentum beams with a modified Gerchberg–Saxton-based phase retrieval algorithm,” Opt. Commun. 405, 271–275 (2017).
[Crossref]

Dahl, G. E.

G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. Mohamed, N. Jaitly, A. Senior, and P. Vincent Vanhoucke, “Nguyen, T. N. Sainath, and B. Kingsbury, “Deep neural networks for acoustic modeling in speech recognition-The shared views of four research groups,” IEEE Signal Process. Mag. 29(6), 82–97 (2012).
[Crossref]

Deng, L.

G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. Mohamed, N. Jaitly, A. Senior, and P. Vincent Vanhoucke, “Nguyen, T. N. Sainath, and B. Kingsbury, “Deep neural networks for acoustic modeling in speech recognition-The shared views of four research groups,” IEEE Signal Process. Mag. 29(6), 82–97 (2012).
[Crossref]

Dieleman, S.

D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis, “Mastering the game of Go with deep neural networks and tree search,” Nature 529(7587), 484–489 (2016).
[Crossref] [PubMed]

Djordjevic, I. B.

Dolinar, S.

Y. Yan, Y. Yue, H. Huang, Y. Ren, N. Ahmed, M. Tur, S. Dolinar, and A. Willner, “Multicasting in a spatial division multiplexing system based on optical orbital angular momentum,” Opt. Lett. 38(19), 3930–3933 (2013).
[Crossref] [PubMed]

J. Wang, J.-Y. Yang, I. M. Fazal, N. Ahmed, Y. Yan, H. Huang, Y. Ren, Y. Yue, S. Dolinar, M. Tur, and A. E. Willner, “Terabit free-space data transmission employing orbital angular momentum multiplexing,” Nat. Photonics 6(7), 488–496 (2012).
[Crossref]

Dong, X.

Doster, T.

Fazal, I. M.

J. Wang, J.-Y. Yang, I. M. Fazal, N. Ahmed, Y. Yan, H. Huang, Y. Ren, Y. Yue, S. Dolinar, M. Tur, and A. E. Willner, “Terabit free-space data transmission employing orbital angular momentum multiplexing,” Nat. Photonics 6(7), 488–496 (2012).
[Crossref]

Fickler, R.

M. Krenn, J. Handsteiner, M. Fink, R. Fickler, R. Ursin, M. Malik, and A. Zeilinger, “Twisted light transmission over 143 km,” Proc. Natl. Acad. Sci. U.S.A. 113(48), 13648–13653 (2016).
[Crossref] [PubMed]

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

Fink, M.

M. Krenn, J. Handsteiner, M. Fink, R. Fickler, R. Ursin, M. Malik, and A. Zeilinger, “Twisted light transmission over 143 km,” Proc. Natl. Acad. Sci. U.S.A. 113(48), 13648–13653 (2016).
[Crossref] [PubMed]

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

Flatté, S. M.

Franke-Arnold, S.

Gibson, G.

Glasser, R. T.

Glavieux, A.

C. Berrou and A. Glavieux, “Near optimum error correcting coding and decoding: turbo-codes,” IEEE Trans. Commun. 44(10), 1261–1271 (1996).
[Crossref]

Gong, L.

S. Zhao, L. Wang, L. Zou, L. Gong, W. Cheng, B. Zheng, and H. Chen, “Both channel coding and wavefront correction on the turbulence mitigation of optical communications using orbital angular momentum multiplexing,” Opt. Commun. 376, 92–98 (2016).
[Crossref]

B. Zheng, B. Wang, L. Gong, S. Zhao, W. Cheng, X. Dong, and Y. Sheng, “Improving the Atmosphere Turbulence Tolerance in Holographic Ghost Imaging System by Channel Coding,” J. Lightwave Technol. 31(17), 2823–2828 (2013).
[Crossref]

Graepel, T.

D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis, “Mastering the game of Go with deep neural networks and tree search,” Nature 529(7587), 484–489 (2016).
[Crossref] [PubMed]

Grewe, D.

D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis, “Mastering the game of Go with deep neural networks and tree search,” Nature 529(7587), 484–489 (2016).
[Crossref] [PubMed]

Guez, A.

D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis, “Mastering the game of Go with deep neural networks and tree search,” Nature 529(7587), 484–489 (2016).
[Crossref] [PubMed]

Guo, L.

W. Wang, P. Wang, T. Cao, H. Tian, Y. Zhang, and L. Guo, “Performance Investigation of Underwater Wireless Optical Communication System Using M -ary OAMSK Modulation Over Oceanic Turbulence,” IEEE Photonics J. 9(5), 1–15 (2017).
[Crossref]

Guo, Z.

C. Kai, P. Huang, F. Shen, H. Zhou, and Z. Guo, “Orbital Angular Momentum Shift Keying Based Optical Communication System,” IEEE Photonics J. 9(2), 1–10 (2017).
[Crossref]

Handsteiner, J.

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D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis, “Mastering the game of Go with deep neural networks and tree search,” Nature 529(7587), 484–489 (2016).
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Leach, M.

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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(8), 10494–10508 (2018).
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Li, Y.

T. Lei, M. Zhang, Y. Li, P. Jia, G. N. Liu, X. Xu, Z. Li, C. Min, J. Lin, C. Yu, H. Niu, and X. Yuan, “Massive individual orbital angular momentum channels for multiplexing enabled by Dammann gratings,” Light Sci. Appl. 4(3), e257 (2015).
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T. Lei, M. Zhang, Y. Li, P. Jia, G. N. Liu, X. Xu, Z. Li, C. Min, J. Lin, C. Yu, H. Niu, and X. Yuan, “Massive individual orbital angular momentum channels for multiplexing enabled by Dammann gratings,” Light Sci. Appl. 4(3), e257 (2015).
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T. Lei, M. Zhang, Y. Li, P. Jia, G. N. Liu, X. Xu, Z. Li, C. Min, J. Lin, C. Yu, H. Niu, and X. Yuan, “Massive individual orbital angular momentum channels for multiplexing enabled by Dammann gratings,” Light Sci. Appl. 4(3), e257 (2015).
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M. Krenn, J. Handsteiner, M. Fink, R. Fickler, R. Ursin, M. Malik, and A. Zeilinger, “Twisted light transmission over 143 km,” Proc. Natl. Acad. Sci. U.S.A. 113(48), 13648–13653 (2016).
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Min, C.

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Figures (16)

Fig. 1
Fig. 1 Schematic diagram of 16-ary OAM-SK-FSO communication system based on Turbo code combining deep-learning.
Fig. 2
Fig. 2 The specific structure of Turbo encoder: a standard Turbo encoder, N × n bit interleavers and a mapper.
Fig. 3
Fig. 3 The specific structure of Turbo decoder: a standard Turbo decoder, N × n bit deinterleavers and a demapper.
Fig. 4
Fig. 4 CNN architecture.
Fig. 5
Fig. 5 16-ary format images with OAM states of {1, −2, 3, −5}.
Fig. 6
Fig. 6 The random crosstalk matrix of transmitted 16-ary format with superposed OAM states.
Fig. 7
Fig. 7 The advanced mapping diagram of superposed OAM states.
Fig. 8
Fig. 8 The detailed transmission process of 16-ary OAM-SK-FSO communication system.
Fig. 9
Fig. 9 (a) original image; (b1) received image using traditional mapping scheme under C n 2 = 1× 10 13 m 2/3 ; (b2) received image using traditional mapping scheme under C n 2 = 5× 10 14 m 2/3 ; (c1) received image using designed mapping scheme under C n 2 = 1× 10 13 m 2/3 ; (c2) received image using designed mapping scheme under C n 2 = 5× 10 14 m 2/3
Fig. 10
Fig. 10 (a) original image; (b1) received image using traditional mapping scheme when distance is 400m; (b2) received image using traditional mapping scheme when distance is 800m; (c1) received image using designed mapping scheme when distance is 400m; (c2) received image using designed mapping scheme when distance is 800m.
Fig. 11
Fig. 11 (a) original image; (b) received image with CNN-based method; (c) received image with Turbo-coded combining CNN
Fig. 12
Fig. 12 BER performance of the OAM-SK-FSO communication system under different turbulence conditions with and without Turbo coding.
Fig. 13
Fig. 13 BER performance of the OAM-SK-FSO communication system under different distance conditions with and without Turbo coding.
Fig. 14
Fig. 14 BER performance of the OAM-SK-FSO communication system under different turbulence conditions with code rate (1/2,1/3).
Fig. 15
Fig. 15 BER performance of the OAM-SK-FSO communication system under different turbulence conditions with random interleaver length (1024,2048,4096,8192).
Fig. 16
Fig. 16 BER performance of the OAM-SK-FSO communication system under different turbulence conditions with bit interleaver length (0,1024,2048,4096).

Equations (2)

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Class_per=  n=1 N 1( p ̂ n | p n ) N ×100
Ф n mvK ( κ )=0.033 C n 2 ( κ 2 +  κ 0 2 ) 11/6 exp( κ 2 / κ m 2 )

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