Abstract

Accurate optical monitors are critical for automating operations of fiber-optic networks. Deep neural network (DNN) based optical monitors have been investigated as accurate optical monitors to leverage a large amount of data obtained from fiber-optic networks. Although DNN-based optical monitors have been trained and tested to ensure the given accuracy criteria, this does not ensure sufficient accuracy under unexpected conditions, that is, out of test conditions, e.g., a newly developed modulation format that is not included in the test dataset. Thus, it is necessary to prepare a monitor to assess the current accuracy of a DNN-based optical monitor's output for robust automation of networks. We present a DNN-based optical monitor that simultaneously outputs an optical signal-to-noise ratio and its uncertainty information using a dropout method at the inference phase. This monitor was evaluated in cases in which the DNNs were trained with either a limited number of records or partially missing records in a training dataset. The proposed monitor successfully informed that own output has large uncertainties due to a limited amount of training data or a missing part in training dataset. Additionally, to improve an accuracy of estimated uncertainty, the number of partial neural networks by dropout at the inference phase was optimized. This is a valuable step toward designing robust “self-driving” optical networks.

© 2019 OAPA

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  24. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.
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  29. S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in Proc. 32nd Int. Conf. Mach. Learn., 2015, pp. 448–456.
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2019 (1)

2018 (4)

F. J. Vaquero Caballeroet al., “Machine learning based linear and nonlinear noise estimation,” J. Opt. Commun. Netw., vol. 10, pp. D42–D51, 2018.

T. Tanimura, T. Hoshida, T. Kato, S. Watanabe, and H. Morikawa, “Simple learning method to guarantee operational range of optical monitors,” J. Opt. Commun. Netw., vol. 10, pp. D63–D71, 2018.

Z. Allen-Zhu, Y. Li, and Z. Song, “A convergence theory for deep learning via over-parameterization,” 2018, arXiv:1811.03962.

A. S. Kashiet al., “Nonlinear signal-to-noise ratio estimation in coherent optical fiber transmission systems using artificial neural networks,” J. Lightw. Technol., vol. 36, no. 23, pp. 5424–5431,  2018.

2017 (4)

D. Wanget al., “Modulation format recognition and OSNR estimation using CNN-based deep learning,” IEEE Photon. Technol. Lett., vol. 29, no. 19, pp. 1667–1670,  2017.

S. Odaet al., “A learning living network with open ROADMs,” J. Lightw. Technol., vol. 35, no. 8, pp. 1350–1356,  2017.

T. Tanimura, T. Hoshida, T. Kato, S. Watanabe, M. Suzuki, and H. Morikawa, “Throughput and latency programmable optical transceiver by using DSP and FEC control,” Opt. Express, vol. 25, no. 10, pp. 10815–10827,  2017.

F. N. Khanet al., “Joint OSNR monitoring and modulation format identification in digital coherent receivers using deep neural networks,” Opt. Express, vol. 25, no. 15, pp. 17767–17776, 2017.

2016 (1)

D. Zibar, M. Piels, R. Jones, and C. G. Schaeffer, “Machine learning techniques in optical communication,” J. Lightw. Technol., vol. 34, no. 6, pp. 1442–1452,  2016.

2014 (1)

N. Srivastavaet al., “Dropout: A simple way to prevent neural networks from over fitting,” J. Mach. Learn. Res., vol. 15, pp. 1929–1958, 2014.

2012 (1)

F. N. Khan, T. S. R. Shen, Y. Zhou, A. P. T. Lau, and C. Lu, “Optical performance monitoring using artificial neural networks trained with empirical moments of asynchronously sampled signal amplitudes,” IEEE Photon. Technol. Lett., vol. 24, no. 12, pp. 982–984,  2012.

2010 (2)

T. S. R. Shen, K. Meng, A. P. T. Lau, and Z. Y. Dong, “Optical performance monitoring using artificial neural network trained with asynchronous amplitude histograms,” IEEE Photon. Technol. Lett., vol. 22, no. 22, pp. 1665–1667,  2010.

J. A. Jargon, X. Wu, H. Y. Choi, Y. C. Chung, and A. E. Willner, “Optical performance monitoring of QPSK data channels by use of neural networks trained with parameters derived from asynchronous constellation diagrams,” Opt. Express, vol. 18, no. 5, pp. 4931–4938, 2010.

2009 (2)

X. Wu, J. A. Jargon, R. A. Skoog, L. Paraschis, and A. E. Willner, “Applications of artificial neural networks in optical performance monitoring,” J. Lightw Technol., vol. 27, no. 16, pp. 3580–3589,  2009.

T. B. Anderson, A. Kowalczyk, K. Clarke, S. D. Dods, D. Hewitt, and J. C. Li, “Multi impairment monitoring for optical networks,” J. Lightw. Technol., vol. 27, no. 16, pp. 3729–3736,  2009.

Abadi, M.

M. Abadiet al., “Tensorflow: A system for large-scale machine learning,” in Proc. 12th USENIX Symp. Oper. Syst. Des. Implementation, 2016, pp. 265–283.

Allen-Zhu, Z.

Z. Allen-Zhu, Y. Li, and Z. Song, “A convergence theory for deep learning via over-parameterization,” 2018, arXiv:1811.03962.

Anderson, T. B.

T. B. Anderson, A. Kowalczyk, K. Clarke, S. D. Dods, D. Hewitt, and J. C. Li, “Multi impairment monitoring for optical networks,” J. Lightw. Technol., vol. 27, no. 16, pp. 3729–3736,  2009.

Ba, J.

J. Ba and D. Kingma, “Adam: A method for stochastic optimization,” in Proc. 3rd Int. Conf. Learn. Represent., 2015.

Bahri, Y.

J. Lee, J. Sohl-Dickstein, J. Pennington, R. Novak, S. Schoenholz, and Y. Bahri, “Deep neural networks as Gaussian processes,” in Proc. Int. Conf. Learn. Represent., 2018.

Bengio, Y.

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.

Bengui, Y.

X. Glorot, A. Bordes, and Y. Bengui, “Deep sparse rectifier neural networks,” in Proc. 14th Int. Conf. Artif. Intell. Statist., 2011, pp. 315–323.

Bishop, C. M.

C. M. Bishop, Pattern Recognition and Machine Learning. New York, NY, USA: Springer, 2006.

Bordes, A.

X. Glorot, A. Bordes, and Y. Bengui, “Deep sparse rectifier neural networks,” in Proc. 14th Int. Conf. Artif. Intell. Statist., 2011, pp. 315–323.

Choi, H. Y.

Chung, Y. C.

Clarke, K.

T. B. Anderson, A. Kowalczyk, K. Clarke, S. D. Dods, D. Hewitt, and J. C. Li, “Multi impairment monitoring for optical networks,” J. Lightw. Technol., vol. 27, no. 16, pp. 3729–3736,  2009.

Courville, A.

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.

Dods, S. D.

T. B. Anderson, A. Kowalczyk, K. Clarke, S. D. Dods, D. Hewitt, and J. C. Li, “Multi impairment monitoring for optical networks,” J. Lightw. Technol., vol. 27, no. 16, pp. 3729–3736,  2009.

Dong, Z. Y.

T. S. R. Shen, K. Meng, A. P. T. Lau, and Z. Y. Dong, “Optical performance monitoring using artificial neural network trained with asynchronous amplitude histograms,” IEEE Photon. Technol. Lett., vol. 22, no. 22, pp. 1665–1667,  2010.

Gal, Y.

Y. Gal and Z. Ghahramani, “Dropout as a Bayesian approximation: Representing model uncertainty in deep learning,” in Proc. 33rd Int. Conf. Mach. Learn., 2016, pp. 1651–1660.

Ghahramani, Z.

Y. Gal and Z. Ghahramani, “Dropout as a Bayesian approximation: Representing model uncertainty in deep learning,” in Proc. 33rd Int. Conf. Mach. Learn., 2016, pp. 1651–1660.

Glorot, X.

X. Glorot, A. Bordes, and Y. Bengui, “Deep sparse rectifier neural networks,” in Proc. 14th Int. Conf. Artif. Intell. Statist., 2011, pp. 315–323.

Goodfellow, I.

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.

Hewitt, D.

T. B. Anderson, A. Kowalczyk, K. Clarke, S. D. Dods, D. Hewitt, and J. C. Li, “Multi impairment monitoring for optical networks,” J. Lightw. Technol., vol. 27, no. 16, pp. 3729–3736,  2009.

Hoshida, T.

T. Tanimura, T. Hoshida, T. Kato, S. Watanabe, and H. Morikawa, “Convolutional neural network-based optical performance monitoring for optical transport networks,” J. Opt. Commun. Netw., vol. 11, pp. A52–A59, 2019.

T. Tanimura, T. Hoshida, T. Kato, S. Watanabe, and H. Morikawa, “Simple learning method to guarantee operational range of optical monitors,” J. Opt. Commun. Netw., vol. 10, pp. D63–D71, 2018.

T. Tanimura, T. Hoshida, T. Kato, S. Watanabe, M. Suzuki, and H. Morikawa, “Throughput and latency programmable optical transceiver by using DSP and FEC control,” Opt. Express, vol. 25, no. 10, pp. 10815–10827,  2017.

T. Tanimura, T. Hoshida, T. Kato, S. Watanabe, and H. Morikawa, “Data-analytics-based optical performance monitoring technique for optical transport networks (invited),” in Proc. Opt. Fiber Commun. Conf. Exhib., 2018, Paper Tu3E.3.

T. Tanimura, T. Hoshida, J. C. Rasmussen, M. Suzuki, and H. Morikawa, “OSNR monitoring by deep neural networks trained with asynchronously sampled data,” in Proc. Optoelectron. Commun. Conf./Int. Conf. Photon. Switching, 2016, Paper TuB3-5.

T. Tanimura, T. Kato, S. Watanabe, and T. Hoshida, “Deep neural network based optical monitor providing self-confidence as auxiliary output,” in Proc. Eur. Conf. Opt. Commun., 2018, Paper We1D.5.

Ioffe, S.

S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in Proc. 32nd Int. Conf. Mach. Learn., 2015, pp. 448–456.

Jargon, J. A.

J. A. Jargon, X. Wu, H. Y. Choi, Y. C. Chung, and A. E. Willner, “Optical performance monitoring of QPSK data channels by use of neural networks trained with parameters derived from asynchronous constellation diagrams,” Opt. Express, vol. 18, no. 5, pp. 4931–4938, 2010.

X. Wu, J. A. Jargon, R. A. Skoog, L. Paraschis, and A. E. Willner, “Applications of artificial neural networks in optical performance monitoring,” J. Lightw Technol., vol. 27, no. 16, pp. 3580–3589,  2009.

Jones, R.

D. Zibar, M. Piels, R. Jones, and C. G. Schaeffer, “Machine learning techniques in optical communication,” J. Lightw. Technol., vol. 34, no. 6, pp. 1442–1452,  2016.

J. Wass, J. Thrane, M. Piels, R. Jones, and D. Zibar, “Gaussian process regression for WDM system performance prediction,” in Proc. Opt. Fiber Commun. Conf. Exhib., 2017, Paper Tu3D.7.

Kashi, A. S.

A. S. Kashiet al., “Nonlinear signal-to-noise ratio estimation in coherent optical fiber transmission systems using artificial neural networks,” J. Lightw. Technol., vol. 36, no. 23, pp. 5424–5431,  2018.

Kato, T.

T. Tanimura, T. Hoshida, T. Kato, S. Watanabe, and H. Morikawa, “Convolutional neural network-based optical performance monitoring for optical transport networks,” J. Opt. Commun. Netw., vol. 11, pp. A52–A59, 2019.

T. Tanimura, T. Hoshida, T. Kato, S. Watanabe, and H. Morikawa, “Simple learning method to guarantee operational range of optical monitors,” J. Opt. Commun. Netw., vol. 10, pp. D63–D71, 2018.

T. Tanimura, T. Hoshida, T. Kato, S. Watanabe, M. Suzuki, and H. Morikawa, “Throughput and latency programmable optical transceiver by using DSP and FEC control,” Opt. Express, vol. 25, no. 10, pp. 10815–10827,  2017.

T. Tanimura, T. Hoshida, T. Kato, S. Watanabe, and H. Morikawa, “Data-analytics-based optical performance monitoring technique for optical transport networks (invited),” in Proc. Opt. Fiber Commun. Conf. Exhib., 2018, Paper Tu3E.3.

T. Tanimura, T. Kato, S. Watanabe, and T. Hoshida, “Deep neural network based optical monitor providing self-confidence as auxiliary output,” in Proc. Eur. Conf. Opt. Commun., 2018, Paper We1D.5.

Khan, F. N.

F. N. Khanet al., “Joint OSNR monitoring and modulation format identification in digital coherent receivers using deep neural networks,” Opt. Express, vol. 25, no. 15, pp. 17767–17776, 2017.

F. N. Khan, T. S. R. Shen, Y. Zhou, A. P. T. Lau, and C. Lu, “Optical performance monitoring using artificial neural networks trained with empirical moments of asynchronously sampled signal amplitudes,” IEEE Photon. Technol. Lett., vol. 24, no. 12, pp. 982–984,  2012.

Kingma, D.

J. Ba and D. Kingma, “Adam: A method for stochastic optimization,” in Proc. 3rd Int. Conf. Learn. Represent., 2015.

Kowalczyk, A.

T. B. Anderson, A. Kowalczyk, K. Clarke, S. D. Dods, D. Hewitt, and J. C. Li, “Multi impairment monitoring for optical networks,” J. Lightw. Technol., vol. 27, no. 16, pp. 3729–3736,  2009.

Lau, A. P. T.

F. N. Khan, T. S. R. Shen, Y. Zhou, A. P. T. Lau, and C. Lu, “Optical performance monitoring using artificial neural networks trained with empirical moments of asynchronously sampled signal amplitudes,” IEEE Photon. Technol. Lett., vol. 24, no. 12, pp. 982–984,  2012.

T. S. R. Shen, K. Meng, A. P. T. Lau, and Z. Y. Dong, “Optical performance monitoring using artificial neural network trained with asynchronous amplitude histograms,” IEEE Photon. Technol. Lett., vol. 22, no. 22, pp. 1665–1667,  2010.

Lee, J.

J. Lee, J. Sohl-Dickstein, J. Pennington, R. Novak, S. Schoenholz, and Y. Bahri, “Deep neural networks as Gaussian processes,” in Proc. Int. Conf. Learn. Represent., 2018.

Li, J. C.

T. B. Anderson, A. Kowalczyk, K. Clarke, S. D. Dods, D. Hewitt, and J. C. Li, “Multi impairment monitoring for optical networks,” J. Lightw. Technol., vol. 27, no. 16, pp. 3729–3736,  2009.

Li, Y.

Z. Allen-Zhu, Y. Li, and Z. Song, “A convergence theory for deep learning via over-parameterization,” 2018, arXiv:1811.03962.

Lu, C.

F. N. Khan, T. S. R. Shen, Y. Zhou, A. P. T. Lau, and C. Lu, “Optical performance monitoring using artificial neural networks trained with empirical moments of asynchronously sampled signal amplitudes,” IEEE Photon. Technol. Lett., vol. 24, no. 12, pp. 982–984,  2012.

Meng, F.

F. Menget al., “Field trial of Gaussian process learning of function-agnostic channel performance under uncertainty,” in Proc. Opt. Fiber Commun. Conf. Exhib., 2018, Paper W4F.5.

Meng, K.

T. S. R. Shen, K. Meng, A. P. T. Lau, and Z. Y. Dong, “Optical performance monitoring using artificial neural network trained with asynchronous amplitude histograms,” IEEE Photon. Technol. Lett., vol. 22, no. 22, pp. 1665–1667,  2010.

Morikawa, H.

T. Tanimura, T. Hoshida, T. Kato, S. Watanabe, and H. Morikawa, “Convolutional neural network-based optical performance monitoring for optical transport networks,” J. Opt. Commun. Netw., vol. 11, pp. A52–A59, 2019.

T. Tanimura, T. Hoshida, T. Kato, S. Watanabe, and H. Morikawa, “Simple learning method to guarantee operational range of optical monitors,” J. Opt. Commun. Netw., vol. 10, pp. D63–D71, 2018.

T. Tanimura, T. Hoshida, T. Kato, S. Watanabe, M. Suzuki, and H. Morikawa, “Throughput and latency programmable optical transceiver by using DSP and FEC control,” Opt. Express, vol. 25, no. 10, pp. 10815–10827,  2017.

T. Tanimura, T. Hoshida, T. Kato, S. Watanabe, and H. Morikawa, “Data-analytics-based optical performance monitoring technique for optical transport networks (invited),” in Proc. Opt. Fiber Commun. Conf. Exhib., 2018, Paper Tu3E.3.

T. Tanimura, T. Hoshida, J. C. Rasmussen, M. Suzuki, and H. Morikawa, “OSNR monitoring by deep neural networks trained with asynchronously sampled data,” in Proc. Optoelectron. Commun. Conf./Int. Conf. Photon. Switching, 2016, Paper TuB3-5.

Novak, R.

J. Lee, J. Sohl-Dickstein, J. Pennington, R. Novak, S. Schoenholz, and Y. Bahri, “Deep neural networks as Gaussian processes,” in Proc. Int. Conf. Learn. Represent., 2018.

Oda, S.

S. Odaet al., “A learning living network with open ROADMs,” J. Lightw. Technol., vol. 35, no. 8, pp. 1350–1356,  2017.

Paraschis, L.

X. Wu, J. A. Jargon, R. A. Skoog, L. Paraschis, and A. E. Willner, “Applications of artificial neural networks in optical performance monitoring,” J. Lightw Technol., vol. 27, no. 16, pp. 3580–3589,  2009.

Pennington, J.

J. Lee, J. Sohl-Dickstein, J. Pennington, R. Novak, S. Schoenholz, and Y. Bahri, “Deep neural networks as Gaussian processes,” in Proc. Int. Conf. Learn. Represent., 2018.

Piels, M.

D. Zibar, M. Piels, R. Jones, and C. G. Schaeffer, “Machine learning techniques in optical communication,” J. Lightw. Technol., vol. 34, no. 6, pp. 1442–1452,  2016.

J. Wass, J. Thrane, M. Piels, R. Jones, and D. Zibar, “Gaussian process regression for WDM system performance prediction,” in Proc. Opt. Fiber Commun. Conf. Exhib., 2017, Paper Tu3D.7.

Rasmussen, C. E.

C. E. Rasmussen and C. K. I. Williams, Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning). Cambridge, MA, USA: MIT Press, 2006.

Rasmussen, J. C.

T. Tanimura, T. Hoshida, J. C. Rasmussen, M. Suzuki, and H. Morikawa, “OSNR monitoring by deep neural networks trained with asynchronously sampled data,” in Proc. Optoelectron. Commun. Conf./Int. Conf. Photon. Switching, 2016, Paper TuB3-5.

Schaeffer, C. G.

D. Zibar, M. Piels, R. Jones, and C. G. Schaeffer, “Machine learning techniques in optical communication,” J. Lightw. Technol., vol. 34, no. 6, pp. 1442–1452,  2016.

Schoenholz, S.

J. Lee, J. Sohl-Dickstein, J. Pennington, R. Novak, S. Schoenholz, and Y. Bahri, “Deep neural networks as Gaussian processes,” in Proc. Int. Conf. Learn. Represent., 2018.

Shen, T. S. R.

F. N. Khan, T. S. R. Shen, Y. Zhou, A. P. T. Lau, and C. Lu, “Optical performance monitoring using artificial neural networks trained with empirical moments of asynchronously sampled signal amplitudes,” IEEE Photon. Technol. Lett., vol. 24, no. 12, pp. 982–984,  2012.

T. S. R. Shen, K. Meng, A. P. T. Lau, and Z. Y. Dong, “Optical performance monitoring using artificial neural network trained with asynchronous amplitude histograms,” IEEE Photon. Technol. Lett., vol. 22, no. 22, pp. 1665–1667,  2010.

Skoog, R. A.

X. Wu, J. A. Jargon, R. A. Skoog, L. Paraschis, and A. E. Willner, “Applications of artificial neural networks in optical performance monitoring,” J. Lightw Technol., vol. 27, no. 16, pp. 3580–3589,  2009.

Sohl-Dickstein, J.

J. Lee, J. Sohl-Dickstein, J. Pennington, R. Novak, S. Schoenholz, and Y. Bahri, “Deep neural networks as Gaussian processes,” in Proc. Int. Conf. Learn. Represent., 2018.

Song, Z.

Z. Allen-Zhu, Y. Li, and Z. Song, “A convergence theory for deep learning via over-parameterization,” 2018, arXiv:1811.03962.

Srivastava, N.

N. Srivastavaet al., “Dropout: A simple way to prevent neural networks from over fitting,” J. Mach. Learn. Res., vol. 15, pp. 1929–1958, 2014.

Suzuki, M.

T. Tanimura, T. Hoshida, T. Kato, S. Watanabe, M. Suzuki, and H. Morikawa, “Throughput and latency programmable optical transceiver by using DSP and FEC control,” Opt. Express, vol. 25, no. 10, pp. 10815–10827,  2017.

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