Abstract

To address the open and diverse situation of future optical networks, it is necessary to find a methodology to accurately estimate the value of a target quantity in an optical performance monitor (OPM) depending on the high-level monitoring objectives declared by the network operator. Using machine learning techniques partially enables a trainable OPM; however, it still requires the feature selection before the learning process. Here, we show the OPM that uses a convolutional neural network (CNN) with a digital coherent receiver to deal with the abundance of training data required for convergence and pre-processing of input data by human engineers needed for feature (representation) extraction. To proof a concept of the OPM based on CNN, we experimentally demonstrate that a CNN can learn an accurate optical signal-to-noise-ratio (OSNR) estimation functionality from asynchronously sampled data right after intradyne coherent detection. We evaluate bias errors and standard deviations of a CNN-based OSNR estimator for six combinations of modulation formats and symbol rates and confirm that the proposed OSNR estimator can provide accurate estimation results (<0.4  dB bias errors and standard deviations). Additionally, we investigate filters in the trained CNN to reveal what the CNN learned in the training phase. This is a valuable step toward designing autonomous “self-driving” optical networks.

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

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References

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2017 (6)

2016 (1)

F. N. Khan, K. Zhong, W. H. Al-Arashi, C. Yu, C. Lu, and A. P. T. Lau, “Modulation format identification in coherent receivers using deep machine learning,” IEEE Photon. Technol. Lett., vol.  28, no. 17, pp. 1886–1889, 2016.
[Crossref]

2015 (2)

2014 (2)

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from over fitting,” J. Mach. Learn. Res., vol.  15, pp. 1929–1958, 2014.

M. C. Tan, F. N. Khan, W. H. Al-Arashi, Y. D. Zhou, and A. P. T. Lau, “Simultaneous optical performance monitoring and modulation format/bit-rate identification using principal component analysis,” J. Opt. Commun. Netw., vol.  6, no. 5, pp. 441–448, 2014.
[Crossref]

2012 (2)

T. S. R. Shen, Q. Sui, and A. P. T. Lau, “OSNR monitoring for PM-QPSK systems with large inline chromatic dispersion using artificial neural network,” IEEE Photon. Technol. Lett., vol.  24, no. 17, pp. 1564–1567, 2012.
[Crossref]

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.
[Crossref]

2011 (1)

X. Wu, J. A. Jargon, L. Paraschis, and A. E. Willner, “ANN-based optical performance monitoring of QPSK signals using parameters derived from balanced-detected asynchronous diagrams,” IEEE Photon. Technol. Lett., vol.  23, no. 4, pp. 248–250, 2011.
[Crossref]

2010 (1)

2009 (1)

2006 (2)

Abadi, M.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, and M. Kudlur, “Tensorflow: a system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI), 2016.

Aguado, A.

Al-Arashi, W. H.

Aoki, Y.

Ba, J.

J. Ba and D. Kingma, “Adam: a method for stochastic optimization,” in 3rd Int. Conf. on Learning Representations (ICLR), 2015.

Barham, P.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, and M. Kudlur, “Tensorflow: a system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI), 2016.

Bengio, Y.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol.  521, pp. 436–444, 2015.
[Crossref]

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning (MIT, 2016).

Bengui, Y.

X. Glorot, A. Bordes, and Y. Bengui, “Deep sparse rectifier neural networks,” in 14th Int. Conf. on Artificial Intelligence and Statistics (AISTATS), 2011, pp. 315–323.

Birk, M.

Bishop, C. M.

C. M. Bishop, Pattern Recognition and Machine Learning (Springer, 2006).

Bordes, A.

X. Glorot, A. Bordes, and Y. Bengui, “Deep sparse rectifier neural networks,” in 14th Int. Conf. on Artificial Intelligence and Statistics (AISTATS), 2011, pp. 315–323.

Chen, J.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, and M. Kudlur, “Tensorflow: a system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI), 2016.

Chen, X.

D. Wang, M. Zhang, Z. Li, J. Li, M. Fu, Y. Cui, and X. Chen, “Modulation format recognition and OSNR estimation using CNN-based deep learning,” IEEE Photon. Technol. Lett., vol.  29, no. 19, pp. 1667–1670, 2017.
[Crossref]

D. Wang, M. Zhang, J. Li, Z. Li, J. Li, C. Song, and X. Chen, “Intelligent constellation diagram analyser using convolutional neural network-based deep learning,” Opt. Express, vol.  25, no. 15, pp. 17150–17166, 2017.
[Crossref]

Chen, Z.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, and M. Kudlur, “Tensorflow: a system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI), 2016.

Choi, H. Y.

Chung, Y. C.

Courville, A.

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning (MIT, 2016).

Cui, Y.

D. Wang, M. Zhang, Z. Li, J. Li, M. Fu, Y. Cui, and X. Chen, “Modulation format recognition and OSNR estimation using CNN-based deep learning,” IEEE Photon. Technol. Lett., vol.  29, no. 19, pp. 1667–1670, 2017.
[Crossref]

Davis, A.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, and M. Kudlur, “Tensorflow: a system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI), 2016.

Dean, J.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, and M. Kudlur, “Tensorflow: a system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI), 2016.

Devin, M.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, and M. Kudlur, “Tensorflow: a system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI), 2016.

Diniz, J. C. M.

Fan, Q.

S. Yan, F. N. Khan, A. Mavromatis, D. Gkounis, Q. Fan, F. Ntavou, K. Nikolovgenis, F. Meng, E. H. Salas, C. Guo, C. Lu, A. P. T. Lau, R. Nejabati, and D. Simeonidou, “Field trial of machine-learning-assisted and SDN-based optical network planning with network-scale monitoring database,” in 43rd European Conf. on Optical Communication (ECOC), 2017.

Fu, M.

D. Wang, M. Zhang, Z. Li, J. Li, M. Fu, Y. Cui, and X. Chen, “Modulation format recognition and OSNR estimation using CNN-based deep learning,” IEEE Photon. Technol. Lett., vol.  29, no. 19, pp. 1667–1670, 2017.
[Crossref]

Ghemawat, S.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, and M. Kudlur, “Tensorflow: a system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI), 2016.

Gkounis, D.

S. Yan, F. N. Khan, A. Mavromatis, D. Gkounis, Q. Fan, F. Ntavou, K. Nikolovgenis, F. Meng, E. H. Salas, C. Guo, C. Lu, A. P. T. Lau, R. Nejabati, and D. Simeonidou, “Field trial of machine-learning-assisted and SDN-based optical network planning with network-scale monitoring database,” in 43rd European Conf. on Optical Communication (ECOC), 2017.

Glorot, X.

X. Glorot, A. Bordes, and Y. Bengui, “Deep sparse rectifier neural networks,” in 14th Int. Conf. on Artificial Intelligence and Statistics (AISTATS), 2011, pp. 315–323.

Goodfellow, I.

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning (MIT, 2016).

Guo, C.

S. Yan, F. N. Khan, A. Mavromatis, D. Gkounis, Q. Fan, F. Ntavou, K. Nikolovgenis, F. Meng, E. H. Salas, C. Guo, C. Lu, A. P. T. Lau, R. Nejabati, and D. Simeonidou, “Field trial of machine-learning-assisted and SDN-based optical network planning with network-scale monitoring database,” in 43rd European Conf. on Optical Communication (ECOC), 2017.

Hinton, G.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol.  521, pp. 436–444, 2015.
[Crossref]

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from over fitting,” J. Mach. Learn. Res., vol.  15, pp. 1929–1958, 2014.

Hinton, G. E.

G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science, vol.  313, no. 5786, pp. 504–507, 2006.
[Crossref]

Hoshida, T.

S. Oda, M. Miyabe, S. Yoshida, T. Katagiri, Y. Aoki, T. Hoshida, J. C. Rasmussen, M. Birk, and K. Tse, “A learning living network with open ROADMs,” J. Lightwave Technol., vol.  35, no. 8, pp. 1350–1356, 2017.
[Crossref]

T. Tanimura, T. Hoshida, J. C. Rasmussen, M. Suzuki, and H. Morikawa, “OSNR monitoring by deep neural networks trained with asynchronously sampled data,” in OptoElectronics and Communications Conference held jointly with Int. Conf. on Photonics in Switching (OECC/PS), 2016, paper TuB3-5.

T. Tanimura, T. Hoshida, T. Kato, S. Watanabe, J. C. Rasmussen, M. Suzuki, and H. Morikawa, “Deep learning based OSNR monitoring independent of modulation format, symbol rate and chromatic dispersion,” in European Conf. on Optical Communication, 2016, paper Tu2C.2.

T. Tanimura, T. Hoshida, T. Kato, S. Watanabe, and H. Morikawa, “Data-analytics-based optical performance monitoring technique for optical transport networks (Invited),” in Optical Fiber Communications Conf. and Exhibition (OFC), 2018, paper Tu3E.3.

Ioffe, S.

S. Ioffe and C. Szegedy, “Batch normalization: accelerating deep network training by reducing internal covariate shift,” in 32nd Int. Conf. on Machine Learning (ICML), 2015.

Irving, G.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, and M. Kudlur, “Tensorflow: a system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI), 2016.

Isard, M.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, and M. Kudlur, “Tensorflow: a system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI), 2016.

Jargon, J. A.

Jones, R.

Katagiri, T.

Kato, T.

T. Tanimura, T. Hoshida, T. Kato, S. Watanabe, J. C. Rasmussen, M. Suzuki, and H. Morikawa, “Deep learning based OSNR monitoring independent of modulation format, symbol rate and chromatic dispersion,” in European Conf. on Optical Communication, 2016, paper Tu2C.2.

T. Tanimura, T. Hoshida, T. Kato, S. Watanabe, and H. Morikawa, “Data-analytics-based optical performance monitoring technique for optical transport networks (Invited),” in Optical Fiber Communications Conf. and Exhibition (OFC), 2018, paper Tu3E.3.

Katoh, K.

Khan, F. N.

F. N. Khan, K. Zhong, X. Zhou, W. H. Al-Arashi, C. Yu, C. Lu, and A. P. T. Lau, “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.
[Crossref]

F. N. Khan, K. Zhong, W. H. Al-Arashi, C. Yu, C. Lu, and A. P. T. Lau, “Modulation format identification in coherent receivers using deep machine learning,” IEEE Photon. Technol. Lett., vol.  28, no. 17, pp. 1886–1889, 2016.
[Crossref]

F. N. Khan, Y. Yu, M. C. Tan, W. H. Al-Arashi, C. Yu, A. P. T. Lau, and C. Lu, “Experimental demonstration of joint OSNR monitoring and modulation format identification using asynchronous single channel sampling,” Opt. Express, vol.  23, pp. 30337–30346, 2015.
[Crossref]

M. C. Tan, F. N. Khan, W. H. Al-Arashi, Y. D. Zhou, and A. P. T. Lau, “Simultaneous optical performance monitoring and modulation format/bit-rate identification using principal component analysis,” J. Opt. Commun. Netw., vol.  6, no. 5, pp. 441–448, 2014.
[Crossref]

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.
[Crossref]

S. Yan, F. N. Khan, A. Mavromatis, D. Gkounis, Q. Fan, F. Ntavou, K. Nikolovgenis, F. Meng, E. H. Salas, C. Guo, C. Lu, A. P. T. Lau, R. Nejabati, and D. Simeonidou, “Field trial of machine-learning-assisted and SDN-based optical network planning with network-scale monitoring database,” in 43rd European Conf. on Optical Communication (ECOC), 2017.

Kikuchi, K.

Kingma, D.

J. Ba and D. Kingma, “Adam: a method for stochastic optimization,” in 3rd Int. Conf. on Learning Representations (ICLR), 2015.

Krizhevsky, A.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from over fitting,” J. Mach. Learn. Res., vol.  15, pp. 1929–1958, 2014.

Kudlur, M.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, and M. Kudlur, “Tensorflow: a system for large-scale machine learning,” in 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI), 2016.

Lau, A. P. T.

F. N. Khan, K. Zhong, X. Zhou, W. H. Al-Arashi, C. Yu, C. Lu, and A. P. T. Lau, “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.
[Crossref]

F. N. Khan, K. Zhong, W. H. Al-Arashi, C. Yu, C. Lu, and A. P. T. Lau, “Modulation format identification in coherent receivers using deep machine learning,” IEEE Photon. Technol. Lett., vol.  28, no. 17, pp. 1886–1889, 2016.
[Crossref]

F. N. Khan, Y. Yu, M. C. Tan, W. H. Al-Arashi, C. Yu, A. P. T. Lau, and C. Lu, “Experimental demonstration of joint OSNR monitoring and modulation format identification using asynchronous single channel sampling,” Opt. Express, vol.  23, pp. 30337–30346, 2015.
[Crossref]

M. C. Tan, F. N. Khan, W. H. Al-Arashi, Y. D. Zhou, and A. P. T. Lau, “Simultaneous optical performance monitoring and modulation format/bit-rate identification using principal component analysis,” J. Opt. Commun. Netw., vol.  6, no. 5, pp. 441–448, 2014.
[Crossref]

T. S. R. Shen, Q. Sui, and A. P. T. Lau, “OSNR monitoring for PM-QPSK systems with large inline chromatic dispersion using artificial neural network,” IEEE Photon. Technol. Lett., vol.  24, no. 17, pp. 1564–1567, 2012.
[Crossref]

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.
[Crossref]

S. Yan, F. N. Khan, A. Mavromatis, D. Gkounis, Q. Fan, F. Ntavou, K. Nikolovgenis, F. Meng, E. H. Salas, C. Guo, C. Lu, A. P. T. Lau, R. Nejabati, and D. Simeonidou, “Field trial of machine-learning-assisted and SDN-based optical network planning with network-scale monitoring database,” in 43rd European Conf. on Optical Communication (ECOC), 2017.

LeCun, Y.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol.  521, pp. 436–444, 2015.
[Crossref]

Li, J.

Li, Z.

D. Wang, M. Zhang, Z. Li, J. Li, M. Fu, Y. Cui, and X. Chen, “Modulation format recognition and OSNR estimation using CNN-based deep learning,” IEEE Photon. Technol. Lett., vol.  29, no. 19, pp. 1667–1670, 2017.
[Crossref]

D. Wang, M. Zhang, J. Li, Z. Li, J. Li, C. Song, and X. Chen, “Intelligent constellation diagram analyser using convolutional neural network-based deep learning,” Opt. Express, vol.  25, no. 15, pp. 17150–17166, 2017.
[Crossref]

Lu, C.

F. N. Khan, K. Zhong, X. Zhou, W. H. Al-Arashi, C. Yu, C. Lu, and A. P. T. Lau, “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.
[Crossref]

F. N. Khan, K. Zhong, W. H. Al-Arashi, C. Yu, C. Lu, and A. P. T. Lau, “Modulation format identification in coherent receivers using deep machine learning,” IEEE Photon. Technol. Lett., vol.  28, no. 17, pp. 1886–1889, 2016.
[Crossref]

F. N. Khan, Y. Yu, M. C. Tan, W. H. Al-Arashi, C. Yu, A. P. T. Lau, and C. Lu, “Experimental demonstration of joint OSNR monitoring and modulation format identification using asynchronous single channel sampling,” Opt. Express, vol.  23, pp. 30337–30346, 2015.
[Crossref]

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.
[Crossref]

S. Yan, F. N. Khan, A. Mavromatis, D. Gkounis, Q. Fan, F. Ntavou, K. Nikolovgenis, F. Meng, E. H. Salas, C. Guo, C. Lu, A. P. T. Lau, R. Nejabati, and D. Simeonidou, “Field trial of machine-learning-assisted and SDN-based optical network planning with network-scale monitoring database,” in 43rd European Conf. on Optical Communication (ECOC), 2017.

Ly-Gagnon, D.-S.

Mavromatis, A.

S. Yan, F. N. Khan, A. Mavromatis, D. Gkounis, Q. Fan, F. Ntavou, K. Nikolovgenis, F. Meng, E. H. Salas, C. Guo, C. Lu, A. P. T. Lau, R. Nejabati, and D. Simeonidou, “Field trial of machine-learning-assisted and SDN-based optical network planning with network-scale monitoring database,” in 43rd European Conf. on Optical Communication (ECOC), 2017.

Meng, F.

S. Yan, F. N. Khan, A. Mavromatis, D. Gkounis, Q. Fan, F. Ntavou, K. Nikolovgenis, F. Meng, E. H. Salas, C. Guo, C. Lu, A. P. T. Lau, R. Nejabati, and D. Simeonidou, “Field trial of machine-learning-assisted and SDN-based optical network planning with network-scale monitoring database,” in 43rd European Conf. on Optical Communication (ECOC), 2017.

F. Meng, Y. Ou, S. Yan, K. Sideris, M. D. G. Pascual, R. Nejabati, and D. Simeonidou, “Field trial of a novel SDN enabled network restoration utilizing in-depth optical performance monitoring assisted network re-planning,” in Optical Fiber Communication Conf. (OFC), 2017, paper Th1J.8.

Miyabe, M.

Morikawa, H.

T. Tanimura, T. Hoshida, T. Kato, S. Watanabe, J. C. Rasmussen, M. Suzuki, and H. Morikawa, “Deep learning based OSNR monitoring independent of modulation format, symbol rate and chromatic dispersion,” in European Conf. on Optical Communication, 2016, paper Tu2C.2.

T. Tanimura, T. Hoshida, J. C. Rasmussen, M. Suzuki, and H. Morikawa, “OSNR monitoring by deep neural networks trained with asynchronously sampled data,” in OptoElectronics and Communications Conference held jointly with Int. Conf. on Photonics in Switching (OECC/PS), 2016, paper TuB3-5.

T. Tanimura, T. Hoshida, T. Kato, S. Watanabe, and H. Morikawa, “Data-analytics-based optical performance monitoring technique for optical transport networks (Invited),” in Optical Fiber Communications Conf. and Exhibition (OFC), 2018, paper Tu3E.3.

Nejabati, R.

S. Yan, A. Aguado, Y. Ou, R. Wang, R. Nejabati, and D. Simeonidou, “Multi-layer network analytics with SDN-based monitoring framework,” J. Opt. Commun. Netw., vol.  9, no. 2, pp. A271–A279, 2017.
[Crossref]

S. Yan, F. N. Khan, A. Mavromatis, D. Gkounis, Q. Fan, F. Ntavou, K. Nikolovgenis, F. Meng, E. H. Salas, C. Guo, C. Lu, A. P. T. Lau, R. Nejabati, and D. Simeonidou, “Field trial of machine-learning-assisted and SDN-based optical network planning with network-scale monitoring database,” in 43rd European Conf. on Optical Communication (ECOC), 2017.

F. Meng, Y. Ou, S. Yan, K. Sideris, M. D. G. Pascual, R. Nejabati, and D. Simeonidou, “Field trial of a novel SDN enabled network restoration utilizing in-depth optical performance monitoring assisted network re-planning,” in Optical Fiber Communication Conf. (OFC), 2017, paper Th1J.8.

Nikolovgenis, K.

S. Yan, F. N. Khan, A. Mavromatis, D. Gkounis, Q. Fan, F. Ntavou, K. Nikolovgenis, F. Meng, E. H. Salas, C. Guo, C. Lu, A. P. T. Lau, R. Nejabati, and D. Simeonidou, “Field trial of machine-learning-assisted and SDN-based optical network planning with network-scale monitoring database,” in 43rd European Conf. on Optical Communication (ECOC), 2017.

Ntavou, F.

S. Yan, F. N. Khan, A. Mavromatis, D. Gkounis, Q. Fan, F. Ntavou, K. Nikolovgenis, F. Meng, E. H. Salas, C. Guo, C. Lu, A. P. T. Lau, R. Nejabati, and D. Simeonidou, “Field trial of machine-learning-assisted and SDN-based optical network planning with network-scale monitoring database,” in 43rd European Conf. on Optical Communication (ECOC), 2017.

Oda, S.

Ou, Y.

S. Yan, A. Aguado, Y. Ou, R. Wang, R. Nejabati, and D. Simeonidou, “Multi-layer network analytics with SDN-based monitoring framework,” J. Opt. Commun. Netw., vol.  9, no. 2, pp. A271–A279, 2017.
[Crossref]

F. Meng, Y. Ou, S. Yan, K. Sideris, M. D. G. Pascual, R. Nejabati, and D. Simeonidou, “Field trial of a novel SDN enabled network restoration utilizing in-depth optical performance monitoring assisted network re-planning,” in Optical Fiber Communication Conf. (OFC), 2017, paper Th1J.8.

Paraschis, L.

X. Wu, J. A. Jargon, L. Paraschis, and A. E. Willner, “ANN-based optical performance monitoring of QPSK signals using parameters derived from balanced-detected asynchronous diagrams,” IEEE Photon. Technol. Lett., vol.  23, no. 4, pp. 248–250, 2011.
[Crossref]

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

Pascual, M. D. G.

F. Meng, Y. Ou, S. Yan, K. Sideris, M. D. G. Pascual, R. Nejabati, and D. Simeonidou, “Field trial of a novel SDN enabled network restoration utilizing in-depth optical performance monitoring assisted network re-planning,” in Optical Fiber Communication Conf. (OFC), 2017, paper Th1J.8.

Piels, M.

Rasmussen, J. C.

S. Oda, M. Miyabe, S. Yoshida, T. Katagiri, Y. Aoki, T. Hoshida, J. C. Rasmussen, M. Birk, and K. Tse, “A learning living network with open ROADMs,” J. Lightwave Technol., vol.  35, no. 8, pp. 1350–1356, 2017.
[Crossref]

T. Tanimura, T. Hoshida, T. Kato, S. Watanabe, J. C. Rasmussen, M. Suzuki, and H. Morikawa, “Deep learning based OSNR monitoring independent of modulation format, symbol rate and chromatic dispersion,” in European Conf. on Optical Communication, 2016, paper Tu2C.2.

T. Tanimura, T. Hoshida, J. C. Rasmussen, M. Suzuki, and H. Morikawa, “OSNR monitoring by deep neural networks trained with asynchronously sampled data,” in OptoElectronics and Communications Conference held jointly with Int. Conf. on Photonics in Switching (OECC/PS), 2016, paper TuB3-5.

Salakhutdinov, R.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from over fitting,” J. Mach. Learn. Res., vol.  15, pp. 1929–1958, 2014.

Salakhutdinov, R. R.

G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science, vol.  313, no. 5786, pp. 504–507, 2006.
[Crossref]

Salas, E. H.

S. Yan, F. N. Khan, A. Mavromatis, D. Gkounis, Q. Fan, F. Ntavou, K. Nikolovgenis, F. Meng, E. H. Salas, C. Guo, C. Lu, A. P. T. Lau, R. Nejabati, and D. Simeonidou, “Field trial of machine-learning-assisted and SDN-based optical network planning with network-scale monitoring database,” in 43rd European Conf. on Optical Communication (ECOC), 2017.

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.
[Crossref]

T. S. R. Shen, Q. Sui, and A. P. T. Lau, “OSNR monitoring for PM-QPSK systems with large inline chromatic dispersion using artificial neural network,” IEEE Photon. Technol. Lett., vol.  24, no. 17, pp. 1564–1567, 2012.
[Crossref]

Sideris, K.

F. Meng, Y. Ou, S. Yan, K. Sideris, M. D. G. Pascual, R. Nejabati, and D. Simeonidou, “Field trial of a novel SDN enabled network restoration utilizing in-depth optical performance monitoring assisted network re-planning,” in Optical Fiber Communication Conf. (OFC), 2017, paper Th1J.8.

Simeonidou, D.

S. Yan, A. Aguado, Y. Ou, R. Wang, R. Nejabati, and D. Simeonidou, “Multi-layer network analytics with SDN-based monitoring framework,” J. Opt. Commun. Netw., vol.  9, no. 2, pp. A271–A279, 2017.
[Crossref]

S. Yan, F. N. Khan, A. Mavromatis, D. Gkounis, Q. Fan, F. Ntavou, K. Nikolovgenis, F. Meng, E. H. Salas, C. Guo, C. Lu, A. P. T. Lau, R. Nejabati, and D. Simeonidou, “Field trial of machine-learning-assisted and SDN-based optical network planning with network-scale monitoring database,” in 43rd European Conf. on Optical Communication (ECOC), 2017.

F. Meng, Y. Ou, S. Yan, K. Sideris, M. D. G. Pascual, R. Nejabati, and D. Simeonidou, “Field trial of a novel SDN enabled network restoration utilizing in-depth optical performance monitoring assisted network re-planning,” in Optical Fiber Communication Conf. (OFC), 2017, paper Th1J.8.

Skoog, R. A.

Song, C.

Srivastava, N.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from over fitting,” J. Mach. Learn. Res., vol.  15, pp. 1929–1958, 2014.

Sui, Q.

T. S. R. Shen, Q. Sui, and A. P. T. Lau, “OSNR monitoring for PM-QPSK systems with large inline chromatic dispersion using artificial neural network,” IEEE Photon. Technol. Lett., vol.  24, no. 17, pp. 1564–1567, 2012.
[Crossref]

Sutskever, I.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “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, J. C. Rasmussen, M. Suzuki, and H. Morikawa, “Deep learning based OSNR monitoring independent of modulation format, symbol rate and chromatic dispersion,” in European Conf. on Optical Communication, 2016, paper Tu2C.2.

T. Tanimura, T. Hoshida, J. C. Rasmussen, M. Suzuki, and H. Morikawa, “OSNR monitoring by deep neural networks trained with asynchronously sampled data,” in OptoElectronics and Communications Conference held jointly with Int. Conf. on Photonics in Switching (OECC/PS), 2016, paper TuB3-5.

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S. Ioffe and C. Szegedy, “Batch normalization: accelerating deep network training by reducing internal covariate shift,” in 32nd Int. Conf. on Machine Learning (ICML), 2015.

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Tanimura, T.

T. Tanimura, T. Hoshida, J. C. Rasmussen, M. Suzuki, and H. Morikawa, “OSNR monitoring by deep neural networks trained with asynchronously sampled data,” in OptoElectronics and Communications Conference held jointly with Int. Conf. on Photonics in Switching (OECC/PS), 2016, paper TuB3-5.

T. Tanimura, T. Hoshida, T. Kato, S. Watanabe, J. C. Rasmussen, M. Suzuki, and H. Morikawa, “Deep learning based OSNR monitoring independent of modulation format, symbol rate and chromatic dispersion,” in European Conf. on Optical Communication, 2016, paper Tu2C.2.

T. Tanimura, T. Hoshida, T. Kato, S. Watanabe, and H. Morikawa, “Data-analytics-based optical performance monitoring technique for optical transport networks (Invited),” in Optical Fiber Communications Conf. and Exhibition (OFC), 2018, paper Tu3E.3.

Thrane, J.

Tse, K.

Tsukamoto, S.

Wang, D.

D. Wang, M. Zhang, Z. Li, J. Li, M. Fu, Y. Cui, and X. Chen, “Modulation format recognition and OSNR estimation using CNN-based deep learning,” IEEE Photon. Technol. Lett., vol.  29, no. 19, pp. 1667–1670, 2017.
[Crossref]

D. Wang, M. Zhang, J. Li, Z. Li, J. Li, C. Song, and X. Chen, “Intelligent constellation diagram analyser using convolutional neural network-based deep learning,” Opt. Express, vol.  25, no. 15, pp. 17150–17166, 2017.
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Wang, R.

Wass, J.

Watanabe, S.

T. Tanimura, T. Hoshida, T. Kato, S. Watanabe, and H. Morikawa, “Data-analytics-based optical performance monitoring technique for optical transport networks (Invited),” in Optical Fiber Communications Conf. and Exhibition (OFC), 2018, paper Tu3E.3.

T. Tanimura, T. Hoshida, T. Kato, S. Watanabe, J. C. Rasmussen, M. Suzuki, and H. Morikawa, “Deep learning based OSNR monitoring independent of modulation format, symbol rate and chromatic dispersion,” in European Conf. on Optical Communication, 2016, paper Tu2C.2.

Willner, A. E.

Wu, X.

Yan, S.

S. Yan, A. Aguado, Y. Ou, R. Wang, R. Nejabati, and D. Simeonidou, “Multi-layer network analytics with SDN-based monitoring framework,” J. Opt. Commun. Netw., vol.  9, no. 2, pp. A271–A279, 2017.
[Crossref]

F. Meng, Y. Ou, S. Yan, K. Sideris, M. D. G. Pascual, R. Nejabati, and D. Simeonidou, “Field trial of a novel SDN enabled network restoration utilizing in-depth optical performance monitoring assisted network re-planning,” in Optical Fiber Communication Conf. (OFC), 2017, paper Th1J.8.

S. Yan, F. N. Khan, A. Mavromatis, D. Gkounis, Q. Fan, F. Ntavou, K. Nikolovgenis, F. Meng, E. H. Salas, C. Guo, C. Lu, A. P. T. Lau, R. Nejabati, and D. Simeonidou, “Field trial of machine-learning-assisted and SDN-based optical network planning with network-scale monitoring database,” in 43rd European Conf. on Optical Communication (ECOC), 2017.

Yoshida, S.

Yu, C.

Yu, Y.

Zhang, M.

D. Wang, M. Zhang, J. Li, Z. Li, J. Li, C. Song, and X. Chen, “Intelligent constellation diagram analyser using convolutional neural network-based deep learning,” Opt. Express, vol.  25, no. 15, pp. 17150–17166, 2017.
[Crossref]

D. Wang, M. Zhang, Z. Li, J. Li, M. Fu, Y. Cui, and X. Chen, “Modulation format recognition and OSNR estimation using CNN-based deep learning,” IEEE Photon. Technol. Lett., vol.  29, no. 19, pp. 1667–1670, 2017.
[Crossref]

Zhong, K.

F. N. Khan, K. Zhong, X. Zhou, W. H. Al-Arashi, C. Yu, C. Lu, and A. P. T. Lau, “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.
[Crossref]

F. N. Khan, K. Zhong, W. H. Al-Arashi, C. Yu, C. Lu, and A. P. T. Lau, “Modulation format identification in coherent receivers using deep machine learning,” IEEE Photon. Technol. Lett., vol.  28, no. 17, pp. 1886–1889, 2016.
[Crossref]

Zhou, X.

Zhou, Y.

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.
[Crossref]

Zhou, Y. D.

Zibar, D.

IEEE Photon. Technol. Lett. (5)

X. Wu, J. A. Jargon, L. Paraschis, and A. E. Willner, “ANN-based optical performance monitoring of QPSK signals using parameters derived from balanced-detected asynchronous diagrams,” IEEE Photon. Technol. Lett., vol.  23, no. 4, pp. 248–250, 2011.
[Crossref]

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.
[Crossref]

T. S. R. Shen, Q. Sui, and A. P. T. Lau, “OSNR monitoring for PM-QPSK systems with large inline chromatic dispersion using artificial neural network,” IEEE Photon. Technol. Lett., vol.  24, no. 17, pp. 1564–1567, 2012.
[Crossref]

F. N. Khan, K. Zhong, W. H. Al-Arashi, C. Yu, C. Lu, and A. P. T. Lau, “Modulation format identification in coherent receivers using deep machine learning,” IEEE Photon. Technol. Lett., vol.  28, no. 17, pp. 1886–1889, 2016.
[Crossref]

D. Wang, M. Zhang, Z. Li, J. Li, M. Fu, Y. Cui, and X. Chen, “Modulation format recognition and OSNR estimation using CNN-based deep learning,” IEEE Photon. Technol. Lett., vol.  29, no. 19, pp. 1667–1670, 2017.
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T. Tanimura, T. Hoshida, J. C. Rasmussen, M. Suzuki, and H. Morikawa, “OSNR monitoring by deep neural networks trained with asynchronously sampled data,” in OptoElectronics and Communications Conference held jointly with Int. Conf. on Photonics in Switching (OECC/PS), 2016, paper TuB3-5.

T. Tanimura, T. Hoshida, T. Kato, S. Watanabe, J. C. Rasmussen, M. Suzuki, and H. Morikawa, “Deep learning based OSNR monitoring independent of modulation format, symbol rate and chromatic dispersion,” in European Conf. on Optical Communication, 2016, paper Tu2C.2.

F. Meng, Y. Ou, S. Yan, K. Sideris, M. D. G. Pascual, R. Nejabati, and D. Simeonidou, “Field trial of a novel SDN enabled network restoration utilizing in-depth optical performance monitoring assisted network re-planning,” in Optical Fiber Communication Conf. (OFC), 2017, paper Th1J.8.

S. Yan, F. N. Khan, A. Mavromatis, D. Gkounis, Q. Fan, F. Ntavou, K. Nikolovgenis, F. Meng, E. H. Salas, C. Guo, C. Lu, A. P. T. Lau, R. Nejabati, and D. Simeonidou, “Field trial of machine-learning-assisted and SDN-based optical network planning with network-scale monitoring database,” in 43rd European Conf. on Optical Communication (ECOC), 2017.

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Figures (13)

Fig. 1.
Fig. 1. Diagram of CNN-based optical physical-layer monitor.
Fig. 2.
Fig. 2. Diagram of FC layer and neuron.
Fig. 3.
Fig. 3. Experimental and simulation setup. DAC, digital-to-analog converter; InP IQM, indium phosphide in-phase and quadrature-phase modulator; LD, laser diode; EDFA, erbium-doped fiber amplifier; VOA, variable optical attenuator; ASE, amplified spontaneous emission source; OBPF, optical bandpass filter; ADC, analog-to-digital converter.
Fig. 4.
Fig. 4. Convolutional neural network architecture for OSNR estimation.
Fig. 5.
Fig. 5. Evaluation results of CNN-based OSNR estimator by using 16-GBd DP-QPSK training data set.
Fig. 6.
Fig. 6. Evaluation results of CNN-based OSNR estimator by using 16-GBd DP-QPSK test data set.
Fig. 7.
Fig. 7. Bias error of CNN-based OSNR estimator using test data set with 14- and 16-GBd DP-QPSK/16QAM/64QAM.
Fig. 8.
Fig. 8. Standard deviation of CNN-based OSNR estimator by using test data set with 14- and 16-GBd DP-QPSK/16QAM/64QAM.
Fig. 9.
Fig. 9. Bias error and standard deviation of CNN-based OSNR estimator as a function of residual chromatic dispersion.
Fig. 10.
Fig. 10. Kernels of Conv. 1-1 of the trained CNN in time-domain.
Fig. 11.
Fig. 11. Kernels of Conv. 1-1 of the trained CNN in frequency-domain.
Fig. 12.
Fig. 12. Example of absolute values of FFT components of input vector for each input channel of Conv. 1-1 of the trained CNN.
Fig. 13.
Fig. 13. Kernels of Conv. 1-2 of the trained CNN in frequency-domain.

Equations (3)

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

s i = f ( k = 0 K 1 j = 0 N 1 x i j , k w j , k + b k ) ,
u i = max p P i x p .
y = f ( j = 1 N w j x j + b ) ,