Abstract

It is necessary to guarantee the operational range of machine learning (ML)-based optical physical-layer monitors (OPMs). To declare high-level monitoring objectives and obtain their values from OPMs, finding a methodology to accurately estimate the value of a target quantity and ensure their operational range is necessary. We introduce a deep neural network (DNN) with a digital coherent receiver to ML-based OPMs to deal with the abundance of training data needed for convergence and the pre-processing of input data by human engineers needed for feature (representation) extraction. However, guaranteeing the operational range of trained models on DNN-based OPMs was left for another investigation. To address this issue with DNN-based OPMs, we propose an “operational range expander,” a simple treatment of the link between pre-processing training datasets and their specified operational range. We assess the operational range expander by performing simulation and experimentation using a DNN-based optical signal-to-noise ratio (OSNR) estimator. We select a laser frequency offset between a signal and a local oscillator in digital coherent receivers as an example quantity for a practical operational range expander in this study. This is because the OPMs need to work before digitally compensating frequency offset despite the difficulty in fully controlling frequency offset in practical situations. We evaluate bias errors and standard deviations of OSNR estimation from different frequency offsets ranging from 3.5 to +3.5  GHz and confirm that the provided operational range expander specified the operational range of DNN-based OSNR estimators through their training phase.

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

Full Article  |  PDF Article
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References

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2017 (7)

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]

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]

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]

J. Thrane, J. Wass, M. Piels, J. C. M. Diniz, R. Jones, and D. Zibar, “Machine learning techniques for optical performance monitoring from directly detected PDM-QAM signals,” J. Lightwave Technol., vol.  35, no. 4, pp. 868–875, 2017.
[Crossref]

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, 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.
[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]

2016 (3)

K. Kikuchi, “Fundamentals of coherent optical fiber communications,” J. Lightwave Technol., vol.  34, no. 1, pp. 157–179, 2016.
[Crossref]

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

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)

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]

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)

1992 (1)

D. J. C. MacKay, “A practical Bayesian framework for backpropagation networks,” Neural Comput., vol.  4, no. 3, pp. 448–472, 1992.
[Crossref]

Abadi, M.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P.Warden, M.Wicke, Y. Yu, and X. Zheng, “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.

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]

Aoki, Y.

Ba, J.

J. Ba and D. Kingma, “Adam: a method for stochastic optimization,” in 3rd Int. Conf. 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, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P.Warden, M.Wicke, Y. Yu, and X. Zheng, “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]

Bengui, Y.

X. Glorot, A. Bordes, and Y. Bengui, “Deep sparse rectifier neural networks,” in 14th Int. Conf. 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. 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, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P.Warden, M.Wicke, Y. Yu, and X. Zheng, “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, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P.Warden, M.Wicke, Y. Yu, and X. Zheng, “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.

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, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P.Warden, M.Wicke, Y. Yu, and X. Zheng, “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, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P.Warden, M.Wicke, Y. Yu, and X. Zheng, “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, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P.Warden, M.Wicke, Y. Yu, and X. Zheng, “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. Optical Communication (ECOC), PDP, 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, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P.Warden, M.Wicke, Y. Yu, and X. Zheng, “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. Optical Communication (ECOC), PDP, 2017.

Glorot, X.

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

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. Optical Communication (ECOC), PDP, 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.

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, 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.
[Crossref]

T. Tanimura, T. Hoshida, T. Kato, S. Watanabe, and H. Morikawa, “Data-analytics-based optical performance monitoring technique for optical transport networks,” in Optical Fiber Communications Conf. and Exhibition (OFC), 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 Int. Conf. Photonics in Switching OptoElectronics and Communications Conf. (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 42nd European Conf. Optical Communication (ECOC), 2016, paper Tu2C.2.

Ioffe, S.

S. Ioffe and C. Szegedy, “Batch normalization: accelerating deep network training by reducing internal covariate shift,” in 32nd Int. Conf. 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, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P.Warden, M.Wicke, Y. Yu, and X. Zheng, “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, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P.Warden, M.Wicke, Y. Yu, and X. Zheng, “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.

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Figures (10)

Fig. 1.
Fig. 1. Schematic diagram of DNN-based optical physical-layer monitor and CNN.
Fig. 2.
Fig. 2. Schematic diagram of (a) DNN-based OPM with operational range expander, and (b) frequency distribution of quantity operated in the operational range expander.
Fig. 3.
Fig. 3. Schematic diagram of frequency offset between signal and local laser in digital coherent receiver. LD, laser diode; BPD, balanced photo diode.
Fig. 4.
Fig. 4. 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 band-pass filter; ADC, analog-to-digital converter.
Fig. 5.
Fig. 5. CNN architecture for OSNR estimation.
Fig. 6.
Fig. 6. Evaluation results of CNN-based OSNR estimator trained with simulation data with frequency offset = 0 . (a) Using test dataset with frequency offset = 0 , and (b) 1 GHz.
Fig. 7.
Fig. 7. Evaluation results of CNN-based OSNR estimator trained with simulation data of the operational range expander of frequency offset. The operational range specifier = 0 (blue circles), 1.25 (red diamonds), and 2.5 GHz (green triangles).
Fig. 8.
Fig. 8. Evaluation results of CNN-based OSNR estimator trained with simulation data having the operational range expander of frequency offset. The operational range specifier = 0 (blue circles), 1.25 (red diamonds), and 2.5 GHz (green triangles). (a) Bias error, (b) enlarged graph of (a), and (c) standard deviation of CNN-estimated OSNR as a function of frequency offset of incoming signals.
Fig. 9.
Fig. 9. Standard deviation of measured frequency offset derived from the number of measurements N = 23 by using a DSP-based method.
Fig. 10.
Fig. 10. Evaluation results of CNN-based OSNR estimator trained with experimental data having the operational range expander of frequency offset. The operational range specifier = 0 (blue circles), 1.25 (red diamonds), and 2.5 GHz (green triangles). (a) Bias error, (b) enlarged graph of (a), and (c) standard deviation of CNN-estimated OSNR as a function of frequency offset of incoming signals.

Equations (4)

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

E out = T ( E in , ξ ) , ξ = g ( S , u ) , T ( E in , 0 ) = E in ,
HI = Re ( E H E LO * ) , HQ = Im ( E H E LO * ) , VI = Re ( E V E LO * ) , VQ = Im ( E V E LO * ) ,
T ( E in , ξ ) = ( ξ 11 ξ 12 ξ 21 ξ 22 ) E in .
ξ 12 = ξ 21 = 0 , ξ 11 = ξ 22 = g ( S , u ) , g ( S , u ) = exp ( j 2 π Sut ) , S = f max ,