Abstract

The reduction of system margin in open optical line systems (OLSs) requires the capability to predict the quality of transmission (QoT) within them. This quantity is given by the generalized signal-to-noise ratio (GSNR), including both the effects of amplified spontaneous emission (ASE) noise and nonlinear interference accumulation. Among these, estimating the ASE noise is the most challenging task due to the spectrally resolved working point of the erbium-doped fiber amplifiers (EDFAs), which depend on the spectral load, given the overall gain profile. An accurate GSNR estimation enables control of the power optimization and the possibility to automatically deploy lightpaths with a minimum margin in a reliable manner. We suppose an agnostic operation of the OLS, meaning that the EDFAs are operated as black boxes and rely only on telemetry data from the optical channel monitor at the end of the OLS. We acquire an experimental data set from an OLS made of 11 EDFAs and show that, without any knowledge of the system characteristics, an average extra margin of 2.28 dB is necessary to maintain a conservative threshold of QoT. Following this, we applied deep neural network machine-learning techniques, demonstrating a reduction in the needed margin average down to 0.15 dB.

© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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References

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2019 (3)

2018 (4)

2017 (4)

2015 (2)

D. J. Ives, P. Bayvel, and S. J. Savory, “Routing, modulation, spectrum and launch power assignment to maximize the traffic throughput of a nonlinear optical mesh network,” Photon. Netw. Commun. 29, 244–256 (2015).
[Crossref]

V. Curri, A. Carena, A. Arduino, G. Bosco, P. Poggiolini, A. Nespola, and F. Forghieri, “Design strategies and merit of system parameters for uniform uncompensated links supporting Nyquist-WDM transmission,” J. Lightwave Technol. 33, 3921–3932 (2015).
[Crossref]

2014 (1)

A. Nespola, S. Straullu, A. Carena, G. Bosco, R. Cigliutti, V. Curri, P. Poggiolini, M. Hirano, Y. Yamamoto, T. Sasaki, J. Bauwelinck, K. Verheyen, and F. Forghieri, “GN-model validation over seven fiber types in uncompensated PM-16QAM Nyquist-WDM links,” IEEE Photon. Technol. Lett. 26, 206–209 (2014).
[Crossref]

2013 (1)

2012 (2)

2010 (1)

A. Bononi, P. Serena, and N. Rossi, “Nonlinear signal–noise interactions in dispersion-managed links with various modulation formats,” Opt. Fiber Technol. 16, 73–85 (2010).
[Crossref]

2003 (1)

Amar, D.

M. Freire, S. Mansfeld, D. Amar, F. Gillet, A. Lavignotte, and C. Lepers, “Predicting optical power excursions in erbium doped fiber amplifiers using neural networks,” in Asia Communications and Photonics Conference (ACP) (IEEE, 2018), pp. 1–3.

Aono, Y.

W. Mo, Y.-K. Huang, S. Zhang, E. Ip, D. C. Kilper, Y. Aono, and T. Tajima, “ANN-based transfer learning for QoT prediction in real-time mixed line-rate systems,” in Optical Fiber Communication Conference and Exposition (OFC) (IEEE, 2018), pp. 1–3.

Arduino, A.

Auge, J. L.

M. Cantono, D. Pilori, A. Ferrari, C. Catanese, J. Thouras, J. L. Auge, and V. Curri, “On the interplay of nonlinear interference generation with stimulated Raman scattering for QoT estimation,” J. Lightwave Technol. 36, 3131–3141 (2018).
[Crossref]

G. Grammel, V. Curri, and J. L. Auge, “Physical simulation environment of the telecommunications infrastructure project (TIP),” in Optical Fiber Communication Conference and the National Fiber Optic Engineers Conference (2018).

Augé, J.-L.

Bandyopadhyay, S.

B. Taylor, G. Goldfarb, S. Bandyopadhyay, V. Curri, and H.-J. Schmidtke, “Towards a route planning tool for open optical networks in the telecom infrastructure project,” in Optical Fiber Communication Conference and the National Fiber Optic Engineers Conference (2018).

Barletta, L.

C. Rottondi, L. Barletta, A. Giusti, and M. Tornatore, “Machine-learning method for quality of transmission prediction of unestablished lightpaths,” J. Opt. Commun. Netw. 10, A286–A297 (2018).
[Crossref]

L. Barletta, A. Giusti, C. Rottondi, and M. Tornatore, “QoT estimation for unestablished lighpaths using machine learning,” in Optical Fiber Communication Conference (Optical Society of America, 2017), paper Th1J–1.

Bauwelinck, J.

A. Nespola, S. Straullu, A. Carena, G. Bosco, R. Cigliutti, V. Curri, P. Poggiolini, M. Hirano, Y. Yamamoto, T. Sasaki, J. Bauwelinck, K. Verheyen, and F. Forghieri, “GN-model validation over seven fiber types in uncompensated PM-16QAM Nyquist-WDM links,” IEEE Photon. Technol. Lett. 26, 206–209 (2014).
[Crossref]

Bayvel, P.

D. J. Elson, G. Saavedra, K. Shi, D. Semrau, L. Galdino, R. Killey, B. C. Thomsen, and P. Bayvel, “Investigation of bandwidth loading in optical fibre transmission using amplified spontaneous emission noise,” Opt. Express 25, 19529–19537 (2017).
[Crossref]

D. J. Ives, P. Bayvel, and S. J. Savory, “Routing, modulation, spectrum and launch power assignment to maximize the traffic throughput of a nonlinear optical mesh network,” Photon. Netw. Commun. 29, 244–256 (2015).
[Crossref]

Birk, M.

Boertjes, D. W.

Bolshtyansky, M.

Bononi, A.

A. Bononi, P. Serena, and N. Rossi, “Nonlinear signal–noise interactions in dispersion-managed links with various modulation formats,” Opt. Fiber Technol. 16, 73–85 (2010).
[Crossref]

Borraccini, G.

A. Ferrari, G. Borraccini, and V. Curri, “Observing the generalized SNR statistics induced by gain/loss uncertainties,” in European Conference on Optical Communication (ECOC) (IEEE, 2019).

Bosco, G.

V. Curri, A. Carena, A. Arduino, G. Bosco, P. Poggiolini, A. Nespola, and F. Forghieri, “Design strategies and merit of system parameters for uniform uncompensated links supporting Nyquist-WDM transmission,” J. Lightwave Technol. 33, 3921–3932 (2015).
[Crossref]

A. Nespola, S. Straullu, A. Carena, G. Bosco, R. Cigliutti, V. Curri, P. Poggiolini, M. Hirano, Y. Yamamoto, T. Sasaki, J. Bauwelinck, K. Verheyen, and F. Forghieri, “GN-model validation over seven fiber types in uncompensated PM-16QAM Nyquist-WDM links,” IEEE Photon. Technol. Lett. 26, 206–209 (2014).
[Crossref]

A. Carena, V. Curri, G. Bosco, P. Poggiolini, and F. Forghieri, “Modeling of the impact of nonlinear propagation effects in uncompensated optical coherent transmission links,” J. Lightwave Technol. 30, 1524–1539 (2012).
[Crossref]

P. Poggiolini, A. Carena, Y. Jiang, G. Bosco, V. Curri, and F. Forghieri, “Impact of low-OSNR operation on the performance of advanced coherent optical transmission systems,” in The European Conference on Optical Communication (ECOC) (IEEE, 2014), pp. 1–3.

D. Pilori, F. Forghieri, and G. Bosco, “Residual non-linear phase noise in probabilistically shaped 64-QAM optical links,” in Optical Fiber Communication Conference and the National Fiber Optic Engineers Conference (2018).

Cantono, M.

Carena, A.

V. Curri, A. Carena, A. Arduino, G. Bosco, P. Poggiolini, A. Nespola, and F. Forghieri, “Design strategies and merit of system parameters for uniform uncompensated links supporting Nyquist-WDM transmission,” J. Lightwave Technol. 33, 3921–3932 (2015).
[Crossref]

A. Nespola, S. Straullu, A. Carena, G. Bosco, R. Cigliutti, V. Curri, P. Poggiolini, M. Hirano, Y. Yamamoto, T. Sasaki, J. Bauwelinck, K. Verheyen, and F. Forghieri, “GN-model validation over seven fiber types in uncompensated PM-16QAM Nyquist-WDM links,” IEEE Photon. Technol. Lett. 26, 206–209 (2014).
[Crossref]

A. Carena, V. Curri, G. Bosco, P. Poggiolini, and F. Forghieri, “Modeling of the impact of nonlinear propagation effects in uncompensated optical coherent transmission links,” J. Lightwave Technol. 30, 1524–1539 (2012).
[Crossref]

P. Poggiolini, A. Carena, Y. Jiang, G. Bosco, V. Curri, and F. Forghieri, “Impact of low-OSNR operation on the performance of advanced coherent optical transmission systems,” in The European Conference on Optical Communication (ECOC) (IEEE, 2014), pp. 1–3.

Catanese, C.

Chamania, M.

J. Mata, I. De Miguel, R. J. Duran, N. Merayo, S. K. Singh, A. Jukan, and M. Chamania, “Artificial intelligence (AI) methods in optical networks: a comprehensive survey,” Opt. Switching Netw. 28, 43–57 (2018).
[Crossref]

Christodoulopoulos, K.

A. Mahajan, K. Christodoulopoulos, R. Martinez, S. Spadaro, and R. Munoz, “Machine learning assisted EFDA gain ripple modelling for accurate QoT estimation,” in European Conference on Optical Communication (ECOC) (IEEE, 2019).

Christodoulopoulos, K. K.

Cigliutti, R.

A. Nespola, S. Straullu, A. Carena, G. Bosco, R. Cigliutti, V. Curri, P. Poggiolini, M. Hirano, Y. Yamamoto, T. Sasaki, J. Bauwelinck, K. Verheyen, and F. Forghieri, “GN-model validation over seven fiber types in uncompensated PM-16QAM Nyquist-WDM links,” IEEE Photon. Technol. Lett. 26, 206–209 (2014).
[Crossref]

Côté, D.

Curri, V.

M. Cantono, D. Pilori, A. Ferrari, C. Catanese, J. Thouras, J. L. Auge, and V. Curri, “On the interplay of nonlinear interference generation with stimulated Raman scattering for QoT estimation,” J. Lightwave Technol. 36, 3131–3141 (2018).
[Crossref]

M. Filer, M. Cantono, A. Ferrari, G. Grammel, G. Galimberti, and V. Curri, “Multi-vendor experimental validation of an open source QoT estimator for optical networks,” J. Lightwave Technol. 36, 3073–3082 (2018).
[Crossref]

V. Curri, M. Cantono, and R. Gaudino, “Elastic all-optical networks: a new paradigm enabled by the physical layer. How to optimize network performances?” J. Lightwave Technol. 35, 1211–1221 (2017).
[Crossref]

V. Curri, A. Carena, A. Arduino, G. Bosco, P. Poggiolini, A. Nespola, and F. Forghieri, “Design strategies and merit of system parameters for uniform uncompensated links supporting Nyquist-WDM transmission,” J. Lightwave Technol. 33, 3921–3932 (2015).
[Crossref]

A. Nespola, S. Straullu, A. Carena, G. Bosco, R. Cigliutti, V. Curri, P. Poggiolini, M. Hirano, Y. Yamamoto, T. Sasaki, J. Bauwelinck, K. Verheyen, and F. Forghieri, “GN-model validation over seven fiber types in uncompensated PM-16QAM Nyquist-WDM links,” IEEE Photon. Technol. Lett. 26, 206–209 (2014).
[Crossref]

A. Carena, V. Curri, G. Bosco, P. Poggiolini, and F. Forghieri, “Modeling of the impact of nonlinear propagation effects in uncompensated optical coherent transmission links,” J. Lightwave Technol. 30, 1524–1539 (2012).
[Crossref]

P. Poggiolini, A. Carena, Y. Jiang, G. Bosco, V. Curri, and F. Forghieri, “Impact of low-OSNR operation on the performance of advanced coherent optical transmission systems,” in The European Conference on Optical Communication (ECOC) (IEEE, 2014), pp. 1–3.

A. Ferrari, G. Borraccini, and V. Curri, “Observing the generalized SNR statistics induced by gain/loss uncertainties,” in European Conference on Optical Communication (ECOC) (IEEE, 2019).

G. Grammel, V. Curri, and J. L. Auge, “Physical simulation environment of the telecommunications infrastructure project (TIP),” in Optical Fiber Communication Conference and the National Fiber Optic Engineers Conference (2018).

B. Taylor, G. Goldfarb, S. Bandyopadhyay, V. Curri, and H.-J. Schmidtke, “Towards a route planning tool for open optical networks in the telecom infrastructure project,” in Optical Fiber Communication Conference and the National Fiber Optic Engineers Conference (2018).

Dar, R.

De Miguel, I.

J. Mata, I. De Miguel, R. J. Duran, N. Merayo, S. K. Singh, A. Jukan, and M. Chamania, “Artificial intelligence (AI) methods in optical networks: a comprehensive survey,” Opt. Switching Netw. 28, 43–57 (2018).
[Crossref]

Diniz, J. C.

Duran, R. J.

J. Mata, I. De Miguel, R. J. Duran, N. Merayo, S. K. Singh, A. Jukan, and M. Chamania, “Artificial intelligence (AI) methods in optical networks: a comprehensive survey,” Opt. Switching Netw. 28, 43–57 (2018).
[Crossref]

Elson, D. J.

Essiambre, R.-J.

R.-J. Essiambre and R. W. Tkach, “Capacity trends and limits of optical communication networks,” Proc. IEEE 100, 1035–1055 (2012).
[Crossref]

Feder, M.

Ferrari, A.

Filer, M.

Forghieri, F.

V. Curri, A. Carena, A. Arduino, G. Bosco, P. Poggiolini, A. Nespola, and F. Forghieri, “Design strategies and merit of system parameters for uniform uncompensated links supporting Nyquist-WDM transmission,” J. Lightwave Technol. 33, 3921–3932 (2015).
[Crossref]

A. Nespola, S. Straullu, A. Carena, G. Bosco, R. Cigliutti, V. Curri, P. Poggiolini, M. Hirano, Y. Yamamoto, T. Sasaki, J. Bauwelinck, K. Verheyen, and F. Forghieri, “GN-model validation over seven fiber types in uncompensated PM-16QAM Nyquist-WDM links,” IEEE Photon. Technol. Lett. 26, 206–209 (2014).
[Crossref]

A. Carena, V. Curri, G. Bosco, P. Poggiolini, and F. Forghieri, “Modeling of the impact of nonlinear propagation effects in uncompensated optical coherent transmission links,” J. Lightwave Technol. 30, 1524–1539 (2012).
[Crossref]

P. Poggiolini, A. Carena, Y. Jiang, G. Bosco, V. Curri, and F. Forghieri, “Impact of low-OSNR operation on the performance of advanced coherent optical transmission systems,” in The European Conference on Optical Communication (ECOC) (IEEE, 2014), pp. 1–3.

D. Pilori, F. Forghieri, and G. Bosco, “Residual non-linear phase noise in probabilistically shaped 64-QAM optical links,” in Optical Fiber Communication Conference and the National Fiber Optic Engineers Conference (2018).

Freire, M.

M. Freire, S. Mansfeld, D. Amar, F. Gillet, A. Lavignotte, and C. Lepers, “Predicting optical power excursions in erbium doped fiber amplifiers using neural networks,” in Asia Communications and Photonics Conference (ACP) (IEEE, 2018), pp. 1–3.

Galdino, L.

Galimberti, G.

Gaudino, R.

Gillet, F.

M. Freire, S. Mansfeld, D. Amar, F. Gillet, A. Lavignotte, and C. Lepers, “Predicting optical power excursions in erbium doped fiber amplifiers using neural networks,” in Asia Communications and Photonics Conference (ACP) (IEEE, 2018), pp. 1–3.

Giusti, A.

C. Rottondi, L. Barletta, A. Giusti, and M. Tornatore, “Machine-learning method for quality of transmission prediction of unestablished lightpaths,” J. Opt. Commun. Netw. 10, A286–A297 (2018).
[Crossref]

L. Barletta, A. Giusti, C. Rottondi, and M. Tornatore, “QoT estimation for unestablished lighpaths using machine learning,” in Optical Fiber Communication Conference (Optical Society of America, 2017), paper Th1J–1.

Goldfarb, G.

B. Taylor, G. Goldfarb, S. Bandyopadhyay, V. Curri, and H.-J. Schmidtke, “Towards a route planning tool for open optical networks in the telecom infrastructure project,” in Optical Fiber Communication Conference and the National Fiber Optic Engineers Conference (2018).

Grammel, G.

M. Filer, M. Cantono, A. Ferrari, G. Grammel, G. Galimberti, and V. Curri, “Multi-vendor experimental validation of an open source QoT estimator for optical networks,” J. Lightwave Technol. 36, 3073–3082 (2018).
[Crossref]

G. Grammel, V. Curri, and J. L. Auge, “Physical simulation environment of the telecommunications infrastructure project (TIP),” in Optical Fiber Communication Conference and the National Fiber Optic Engineers Conference (2018).

Gutterman, C. L.

S. Zhu, C. L. Gutterman, W. Mo, Y. Li, G. Zussman, and D. C. Kilper, “Machine learning based prediction of erbium-doped fiber WDM line amplifier gain spectra,” in European Conference on Optical Communication (ECOC) (IEEE, 2018), pp. 1–3.

C. L. Gutterman, W. Mo, S. Zhu, Y. Li, D. C. Kilper, and G. Zussman, “Neural network based wavelength assignment in optical switching,” in Proceedings of the Workshop on Big Data Analytics and Machine Learning for Data Communication Networks (ACM, 2017), pp. 37–42.

Hirano, M.

A. Nespola, S. Straullu, A. Carena, G. Bosco, R. Cigliutti, V. Curri, P. Poggiolini, M. Hirano, Y. Yamamoto, T. Sasaki, J. Bauwelinck, K. Verheyen, and F. Forghieri, “GN-model validation over seven fiber types in uncompensated PM-16QAM Nyquist-WDM links,” IEEE Photon. Technol. Lett. 26, 206–209 (2014).
[Crossref]

Huang, Y.-K.

W. Mo, Y.-K. Huang, S. Zhang, E. Ip, D. C. Kilper, Y. Aono, and T. Tajima, “ANN-based transfer learning for QoT prediction in real-time mixed line-rate systems,” in Optical Fiber Communication Conference and Exposition (OFC) (IEEE, 2018), pp. 1–3.

Ionescu, M.

M. Ionescu, “Machine learning for ultrawide bandwidth amplifier configuration,” in 21st International Conference on Transparent Optical Networks (ICTON) (IEEE, 2019).

Ip, E.

W. Mo, Y.-K. Huang, S. Zhang, E. Ip, D. C. Kilper, Y. Aono, and T. Tajima, “ANN-based transfer learning for QoT prediction in real-time mixed line-rate systems,” in Optical Fiber Communication Conference and Exposition (OFC) (IEEE, 2018), pp. 1–3.

Ives, D. J.

D. J. Ives, P. Bayvel, and S. J. Savory, “Routing, modulation, spectrum and launch power assignment to maximize the traffic throughput of a nonlinear optical mesh network,” Photon. Netw. Commun. 29, 244–256 (2015).
[Crossref]

Jiang, Y.

P. Poggiolini, A. Carena, Y. Jiang, G. Bosco, V. Curri, and F. Forghieri, “Impact of low-OSNR operation on the performance of advanced coherent optical transmission systems,” in The European Conference on Optical Communication (ECOC) (IEEE, 2014), pp. 1–3.

Jones, R.

Jukan, A.

J. Mata, I. De Miguel, R. J. Duran, N. Merayo, S. K. Singh, A. Jukan, and M. Chamania, “Artificial intelligence (AI) methods in optical networks: a comprehensive survey,” Opt. Switching Netw. 28, 43–57 (2018).
[Crossref]

Khan, F. N.

F. N. Khan, C. Lu, and A. P. T. Lau, “Optical performance monitoring in fiber-optic networks enabled by machine learning techniques,” in Optical Fiber Communication Conference and Exposition (OFC) (IEEE, 2018), pp. 1–3.

Killey, R.

Kilper, D. C.

W. Mo, Y.-K. Huang, S. Zhang, E. Ip, D. C. Kilper, Y. Aono, and T. Tajima, “ANN-based transfer learning for QoT prediction in real-time mixed line-rate systems,” in Optical Fiber Communication Conference and Exposition (OFC) (IEEE, 2018), pp. 1–3.

S. Zhu, C. L. Gutterman, W. Mo, Y. Li, G. Zussman, and D. C. Kilper, “Machine learning based prediction of erbium-doped fiber WDM line amplifier gain spectra,” in European Conference on Optical Communication (ECOC) (IEEE, 2018), pp. 1–3.

C. L. Gutterman, W. Mo, S. Zhu, Y. Li, D. C. Kilper, and G. Zussman, “Neural network based wavelength assignment in optical switching,” in Proceedings of the Workshop on Big Data Analytics and Machine Learning for Data Communication Networks (ACM, 2017), pp. 37–42.

Lau, A. P. T.

F. N. Khan, C. Lu, and A. P. T. Lau, “Optical performance monitoring in fiber-optic networks enabled by machine learning techniques,” in Optical Fiber Communication Conference and Exposition (OFC) (IEEE, 2018), pp. 1–3.

Lavignotte, A.

M. Freire, S. Mansfeld, D. Amar, F. Gillet, A. Lavignotte, and C. Lepers, “Predicting optical power excursions in erbium doped fiber amplifiers using neural networks,” in Asia Communications and Photonics Conference (ACP) (IEEE, 2018), pp. 1–3.

Lepers, C.

M. Freire, S. Mansfeld, D. Amar, F. Gillet, A. Lavignotte, and C. Lepers, “Predicting optical power excursions in erbium doped fiber amplifiers using neural networks,” in Asia Communications and Photonics Conference (ACP) (IEEE, 2018), pp. 1–3.

Li, Y.

S. Zhu, C. L. Gutterman, W. Mo, Y. Li, G. Zussman, and D. C. Kilper, “Machine learning based prediction of erbium-doped fiber WDM line amplifier gain spectra,” in European Conference on Optical Communication (ECOC) (IEEE, 2018), pp. 1–3.

C. L. Gutterman, W. Mo, S. Zhu, Y. Li, D. C. Kilper, and G. Zussman, “Neural network based wavelength assignment in optical switching,” in Proceedings of the Workshop on Big Data Analytics and Machine Learning for Data Communication Networks (ACM, 2017), pp. 37–42.

Lu, C.

F. N. Khan, C. Lu, and A. P. T. Lau, “Optical performance monitoring in fiber-optic networks enabled by machine learning techniques,” in Optical Fiber Communication Conference and Exposition (OFC) (IEEE, 2018), pp. 1–3.

Mahajan, A.

A. Mahajan, K. Christodoulopoulos, R. Martinez, S. Spadaro, and R. Munoz, “Machine learning assisted EFDA gain ripple modelling for accurate QoT estimation,” in European Conference on Optical Communication (ECOC) (IEEE, 2019).

Mansfeld, S.

M. Freire, S. Mansfeld, D. Amar, F. Gillet, A. Lavignotte, and C. Lepers, “Predicting optical power excursions in erbium doped fiber amplifiers using neural networks,” in Asia Communications and Photonics Conference (ACP) (IEEE, 2018), pp. 1–3.

Martinez, R.

A. Mahajan, K. Christodoulopoulos, R. Martinez, S. Spadaro, and R. Munoz, “Machine learning assisted EFDA gain ripple modelling for accurate QoT estimation,” in European Conference on Optical Communication (ECOC) (IEEE, 2019).

Mata, J.

J. Mata, I. De Miguel, R. J. Duran, N. Merayo, S. K. Singh, A. Jukan, and M. Chamania, “Artificial intelligence (AI) methods in optical networks: a comprehensive survey,” Opt. Switching Netw. 28, 43–57 (2018).
[Crossref]

Mecozzi, A.

Merayo, N.

J. Mata, I. De Miguel, R. J. Duran, N. Merayo, S. K. Singh, A. Jukan, and M. Chamania, “Artificial intelligence (AI) methods in optical networks: a comprehensive survey,” Opt. Switching Netw. 28, 43–57 (2018).
[Crossref]

Mo, W.

S. Zhu, C. L. Gutterman, W. Mo, Y. Li, G. Zussman, and D. C. Kilper, “Machine learning based prediction of erbium-doped fiber WDM line amplifier gain spectra,” in European Conference on Optical Communication (ECOC) (IEEE, 2018), pp. 1–3.

W. Mo, Y.-K. Huang, S. Zhang, E. Ip, D. C. Kilper, Y. Aono, and T. Tajima, “ANN-based transfer learning for QoT prediction in real-time mixed line-rate systems,” in Optical Fiber Communication Conference and Exposition (OFC) (IEEE, 2018), pp. 1–3.

C. L. Gutterman, W. Mo, S. Zhu, Y. Li, D. C. Kilper, and G. Zussman, “Neural network based wavelength assignment in optical switching,” in Proceedings of the Workshop on Big Data Analytics and Machine Learning for Data Communication Networks (ACM, 2017), pp. 37–42.

Munoz, R.

A. Mahajan, K. Christodoulopoulos, R. Martinez, S. Spadaro, and R. Munoz, “Machine learning assisted EFDA gain ripple modelling for accurate QoT estimation,” in European Conference on Optical Communication (ECOC) (IEEE, 2019).

Nespola, A.

V. Curri, A. Carena, A. Arduino, G. Bosco, P. Poggiolini, A. Nespola, and F. Forghieri, “Design strategies and merit of system parameters for uniform uncompensated links supporting Nyquist-WDM transmission,” J. Lightwave Technol. 33, 3921–3932 (2015).
[Crossref]

A. Nespola, S. Straullu, A. Carena, G. Bosco, R. Cigliutti, V. Curri, P. Poggiolini, M. Hirano, Y. Yamamoto, T. Sasaki, J. Bauwelinck, K. Verheyen, and F. Forghieri, “GN-model validation over seven fiber types in uncompensated PM-16QAM Nyquist-WDM links,” IEEE Photon. Technol. Lett. 26, 206–209 (2014).
[Crossref]

Pastorelli, R.

R. Pastorelli, “Network optimization strategies and control plane impacts,” in Optical Fiber Communication Conference (OSA, 2015).

Piels, M.

Pilori, D.

M. Cantono, D. Pilori, A. Ferrari, C. Catanese, J. Thouras, J. L. Auge, and V. Curri, “On the interplay of nonlinear interference generation with stimulated Raman scattering for QoT estimation,” J. Lightwave Technol. 36, 3131–3141 (2018).
[Crossref]

D. Pilori, F. Forghieri, and G. Bosco, “Residual non-linear phase noise in probabilistically shaped 64-QAM optical links,” in Optical Fiber Communication Conference and the National Fiber Optic Engineers Conference (2018).

Poggiolini, P.

V. Curri, A. Carena, A. Arduino, G. Bosco, P. Poggiolini, A. Nespola, and F. Forghieri, “Design strategies and merit of system parameters for uniform uncompensated links supporting Nyquist-WDM transmission,” J. Lightwave Technol. 33, 3921–3932 (2015).
[Crossref]

A. Nespola, S. Straullu, A. Carena, G. Bosco, R. Cigliutti, V. Curri, P. Poggiolini, M. Hirano, Y. Yamamoto, T. Sasaki, J. Bauwelinck, K. Verheyen, and F. Forghieri, “GN-model validation over seven fiber types in uncompensated PM-16QAM Nyquist-WDM links,” IEEE Photon. Technol. Lett. 26, 206–209 (2014).
[Crossref]

A. Carena, V. Curri, G. Bosco, P. Poggiolini, and F. Forghieri, “Modeling of the impact of nonlinear propagation effects in uncompensated optical coherent transmission links,” J. Lightwave Technol. 30, 1524–1539 (2012).
[Crossref]

P. Poggiolini, A. Carena, Y. Jiang, G. Bosco, V. Curri, and F. Forghieri, “Impact of low-OSNR operation on the performance of advanced coherent optical transmission systems,” in The European Conference on Optical Communication (ECOC) (IEEE, 2014), pp. 1–3.

Pointurier, Y.

Reimer, M.

Rossi, N.

A. Bononi, P. Serena, and N. Rossi, “Nonlinear signal–noise interactions in dispersion-managed links with various modulation formats,” Opt. Fiber Technol. 16, 73–85 (2010).
[Crossref]

Rottondi, C.

C. Rottondi, L. Barletta, A. Giusti, and M. Tornatore, “Machine-learning method for quality of transmission prediction of unestablished lightpaths,” J. Opt. Commun. Netw. 10, A286–A297 (2018).
[Crossref]

L. Barletta, A. Giusti, C. Rottondi, and M. Tornatore, “QoT estimation for unestablished lighpaths using machine learning,” in Optical Fiber Communication Conference (Optical Society of America, 2017), paper Th1J–1.

Saavedra, G.

Sartzetakis, I.

Sasaki, T.

A. Nespola, S. Straullu, A. Carena, G. Bosco, R. Cigliutti, V. Curri, P. Poggiolini, M. Hirano, Y. Yamamoto, T. Sasaki, J. Bauwelinck, K. Verheyen, and F. Forghieri, “GN-model validation over seven fiber types in uncompensated PM-16QAM Nyquist-WDM links,” IEEE Photon. Technol. Lett. 26, 206–209 (2014).
[Crossref]

Savory, S. J.

D. J. Ives, P. Bayvel, and S. J. Savory, “Routing, modulation, spectrum and launch power assignment to maximize the traffic throughput of a nonlinear optical mesh network,” Photon. Netw. Commun. 29, 244–256 (2015).
[Crossref]

Schmidtke, H.-J.

B. Taylor, G. Goldfarb, S. Bandyopadhyay, V. Curri, and H.-J. Schmidtke, “Towards a route planning tool for open optical networks in the telecom infrastructure project,” in Optical Fiber Communication Conference and the National Fiber Optic Engineers Conference (2018).

Semrau, D.

Serena, P.

A. Bononi, P. Serena, and N. Rossi, “Nonlinear signal–noise interactions in dispersion-managed links with various modulation formats,” Opt. Fiber Technol. 16, 73–85 (2010).
[Crossref]

Shi, K.

Shtaif, M.

Singh, S. K.

J. Mata, I. De Miguel, R. J. Duran, N. Merayo, S. K. Singh, A. Jukan, and M. Chamania, “Artificial intelligence (AI) methods in optical networks: a comprehensive survey,” Opt. Switching Netw. 28, 43–57 (2018).
[Crossref]

Spadaro, S.

A. Mahajan, K. Christodoulopoulos, R. Martinez, S. Spadaro, and R. Munoz, “Machine learning assisted EFDA gain ripple modelling for accurate QoT estimation,” in European Conference on Optical Communication (ECOC) (IEEE, 2019).

Straullu, S.

A. Nespola, S. Straullu, A. Carena, G. Bosco, R. Cigliutti, V. Curri, P. Poggiolini, M. Hirano, Y. Yamamoto, T. Sasaki, J. Bauwelinck, K. Verheyen, and F. Forghieri, “GN-model validation over seven fiber types in uncompensated PM-16QAM Nyquist-WDM links,” IEEE Photon. Technol. Lett. 26, 206–209 (2014).
[Crossref]

Tajima, T.

W. Mo, Y.-K. Huang, S. Zhang, E. Ip, D. C. Kilper, Y. Aono, and T. Tajima, “ANN-based transfer learning for QoT prediction in real-time mixed line-rate systems,” in Optical Fiber Communication Conference and Exposition (OFC) (IEEE, 2018), pp. 1–3.

Taylor, B.

B. Taylor, G. Goldfarb, S. Bandyopadhyay, V. Curri, and H.-J. Schmidtke, “Towards a route planning tool for open optical networks in the telecom infrastructure project,” in Optical Fiber Communication Conference and the National Fiber Optic Engineers Conference (2018).

Thomsen, B. C.

Thouras, J.

Thrane, J.

Tkach, R. W.

R.-J. Essiambre and R. W. Tkach, “Capacity trends and limits of optical communication networks,” Proc. IEEE 100, 1035–1055 (2012).
[Crossref]

Tornatore, M.

C. Rottondi, L. Barletta, A. Giusti, and M. Tornatore, “Machine-learning method for quality of transmission prediction of unestablished lightpaths,” J. Opt. Commun. Netw. 10, A286–A297 (2018).
[Crossref]

L. Barletta, A. Giusti, C. Rottondi, and M. Tornatore, “QoT estimation for unestablished lighpaths using machine learning,” in Optical Fiber Communication Conference (Optical Society of America, 2017), paper Th1J–1.

Varvarigos, E.

Varvarigos, E. M.

Verheyen, K.

A. Nespola, S. Straullu, A. Carena, G. Bosco, R. Cigliutti, V. Curri, P. Poggiolini, M. Hirano, Y. Yamamoto, T. Sasaki, J. Bauwelinck, K. Verheyen, and F. Forghieri, “GN-model validation over seven fiber types in uncompensated PM-16QAM Nyquist-WDM links,” IEEE Photon. Technol. Lett. 26, 206–209 (2014).
[Crossref]

Wass, J.

Yamamoto, Y.

A. Nespola, S. Straullu, A. Carena, G. Bosco, R. Cigliutti, V. Curri, P. Poggiolini, M. Hirano, Y. Yamamoto, T. Sasaki, J. Bauwelinck, K. Verheyen, and F. Forghieri, “GN-model validation over seven fiber types in uncompensated PM-16QAM Nyquist-WDM links,” IEEE Photon. Technol. Lett. 26, 206–209 (2014).
[Crossref]

Zhang, S.

W. Mo, Y.-K. Huang, S. Zhang, E. Ip, D. C. Kilper, Y. Aono, and T. Tajima, “ANN-based transfer learning for QoT prediction in real-time mixed line-rate systems,” in Optical Fiber Communication Conference and Exposition (OFC) (IEEE, 2018), pp. 1–3.

Zhu, S.

S. Zhu, C. L. Gutterman, W. Mo, Y. Li, G. Zussman, and D. C. Kilper, “Machine learning based prediction of erbium-doped fiber WDM line amplifier gain spectra,” in European Conference on Optical Communication (ECOC) (IEEE, 2018), pp. 1–3.

C. L. Gutterman, W. Mo, S. Zhu, Y. Li, D. C. Kilper, and G. Zussman, “Neural network based wavelength assignment in optical switching,” in Proceedings of the Workshop on Big Data Analytics and Machine Learning for Data Communication Networks (ACM, 2017), pp. 37–42.

Zibar, D.

Zussman, G.

C. L. Gutterman, W. Mo, S. Zhu, Y. Li, D. C. Kilper, and G. Zussman, “Neural network based wavelength assignment in optical switching,” in Proceedings of the Workshop on Big Data Analytics and Machine Learning for Data Communication Networks (ACM, 2017), pp. 37–42.

S. Zhu, C. L. Gutterman, W. Mo, Y. Li, G. Zussman, and D. C. Kilper, “Machine learning based prediction of erbium-doped fiber WDM line amplifier gain spectra,” in European Conference on Optical Communication (ECOC) (IEEE, 2018), pp. 1–3.

IEEE Photon. Technol. Lett. (1)

A. Nespola, S. Straullu, A. Carena, G. Bosco, R. Cigliutti, V. Curri, P. Poggiolini, M. Hirano, Y. Yamamoto, T. Sasaki, J. Bauwelinck, K. Verheyen, and F. Forghieri, “GN-model validation over seven fiber types in uncompensated PM-16QAM Nyquist-WDM links,” IEEE Photon. Technol. Lett. 26, 206–209 (2014).
[Crossref]

J. Lightwave Technol. (7)

M. Bolshtyansky, “Spectral hole burning in erbium-doped fiber amplifiers,” J. Lightwave Technol. 21, 1032–1038 (2003).
[Crossref]

A. Carena, V. Curri, G. Bosco, P. Poggiolini, and F. Forghieri, “Modeling of the impact of nonlinear propagation effects in uncompensated optical coherent transmission links,” J. Lightwave Technol. 30, 1524–1539 (2012).
[Crossref]

V. Curri, A. Carena, A. Arduino, G. Bosco, P. Poggiolini, A. Nespola, and F. Forghieri, “Design strategies and merit of system parameters for uniform uncompensated links supporting Nyquist-WDM transmission,” J. Lightwave Technol. 33, 3921–3932 (2015).
[Crossref]

J. Thrane, J. Wass, M. Piels, J. C. Diniz, R. Jones, and D. Zibar, “Machine learning techniques for optical performance monitoring from directly detected PDM-QAM signals,” J. Lightwave Technol. 35, 868–875 (2017).
[Crossref]

V. Curri, M. Cantono, and R. Gaudino, “Elastic all-optical networks: a new paradigm enabled by the physical layer. How to optimize network performances?” J. Lightwave Technol. 35, 1211–1221 (2017).
[Crossref]

M. Filer, M. Cantono, A. Ferrari, G. Grammel, G. Galimberti, and V. Curri, “Multi-vendor experimental validation of an open source QoT estimator for optical networks,” J. Lightwave Technol. 36, 3073–3082 (2018).
[Crossref]

M. Cantono, D. Pilori, A. Ferrari, C. Catanese, J. Thouras, J. L. Auge, and V. Curri, “On the interplay of nonlinear interference generation with stimulated Raman scattering for QoT estimation,” J. Lightwave Technol. 36, 3131–3141 (2018).
[Crossref]

J. Opt. Commun. Netw. (5)

Opt. Express (2)

Opt. Fiber Technol. (1)

A. Bononi, P. Serena, and N. Rossi, “Nonlinear signal–noise interactions in dispersion-managed links with various modulation formats,” Opt. Fiber Technol. 16, 73–85 (2010).
[Crossref]

Opt. Switching Netw. (1)

J. Mata, I. De Miguel, R. J. Duran, N. Merayo, S. K. Singh, A. Jukan, and M. Chamania, “Artificial intelligence (AI) methods in optical networks: a comprehensive survey,” Opt. Switching Netw. 28, 43–57 (2018).
[Crossref]

Photon. Netw. Commun. (1)

D. J. Ives, P. Bayvel, and S. J. Savory, “Routing, modulation, spectrum and launch power assignment to maximize the traffic throughput of a nonlinear optical mesh network,” Photon. Netw. Commun. 29, 244–256 (2015).
[Crossref]

Proc. IEEE (1)

R.-J. Essiambre and R. W. Tkach, “Capacity trends and limits of optical communication networks,” Proc. IEEE 100, 1035–1055 (2012).
[Crossref]

Other (18)

W. Mo, Y.-K. Huang, S. Zhang, E. Ip, D. C. Kilper, Y. Aono, and T. Tajima, “ANN-based transfer learning for QoT prediction in real-time mixed line-rate systems,” in Optical Fiber Communication Conference and Exposition (OFC) (IEEE, 2018), pp. 1–3.

https://www.cisco.com/c/en/us/products/collateral/optical-networking/ons-15454-series-multiservice-transport-platforms/data_sheet_c78-658542.html .

“Cisco Visual Networking Index: Forecast and Trends, 2017–2022,” Cisco White Paper (2017), https://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/white-paper-c11-741490.html .

R. Pastorelli, “Network optimization strategies and control plane impacts,” in Optical Fiber Communication Conference (OSA, 2015).

D. Pilori, F. Forghieri, and G. Bosco, “Residual non-linear phase noise in probabilistically shaped 64-QAM optical links,” in Optical Fiber Communication Conference and the National Fiber Optic Engineers Conference (2018).

S. Zhu, C. L. Gutterman, W. Mo, Y. Li, G. Zussman, and D. C. Kilper, “Machine learning based prediction of erbium-doped fiber WDM line amplifier gain spectra,” in European Conference on Optical Communication (ECOC) (IEEE, 2018), pp. 1–3.

C. L. Gutterman, W. Mo, S. Zhu, Y. Li, D. C. Kilper, and G. Zussman, “Neural network based wavelength assignment in optical switching,” in Proceedings of the Workshop on Big Data Analytics and Machine Learning for Data Communication Networks (ACM, 2017), pp. 37–42.

A. Mahajan, K. Christodoulopoulos, R. Martinez, S. Spadaro, and R. Munoz, “Machine learning assisted EFDA gain ripple modelling for accurate QoT estimation,” in European Conference on Optical Communication (ECOC) (IEEE, 2019).

M. Ionescu, “Machine learning for ultrawide bandwidth amplifier configuration,” in 21st International Conference on Transparent Optical Networks (ICTON) (IEEE, 2019).

https://www.tensorflow.org/ .

https://www.itu.int/rec/T-REC-G.694.1/en .

P. Poggiolini, A. Carena, Y. Jiang, G. Bosco, V. Curri, and F. Forghieri, “Impact of low-OSNR operation on the performance of advanced coherent optical transmission systems,” in The European Conference on Optical Communication (ECOC) (IEEE, 2014), pp. 1–3.

A. Ferrari, G. Borraccini, and V. Curri, “Observing the generalized SNR statistics induced by gain/loss uncertainties,” in European Conference on Optical Communication (ECOC) (IEEE, 2019).

B. Taylor, G. Goldfarb, S. Bandyopadhyay, V. Curri, and H.-J. Schmidtke, “Towards a route planning tool for open optical networks in the telecom infrastructure project,” in Optical Fiber Communication Conference and the National Fiber Optic Engineers Conference (2018).

G. Grammel, V. Curri, and J. L. Auge, “Physical simulation environment of the telecommunications infrastructure project (TIP),” in Optical Fiber Communication Conference and the National Fiber Optic Engineers Conference (2018).

M. Freire, S. Mansfeld, D. Amar, F. Gillet, A. Lavignotte, and C. Lepers, “Predicting optical power excursions in erbium doped fiber amplifiers using neural networks,” in Asia Communications and Photonics Conference (ACP) (IEEE, 2018), pp. 1–3.

F. N. Khan, C. Lu, and A. P. T. Lau, “Optical performance monitoring in fiber-optic networks enabled by machine learning techniques,” in Optical Fiber Communication Conference and Exposition (OFC) (IEEE, 2018), pp. 1–3.

L. Barletta, A. Giusti, C. Rottondi, and M. Tornatore, “QoT estimation for unestablished lighpaths using machine learning,” in Optical Fiber Communication Conference (Optical Society of America, 2017), paper Th1J–1.

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Figures (12)

Fig. 1.
Fig. 1. Schematic description of an optical network as a topology of ROADM nodes connected by OLSs. The inset shows a general setup for an OLS that in this case is supposed to be open.
Fig. 2.
Fig. 2. Abstraction of an optical network as a topology graph weighted by the generalized SNR degradation for optical line systems, $ {{\rm GSNR}_i}(f)$ .
Fig. 3.
Fig. 3. General scheme for a QoT-E module predicting the $ {\rm GSNR}(f)$ . The three available data sets are shown: (1) static data from device characterization, (2) data from current-state telemetry, and (3) stored data from historical telemetry that feeds a ML module.
Fig. 4.
Fig. 4. Experimental setup: Here, the OLS under investigation is composed of an initial booster amplifier and a cascade of 11 spans, each containing a VOA and an EDFA. We show the input and output spectral power measurements obtained using an optical spectrum analyzer in blue and red, respectively.
Fig. 5.
Fig. 5. Overall OSNR measurements in the frequency domain. The blue dots are the mean values over the entire sample for each channel; the error bars are equal to the standard deviations. In red and green the maximum and the minimum for each channel are outlined, respectively. The dashed red line indicates the overall OSNR minimum of 28.1 dB.
Fig. 6.
Fig. 6. Mean values of four channel OSNRs are plotted with respect to the configurations for an increasing $ {N_\textit{on}} $ . In the legend, we report the central frequency of the channels considered. The colored lines and shaded areas are qualitative visual expressions of the trend of measured data.
Fig. 7.
Fig. 7. Standard deviation values of the same configurations plotted in Fig. 6. As expected, the channel centered at 195.25 THz maintains the highest variance out of all of the configurations. The colored lines and shaded areas are qualitative visual expressions of the trend of measured data.
Fig. 8.
Fig. 8. Qualitative visualization of the OSNR fluctuations that arise from turning on a new channel, for both the ASE noise (shaded lines) and the power of the on channels (dots). Here, the $ {N_\textit{on}} = 1 $ case is given in red, and the $ {N_\textit{on}} = 2 $ case is given in blue. In this figure, all quantities are normalized in order to have a unitary mean value.
Fig. 9.
Fig. 9. Standard deviation trend over all of the channels, highlighting an increase as the OSNR approaches the frequency where the peak of the spectral hole burning occurs, given by the dashed red line in the figure.
Fig. 10.
Fig. 10. RMS error for the worst-case scenario channel, with an increasing number of channels in the on state within the configuration, obtained considering the respective minimum measured OSNR value used as a margin threshold.
Fig. 11.
Fig. 11. Comparison of the OSNR distributions of the DNN guesses and the measured values, respectively.
Fig. 12.
Fig. 12. Comparison of the OSNR averages of the DNN guesses and the measured values, respectively, presented in terms of the number of channels, $ {N_\textit{on}} $ . With the error bars we indicate the RMS error.

Equations (5)

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

G S N R = P R x P A S E + P N L I = ( O S N R 1 + S N R N L 1 ) 1 ,
G S N R = ( i = 1 N 1 G S N R i ) 1 .
G S N R A F 1 ( f ) = G S N R A C 1 ( f ) + G S N R C E 1 ( f ) ) + G S N R E F 1 ( f ) .
O S N R ( f ) = P T x i = o N G i ( f ) L i ( f ) i = o N h f B n N F i [ G i ( f ) 1 ] k = i + 1 N L k ( f ) G k ( f ) ,
R M S = i = 0 D ( O S N R i r O S N R i p ) 2 D ,