Abstract

We demonstrate a technique for performance monitoring of quadrature phase-shift keying data channels by simultaneously identifying optical signal-to-noise ratio (OSNR), chromatic dispersion (CD), and polarization-mode dispersion (PMD) using neural networks trained with parameters derived from asynchronous constellation diagrams. A correlation coefficient of 0.987 is reported for a set of testing data from a 40 Gbps return-to-zero, quadrature phase-shift keying (RZ-QPSK) system. The root-mean-square (RMS) errors are 0.77 dB for OSNR, 18.71 ps/nm for CD, and 1.17 ps for DGD.

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    [CrossRef]
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    [CrossRef]
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    [CrossRef]
  5. S. D. Dods and T. B. Anderson, “Optical Performance Monitoring Technique Using Delay Tap Asynchronous Waveform Sampling,” OFC/NFOEC Technical Digest, 2006, paper OThP5.
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  7. J. A. Jargon, X. Wu, and A. E. Willner, “Optical Performance Monitoring by Use of Artificial Neural Networks Trained with Parameters Derived from Delay-Tap Asynchronous Sampling,” OFC/NFOEC Technical Digest, 2009, paper OThH1.
  8. H. Y. Choi, Y. Takushima, and Y. C. Chung, “Multiple-Impairment Monitoring Technique Using Optical Field Detection and Asynchronous Delay-Tap Sampling Method,” OFC/NFOEC Technical Digest, 2009, paper OThJ5.
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    [CrossRef]
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    [CrossRef]
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    [CrossRef]
  13. P. J. Winzer and R. Essiambre, “Advanced Optical Modulation Formats,” Proc. IEEE 94(5), 952–985 (2006).
    [CrossRef]
  14. V. Arbab, X. Wu, A. E. Willner, and C. Weber, “Optical Performance Monitoring of Data Degradation by Evaluating the Deformation of an Asynchronously Generated I/Q Data Constellation,” European Conference on Optical Communications (ECOC) 2009, paper P3.23.
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    [CrossRef]
  17. “NeuroModeler, ver. 1.5,” Q. J. Zhang and His Neural Network Research Team, Department of Electronics, Carleton University, Ottawa, Canada, 2004.
  18. D. Dahan, D. Levy, and U. Mahlab, “Low Cost Multi-Impairment Monitoring Technique for 43 Gbps DPSK and 86 Gbps DP-DPSK Using Delay Tap Asynchronous Sampling Method,” European Conference on Optical Communications (ECOC) 2009, paper P3.01.

2009 (3)

2006 (2)

P. J. Winzer and R. Essiambre, “Advanced Optical Modulation Formats,” Proc. IEEE 94(5), 952–985 (2006).
[CrossRef]

R. A. Skoog, T. Banwell, J. Gannett, S. Habiby, M. Pang, M. Rauch, and P. Toliver, “Automatic Identification of Impairments Using Support Vector Machine Pattern Classification on Eye Diagrams,” IEEE Photon. Technol. Lett. 18(22), 2398–2400 (2006).
[CrossRef]

2004 (1)

1999 (2)

N. Hanik, A. Gladisch, C. Caspar, and B. Strebel, “Application of Amplitude Histograms to Monitor Performance of Optical Channels,” Electron. Lett. 35(5), 403–404 (1999).
[CrossRef]

S. Ohteru and N. Takachio, “Optical Signal Quality Monitor Using Direct Q-Factor Measurement,” IEEE Photon. Technol. Lett. 11(10), 1307–1309 (1999).
[CrossRef]

1998 (1)

I. Shake, H. Takara, S. Kawanishi, and Y. Yamabayashi, “Optical Signal Quality Monitoring Method Based on Optical Sampling,” Electron. Lett. 34(22), 2152–2154 (1998).
[CrossRef]

1989 (1)

K. Hornik, M. Stinchcombe, and H. White, “Multilayer Feedforward Networks Are Universal Approximators,” Neural Netw. 2(5), 359–366 (1989).
[CrossRef]

Anderson, T. B.

Bach, R.

Banwell, T.

R. A. Skoog, T. Banwell, J. Gannett, S. Habiby, M. Pang, M. Rauch, and P. Toliver, “Automatic Identification of Impairments Using Support Vector Machine Pattern Classification on Eye Diagrams,” IEEE Photon. Technol. Lett. 18(22), 2398–2400 (2006).
[CrossRef]

Blumenthal, D. J.

Caspar, C.

N. Hanik, A. Gladisch, C. Caspar, and B. Strebel, “Application of Amplitude Histograms to Monitor Performance of Optical Channels,” Electron. Lett. 35(5), 403–404 (1999).
[CrossRef]

Clarke, K.

Dods, S. D.

Einstein, D.

Essiambre, R.

P. J. Winzer and R. Essiambre, “Advanced Optical Modulation Formats,” Proc. IEEE 94(5), 952–985 (2006).
[CrossRef]

Gannett, J.

R. A. Skoog, T. Banwell, J. Gannett, S. Habiby, M. Pang, M. Rauch, and P. Toliver, “Automatic Identification of Impairments Using Support Vector Machine Pattern Classification on Eye Diagrams,” IEEE Photon. Technol. Lett. 18(22), 2398–2400 (2006).
[CrossRef]

Gladisch, A.

N. Hanik, A. Gladisch, C. Caspar, and B. Strebel, “Application of Amplitude Histograms to Monitor Performance of Optical Channels,” Electron. Lett. 35(5), 403–404 (1999).
[CrossRef]

Habiby, S.

R. A. Skoog, T. Banwell, J. Gannett, S. Habiby, M. Pang, M. Rauch, and P. Toliver, “Automatic Identification of Impairments Using Support Vector Machine Pattern Classification on Eye Diagrams,” IEEE Photon. Technol. Lett. 18(22), 2398–2400 (2006).
[CrossRef]

Hanik, N.

N. Hanik, A. Gladisch, C. Caspar, and B. Strebel, “Application of Amplitude Histograms to Monitor Performance of Optical Channels,” Electron. Lett. 35(5), 403–404 (1999).
[CrossRef]

Hewitt, D.

Hornik, K.

K. Hornik, M. Stinchcombe, and H. White, “Multilayer Feedforward Networks Are Universal Approximators,” Neural Netw. 2(5), 359–366 (1989).
[CrossRef]

Jargon, J. A.

J. A. Jargon, X. Wu, and A. E. Willner, “Optical Performance Monitoring Using Artificial Neural Networks Trained with Eye-Diagram Parameters,” IEEE Photon. Technol. Lett. 21(1), 54–56 (2009).
[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. 27(16), 3580–3589 (2009).
[CrossRef]

Kawanishi, S.

I. Shake, H. Takara, S. Kawanishi, and Y. Yamabayashi, “Optical Signal Quality Monitoring Method Based on Optical Sampling,” Electron. Lett. 34(22), 2152–2154 (1998).
[CrossRef]

Kilper, D. C.

Kowalczyk, A.

Landolsi, T.

Li, J. C.

Ohteru, S.

S. Ohteru and N. Takachio, “Optical Signal Quality Monitor Using Direct Q-Factor Measurement,” IEEE Photon. Technol. Lett. 11(10), 1307–1309 (1999).
[CrossRef]

Olstar, L.

Pang, M.

R. A. Skoog, T. Banwell, J. Gannett, S. Habiby, M. Pang, M. Rauch, and P. Toliver, “Automatic Identification of Impairments Using Support Vector Machine Pattern Classification on Eye Diagrams,” IEEE Photon. Technol. Lett. 18(22), 2398–2400 (2006).
[CrossRef]

Paraschis, L.

Preiss, M.

Rauch, M.

R. A. Skoog, T. Banwell, J. Gannett, S. Habiby, M. Pang, M. Rauch, and P. Toliver, “Automatic Identification of Impairments Using Support Vector Machine Pattern Classification on Eye Diagrams,” IEEE Photon. Technol. Lett. 18(22), 2398–2400 (2006).
[CrossRef]

Shake, I.

I. Shake, H. Takara, S. Kawanishi, and Y. Yamabayashi, “Optical Signal Quality Monitoring Method Based on Optical Sampling,” Electron. Lett. 34(22), 2152–2154 (1998).
[CrossRef]

Skoog, R. A.

X. Wu, J. A. Jargon, R. A. Skoog, L. Paraschis, and A. E. Willner, “Applications of Artificial Neural Networks in Optical Performance Monitoring,” J. Lightwave Technol. 27(16), 3580–3589 (2009).
[CrossRef]

R. A. Skoog, T. Banwell, J. Gannett, S. Habiby, M. Pang, M. Rauch, and P. Toliver, “Automatic Identification of Impairments Using Support Vector Machine Pattern Classification on Eye Diagrams,” IEEE Photon. Technol. Lett. 18(22), 2398–2400 (2006).
[CrossRef]

Stinchcombe, M.

K. Hornik, M. Stinchcombe, and H. White, “Multilayer Feedforward Networks Are Universal Approximators,” Neural Netw. 2(5), 359–366 (1989).
[CrossRef]

Strebel, B.

N. Hanik, A. Gladisch, C. Caspar, and B. Strebel, “Application of Amplitude Histograms to Monitor Performance of Optical Channels,” Electron. Lett. 35(5), 403–404 (1999).
[CrossRef]

Takachio, N.

S. Ohteru and N. Takachio, “Optical Signal Quality Monitor Using Direct Q-Factor Measurement,” IEEE Photon. Technol. Lett. 11(10), 1307–1309 (1999).
[CrossRef]

Takara, H.

I. Shake, H. Takara, S. Kawanishi, and Y. Yamabayashi, “Optical Signal Quality Monitoring Method Based on Optical Sampling,” Electron. Lett. 34(22), 2152–2154 (1998).
[CrossRef]

Toliver, P.

R. A. Skoog, T. Banwell, J. Gannett, S. Habiby, M. Pang, M. Rauch, and P. Toliver, “Automatic Identification of Impairments Using Support Vector Machine Pattern Classification on Eye Diagrams,” IEEE Photon. Technol. Lett. 18(22), 2398–2400 (2006).
[CrossRef]

White, H.

K. Hornik, M. Stinchcombe, and H. White, “Multilayer Feedforward Networks Are Universal Approximators,” Neural Netw. 2(5), 359–366 (1989).
[CrossRef]

Willner, A. E.

Winzer, P. J.

P. J. Winzer and R. Essiambre, “Advanced Optical Modulation Formats,” Proc. IEEE 94(5), 952–985 (2006).
[CrossRef]

Wu, X.

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. 27(16), 3580–3589 (2009).
[CrossRef]

J. A. Jargon, X. Wu, and A. E. Willner, “Optical Performance Monitoring Using Artificial Neural Networks Trained with Eye-Diagram Parameters,” IEEE Photon. Technol. Lett. 21(1), 54–56 (2009).
[CrossRef]

Yamabayashi, Y.

I. Shake, H. Takara, S. Kawanishi, and Y. Yamabayashi, “Optical Signal Quality Monitoring Method Based on Optical Sampling,” Electron. Lett. 34(22), 2152–2154 (1998).
[CrossRef]

Electron. Lett. (2)

I. Shake, H. Takara, S. Kawanishi, and Y. Yamabayashi, “Optical Signal Quality Monitoring Method Based on Optical Sampling,” Electron. Lett. 34(22), 2152–2154 (1998).
[CrossRef]

N. Hanik, A. Gladisch, C. Caspar, and B. Strebel, “Application of Amplitude Histograms to Monitor Performance of Optical Channels,” Electron. Lett. 35(5), 403–404 (1999).
[CrossRef]

IEEE Photon. Technol. Lett. (3)

S. Ohteru and N. Takachio, “Optical Signal Quality Monitor Using Direct Q-Factor Measurement,” IEEE Photon. Technol. Lett. 11(10), 1307–1309 (1999).
[CrossRef]

R. A. Skoog, T. Banwell, J. Gannett, S. Habiby, M. Pang, M. Rauch, and P. Toliver, “Automatic Identification of Impairments Using Support Vector Machine Pattern Classification on Eye Diagrams,” IEEE Photon. Technol. Lett. 18(22), 2398–2400 (2006).
[CrossRef]

J. A. Jargon, X. Wu, and A. E. Willner, “Optical Performance Monitoring Using Artificial Neural Networks Trained with Eye-Diagram Parameters,” IEEE Photon. Technol. Lett. 21(1), 54–56 (2009).
[CrossRef]

J. Lightwave Technol. (3)

Neural Netw. (1)

K. Hornik, M. Stinchcombe, and H. White, “Multilayer Feedforward Networks Are Universal Approximators,” Neural Netw. 2(5), 359–366 (1989).
[CrossRef]

Proc. IEEE (1)

P. J. Winzer and R. Essiambre, “Advanced Optical Modulation Formats,” Proc. IEEE 94(5), 952–985 (2006).
[CrossRef]

Other (8)

V. Arbab, X. Wu, A. E. Willner, and C. Weber, “Optical Performance Monitoring of Data Degradation by Evaluating the Deformation of an Asynchronously Generated I/Q Data Constellation,” European Conference on Optical Communications (ECOC) 2009, paper P3.23.

M. H. Hassoun, Fundamentals of Artificial Neural Networks (The MIT Press, 1995).

“NeuroModeler, ver. 1.5,” Q. J. Zhang and His Neural Network Research Team, Department of Electronics, Carleton University, Ottawa, Canada, 2004.

D. Dahan, D. Levy, and U. Mahlab, “Low Cost Multi-Impairment Monitoring Technique for 43 Gbps DPSK and 86 Gbps DP-DPSK Using Delay Tap Asynchronous Sampling Method,” European Conference on Optical Communications (ECOC) 2009, paper P3.01.

S. D. Dods and T. B. Anderson, “Optical Performance Monitoring Technique Using Delay Tap Asynchronous Waveform Sampling,” OFC/NFOEC Technical Digest, 2006, paper OThP5.

B. Kozicki, A. Maruta, and K. Kitayama, “Asynchronous Optical Performance Monitoring of RZ-DQPSK Signals Using Delay-Tap Sampling,” ECOC Conference Proceedings, 2007, paper P060.

J. A. Jargon, X. Wu, and A. E. Willner, “Optical Performance Monitoring by Use of Artificial Neural Networks Trained with Parameters Derived from Delay-Tap Asynchronous Sampling,” OFC/NFOEC Technical Digest, 2009, paper OThH1.

H. Y. Choi, Y. Takushima, and Y. C. Chung, “Multiple-Impairment Monitoring Technique Using Optical Field Detection and Asynchronous Delay-Tap Sampling Method,” OFC/NFOEC Technical Digest, 2009, paper OThJ5.

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Figures (5)

Fig. 1
Fig. 1

Artificial neural network architecture.

Fig. 2
Fig. 2

Asynchronous constellation diagrams with various impairments.

Fig. 3
Fig. 3

Dividing the asynchronous constellation diagram into quadrants.

Fig. 4
Fig. 4

Simulation model. CW: continuous-wave; I: in-phase; Q: quadrature-phase; MZM: Mach-Zehnder modulator; CD: chromatic dispersion; DGD: differential group delay; EDFA: Erbium-doped fiber amplifier; BPF: bandpass filter; Att: optical attenuator; DLI: delay-line interferometer; Ф1: + 45°;Ф2: −45°; T: 1-symbol time, 50 ps; BPD: balanced photo-detector; ADC: analog-to-digital convertor.

Fig. 5
Fig. 5

Comparison of testing and ANN-modeled data for the 40 Gbps RZ-QPSK channel.

Equations (3)

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Y = g [ W 2 g ( W 1 X ) ] ,
g ( u ) = 1 / [ 1 + exp ( u ) ] ,
E T r ( w ) = 1 2 n T r k = 1 K | y k ( x n , w ) d k n | 2 ,

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