Abstract

An intelligent optical performance monitor using multi-task learning based artificial neural network (MTL-ANN) is designed for simultaneous OSNR monitoring and modulation format identification (MFI). Signals’ amplitude histograms (AHs) after constant module algorithm are selected as the input features for MTL-ANN. The results obtained from simulation and experiment of NRZ-OOK, PAM4 and PAM8 signals demonstrate that MTL-ANN could achieve OSNR monitoring and MFI simultaneously with higher accuracy and stability compared with single-task learning based ANNs (STL-ANNs). The results show an MFI accuracy of 100% for the three modulation formats under consideration. Furthermore, OSNR monitoring with mean-square error (MSE) of 0.12 dB and accuracy of 100% is achieved while regarding it as regression problem and classification problem, respectively. In this intelligent optical performance monitor, only a single MTL-ANN is deployed, which enables reduced-complexity optical performance monitor (OPM) devices for multi-parameters estimation in future heterogeneous optical network.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2017 (5)

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2014 (3)

M. C. Tan, F. N. Khan, W. H. Al-Arashi, Y. Zhou, and A. P. Tao Lau, “Simultaneous optical performance monitoring and modulation format/bit-rate identification using principal component analysis,” J. Opt. Commun. Netw. 6(5), 441 (2014).
[Crossref]

A. P. T. Lau, Y. Gao, Q. Sui, D. Wang, Q. Zhuge, M. H. Morsy-Osman, M. Chagnon, X. Xu, C. Lu, and D. V. Plant, “Advanced DSP techniques enabling high spectral efficiency and flexible transmissions: toward elastic optical networks,” IEEE Signal Process. Mag. 31(2), 82–92 (2014).
[Crossref]

I. Tomkos, S. Azodolmolky, J. Sole-Pareta, D. Careglio, and E. Palkopoulou, “A tutorial on the flexible optical networking paradigm: State of the art, trends, and research challenges,” Proc. IEEE 102(9), 1317–1337 (2014).
[Crossref]

2012 (2)

2008 (1)

1997 (1)

R. Caruana, “Multitask Learning,” Mach. Learn. 28(1), 41–75 (1997).
[Crossref]

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J. Treichler and B. Agee, “A new approach to multipath correction of constant modulus signals,” IEEE Trans. Acoust. Speech Signal Process. 31(2), 459–472 (1983).
[Crossref]

Agee, B.

J. Treichler and B. Agee, “A new approach to multipath correction of constant modulus signals,” IEEE Trans. Acoust. Speech Signal Process. 31(2), 459–472 (1983).
[Crossref]

Akiyama, Y.

S. Oda, M. Bouda, Y. Ge, S. Yoshida, T. Tanimura, Y. Akiyama, Y. Hirose, Z. Tao, T. Lkeuchi, and T. Hoshida, “Innovative Optical Networking by Optical Performance Monitoring and Learning Process,” in European Conference on Optical Communication (ECOC) (2018), p. 1.
[Crossref]

Al-Arashi, W. H.

Azodolmolky, S.

I. Tomkos, S. Azodolmolky, J. Sole-Pareta, D. Careglio, and E. Palkopoulou, “A tutorial on the flexible optical networking paradigm: State of the art, trends, and research challenges,” Proc. IEEE 102(9), 1317–1337 (2014).
[Crossref]

Baker-Meflah, L.

Bayvel, P.

Bouda, M.

S. Oda, M. Bouda, Y. Ge, S. Yoshida, T. Tanimura, Y. Akiyama, Y. Hirose, Z. Tao, T. Lkeuchi, and T. Hoshida, “Innovative Optical Networking by Optical Performance Monitoring and Learning Process,” in European Conference on Optical Communication (ECOC) (2018), p. 1.
[Crossref]

Careglio, D.

I. Tomkos, S. Azodolmolky, J. Sole-Pareta, D. Careglio, and E. Palkopoulou, “A tutorial on the flexible optical networking paradigm: State of the art, trends, and research challenges,” Proc. IEEE 102(9), 1317–1337 (2014).
[Crossref]

Caruana, R.

R. Caruana, “Multitask Learning,” Mach. Learn. 28(1), 41–75 (1997).
[Crossref]

Chagnon, M.

A. P. T. Lau, Y. Gao, Q. Sui, D. Wang, Q. Zhuge, M. H. Morsy-Osman, M. Chagnon, X. Xu, C. Lu, and D. V. Plant, “Advanced DSP techniques enabling high spectral efficiency and flexible transmissions: toward elastic optical networks,” IEEE Signal Process. Mag. 31(2), 82–92 (2014).
[Crossref]

Chan, C. C.-K.

Chen, W.

X. Fan, Y. Xie, F. Ren, Y. Zhang, X. Huang, W. Chen, T. Zhangsun, and J. Wang, “Joint optical performance monitoring and modulation format/bit-rate identification by CNN-based multi-task learning,” IEEE Photonics J. 10(5), 1–12 (2018).
[Crossref]

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 Photonics Technol. Lett. 29(19), 1667–1670 (2017).
[Crossref]

D. Wang, M. Zhang, J. Li, Z. Li, J. Li, C. Song, and X. Chen, “Intelligent constellation diagram analyzer using convolutional neural network-based deep learning,” Opt. Express 25(15), 17150–17166 (2017).
[Crossref] [PubMed]

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 Photonics Technol. Lett. 29(19), 1667–1670 (2017).
[Crossref]

Dai, Y.

Dong, Z.

Fan, X.

X. Fan, Y. Xie, F. Ren, Y. Zhang, X. Huang, W. Chen, T. Zhangsun, and J. Wang, “Joint optical performance monitoring and modulation format/bit-rate identification by CNN-based multi-task learning,” IEEE Photonics J. 10(5), 1–12 (2018).
[Crossref]

Fan, Y.

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 Photonics Technol. Lett. 29(19), 1667–1670 (2017).
[Crossref]

Fu, S.

Gao, Y.

A. P. T. Lau, Y. Gao, Q. Sui, D. Wang, Q. Zhuge, M. H. Morsy-Osman, M. Chagnon, X. Xu, C. Lu, and D. V. Plant, “Advanced DSP techniques enabling high spectral efficiency and flexible transmissions: toward elastic optical networks,” IEEE Signal Process. Mag. 31(2), 82–92 (2014).
[Crossref]

Ge, Y.

S. Oda, M. Bouda, Y. Ge, S. Yoshida, T. Tanimura, Y. Akiyama, Y. Hirose, Z. Tao, T. Lkeuchi, and T. Hoshida, “Innovative Optical Networking by Optical Performance Monitoring and Learning Process,” in European Conference on Optical Communication (ECOC) (2018), p. 1.
[Crossref]

Guo, P.

He, P.

He, Z.

Hinton, G. E.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Commun. ACM 60(6), 84–90 (2017).
[Crossref]

Hirose, Y.

S. Oda, M. Bouda, Y. Ge, S. Yoshida, T. Tanimura, Y. Akiyama, Y. Hirose, Z. Tao, T. Lkeuchi, and T. Hoshida, “Innovative Optical Networking by Optical Performance Monitoring and Learning Process,” in European Conference on Optical Communication (ECOC) (2018), p. 1.
[Crossref]

Hoshida, T.

S. Oda, M. Bouda, Y. Ge, S. Yoshida, T. Tanimura, Y. Akiyama, Y. Hirose, Z. Tao, T. Lkeuchi, and T. Hoshida, “Innovative Optical Networking by Optical Performance Monitoring and Learning Process,” in European Conference on Optical Communication (ECOC) (2018), p. 1.
[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 Communication Conference (OSA, 2018), p. Tu3E.3.
[Crossref]

Hu, Z.

Huang, X.

X. Fan, Y. Xie, F. Ren, Y. Zhang, X. Huang, W. Chen, T. Zhangsun, and J. Wang, “Joint optical performance monitoring and modulation format/bit-rate identification by CNN-based multi-task learning,” IEEE Photonics J. 10(5), 1–12 (2018).
[Crossref]

Jiang, H.

Johannisson, P.

L. Lundberg, H. Sunnerud, and P. Johannisson, “In-Band OSNR Monitoring of PM-QPSK Using the Stokes Parameters,” in Optical Fiber Communication Conference (OSA, 2015), p. W4D.5.
[Crossref]

Kato, T.

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 Communication Conference (OSA, 2018), p. Tu3E.3.
[Crossref]

Khan, F. N.

Krizhevsky, A.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Commun. ACM 60(6), 84–90 (2017).
[Crossref]

Lau, A. P. T.

Li, J.

Li, Y.

Li, Z.

D. Wang, M. Zhang, J. Li, Z. Li, J. Li, C. Song, and X. Chen, “Intelligent constellation diagram analyzer using convolutional neural network-based deep learning,” Opt. Express 25(15), 17150–17166 (2017).
[Crossref] [PubMed]

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 Photonics Technol. Lett. 29(19), 1667–1670 (2017).
[Crossref]

Liu, D.

Lkeuchi, T.

S. Oda, M. Bouda, Y. Ge, S. Yoshida, T. Tanimura, Y. Akiyama, Y. Hirose, Z. Tao, T. Lkeuchi, and T. Hoshida, “Innovative Optical Networking by Optical Performance Monitoring and Learning Process,” in European Conference on Optical Communication (ECOC) (2018), p. 1.
[Crossref]

Lu, C.

Lu, J.

Lundberg, L.

L. Lundberg, H. Sunnerud, and P. Johannisson, “In-Band OSNR Monitoring of PM-QPSK Using the Stokes Parameters,” in Optical Fiber Communication Conference (OSA, 2015), p. W4D.5.
[Crossref]

Mitchell, J.

Morikawa, H.

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 Communication Conference (OSA, 2018), p. Tu3E.3.
[Crossref]

Morsy-Osman, M. H.

A. P. T. Lau, Y. Gao, Q. Sui, D. Wang, Q. Zhuge, M. H. Morsy-Osman, M. Chagnon, X. Xu, C. Lu, and D. V. Plant, “Advanced DSP techniques enabling high spectral efficiency and flexible transmissions: toward elastic optical networks,” IEEE Signal Process. Mag. 31(2), 82–92 (2014).
[Crossref]

Oda, S.

S. Oda, M. Bouda, Y. Ge, S. Yoshida, T. Tanimura, Y. Akiyama, Y. Hirose, Z. Tao, T. Lkeuchi, and T. Hoshida, “Innovative Optical Networking by Optical Performance Monitoring and Learning Process,” in European Conference on Optical Communication (ECOC) (2018), p. 1.
[Crossref]

Palkopoulou, E.

I. Tomkos, S. Azodolmolky, J. Sole-Pareta, D. Careglio, and E. Palkopoulou, “A tutorial on the flexible optical networking paradigm: State of the art, trends, and research challenges,” Proc. IEEE 102(9), 1317–1337 (2014).
[Crossref]

Plant, D. V.

A. P. T. Lau, Y. Gao, Q. Sui, D. Wang, Q. Zhuge, M. H. Morsy-Osman, M. Chagnon, X. Xu, C. Lu, and D. V. Plant, “Advanced DSP techniques enabling high spectral efficiency and flexible transmissions: toward elastic optical networks,” IEEE Signal Process. Mag. 31(2), 82–92 (2014).
[Crossref]

Ren, F.

X. Fan, Y. Xie, F. Ren, Y. Zhang, X. Huang, W. Chen, T. Zhangsun, and J. Wang, “Joint optical performance monitoring and modulation format/bit-rate identification by CNN-based multi-task learning,” IEEE Photonics J. 10(5), 1–12 (2018).
[Crossref]

Shu, L.

Sole-Pareta, J.

I. Tomkos, S. Azodolmolky, J. Sole-Pareta, D. Careglio, and E. Palkopoulou, “A tutorial on the flexible optical networking paradigm: State of the art, trends, and research challenges,” Proc. IEEE 102(9), 1317–1337 (2014).
[Crossref]

Song, C.

Sui, Q.

Z. Dong, F. N. Khan, Q. Sui, K. Zhong, C. Lu, and A. P. T. Lau, “Optical performance monitoring: a review of current and future technologies,” J. Lightwave Technol. 34(2), 525–543 (2016).
[Crossref]

A. P. T. Lau, Y. Gao, Q. Sui, D. Wang, Q. Zhuge, M. H. Morsy-Osman, M. Chagnon, X. Xu, C. Lu, and D. V. Plant, “Advanced DSP techniques enabling high spectral efficiency and flexible transmissions: toward elastic optical networks,” IEEE Signal Process. Mag. 31(2), 82–92 (2014).
[Crossref]

Sunnerud, H.

L. Lundberg, H. Sunnerud, and P. Johannisson, “In-Band OSNR Monitoring of PM-QPSK Using the Stokes Parameters,” in Optical Fiber Communication Conference (OSA, 2015), p. W4D.5.
[Crossref]

Sutskever, I.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Commun. ACM 60(6), 84–90 (2017).
[Crossref]

Tan, M. C.

Tang, M.

Tanimura, T.

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 Communication Conference (OSA, 2018), p. Tu3E.3.
[Crossref]

S. Oda, M. Bouda, Y. Ge, S. Yoshida, T. Tanimura, Y. Akiyama, Y. Hirose, Z. Tao, T. Lkeuchi, and T. Hoshida, “Innovative Optical Networking by Optical Performance Monitoring and Learning Process,” in European Conference on Optical Communication (ECOC) (2018), p. 1.
[Crossref]

Tao, Z.

S. Oda, M. Bouda, Y. Ge, S. Yoshida, T. Tanimura, Y. Akiyama, Y. Hirose, Z. Tao, T. Lkeuchi, and T. Hoshida, “Innovative Optical Networking by Optical Performance Monitoring and Learning Process,” in European Conference on Optical Communication (ECOC) (2018), p. 1.
[Crossref]

Tao Lau, A. P.

Thomsen, B.

Tomkos, I.

I. Tomkos, S. Azodolmolky, J. Sole-Pareta, D. Careglio, and E. Palkopoulou, “A tutorial on the flexible optical networking paradigm: State of the art, trends, and research challenges,” Proc. IEEE 102(9), 1317–1337 (2014).
[Crossref]

Treichler, J.

J. Treichler and B. Agee, “A new approach to multipath correction of constant modulus signals,” IEEE Trans. Acoust. Speech Signal Process. 31(2), 459–472 (1983).
[Crossref]

Wan, Z.

Wang, D.

D. Wang, M. Zhang, J. Li, Z. Li, J. Li, C. Song, and X. Chen, “Intelligent constellation diagram analyzer using convolutional neural network-based deep learning,” Opt. Express 25(15), 17150–17166 (2017).
[Crossref] [PubMed]

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 Photonics Technol. Lett. 29(19), 1667–1670 (2017).
[Crossref]

A. P. T. Lau, Y. Gao, Q. Sui, D. Wang, Q. Zhuge, M. H. Morsy-Osman, M. Chagnon, X. Xu, C. Lu, and D. V. Plant, “Advanced DSP techniques enabling high spectral efficiency and flexible transmissions: toward elastic optical networks,” IEEE Signal Process. Mag. 31(2), 82–92 (2014).
[Crossref]

Wang, J.

X. Fan, Y. Xie, F. Ren, Y. Zhang, X. Huang, W. Chen, T. Zhangsun, and J. Wang, “Joint optical performance monitoring and modulation format/bit-rate identification by CNN-based multi-task learning,” IEEE Photonics J. 10(5), 1–12 (2018).
[Crossref]

Wang, Z.

Watanabe, S.

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 Communication Conference (OSA, 2018), p. Tu3E.3.
[Crossref]

Wu, Q.

Xie, Y.

X. Fan, Y. Xie, F. Ren, Y. Zhang, X. Huang, W. Chen, T. Zhangsun, and J. Wang, “Joint optical performance monitoring and modulation format/bit-rate identification by CNN-based multi-task learning,” IEEE Photonics J. 10(5), 1–12 (2018).
[Crossref]

Xu, K.

Xu, X.

A. P. T. Lau, Y. Gao, Q. Sui, D. Wang, Q. Zhuge, M. H. Morsy-Osman, M. Chagnon, X. Xu, C. Lu, and D. V. Plant, “Advanced DSP techniques enabling high spectral efficiency and flexible transmissions: toward elastic optical networks,” IEEE Signal Process. Mag. 31(2), 82–92 (2014).
[Crossref]

Xu, Z.

Yang, A.

Yin, F.

Yoshida, S.

S. Oda, M. Bouda, Y. Ge, S. Yoshida, T. Tanimura, Y. Akiyama, Y. Hirose, Z. Tao, T. Lkeuchi, and T. Hoshida, “Innovative Optical Networking by Optical Performance Monitoring and Learning Process,” in European Conference on Optical Communication (ECOC) (2018), p. 1.
[Crossref]

Yu, C.

Yu, Y.

Zhang, H.

Zhang, M.

D. Wang, M. Zhang, J. Li, Z. Li, J. Li, C. Song, and X. Chen, “Intelligent constellation diagram analyzer using convolutional neural network-based deep learning,” Opt. Express 25(15), 17150–17166 (2017).
[Crossref] [PubMed]

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 Photonics Technol. Lett. 29(19), 1667–1670 (2017).
[Crossref]

Zhang, N.

Zhang, W.

Zhang, X.

Zhang, Y.

X. Fan, Y. Xie, F. Ren, Y. Zhang, X. Huang, W. Chen, T. Zhangsun, and J. Wang, “Joint optical performance monitoring and modulation format/bit-rate identification by CNN-based multi-task learning,” IEEE Photonics J. 10(5), 1–12 (2018).
[Crossref]

Zhangsun, T.

X. Fan, Y. Xie, F. Ren, Y. Zhang, X. Huang, W. Chen, T. Zhangsun, and J. Wang, “Joint optical performance monitoring and modulation format/bit-rate identification by CNN-based multi-task learning,” IEEE Photonics J. 10(5), 1–12 (2018).
[Crossref]

Zhong, K.

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Figures (10)

Fig. 1
Fig. 1 AHs with 100 bins for different OSNRs for NRZ-OOK (first row), PAM4 (second row) and PAM8 (third row) signals after CMA equalization.
Fig. 2
Fig. 2 Schematic structure of MTL-ANN.
Fig. 3
Fig. 3 Simulation setup for simultaneous OSNR monitoring and MFI.
Fig. 4
Fig. 4 (a) MFI accuracy and (b) OSNR estimated MSE versus hidden neurons in shared hidden layer for MTL-ANN and STL-ANN (Average, maximum and minimum value from eight random initialization).
Fig. 5
Fig. 5 OSNR estimated MSE versus ratio of OSNR loss weight to MFI loss weight (Average, maximum and minimum value from eight random initialization).
Fig. 6
Fig. 6 True OSNRs versus estimated OSNRs of MTL-ANN.
Fig. 7
Fig. 7 (a) MFI accuracy versus hidden neurons in shared hidden layer (b) OSNR accuracy versus hidden neurons in shared hidden layer for STL-ANN and MTL-ANN with different loss ratio (Average, maximum and minimum value from eight random initialization).
Fig. 8
Fig. 8 Experimental setup for simultaneous OSNR monitoring and MFI.
Fig. 9
Fig. 9 Experimental (a) MFI accuracy and (b) OSNR estimated MSE versus hidden neurons in shared hidden layer for STL-ANN and MTL-ANN with 100 loss ratio (Average, maximum and minimum value from eight random initialization).
Fig. 10
Fig. 10 Experimental (a) MFI accuracy versus hidden neurons in shared hidden layer (b) OSNR accuracy versus hidden neurons in shared hidden layer for STL-ANN and MTL-ANN with 100 loss ratio (Average, maximum and minimum value from eight random initialization).

Tables (1)

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Table 1 Computation time per sample for testing MTL-ANN and STL-ANNs

Equations (4)

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A r ( x )=f( i w ri x i )
Tanh(x)= e x e x e x + e x
Softmax( x i )= e x i i e x i
J(w)= t=1 T { w t k=1 K t | [ y t (n) ] k [ h t ( x(n) ) ] k | 2 } +λ i=1 m ϑ i 2

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