Abstract

A loss weight adaptive multi-task learning based artificial neural network (MTL-ANN) is applied for joint optical signal-to-noise ratio (OSNR) monitoring and modulation format identification (MFI). We conduct an experiment of polarization division multiplexing (PDM) coherent optical system with 5 km standard single mode fiber (SSMF) transmission to verify this monitor. A group of modulation schemes including nine modulation adaptive M-QAM formats are selected as the transmission signals. Instead of circular constellation, signals’ amplitude histograms after constant module algorithm (CMA) based polarization de-multiplexing are selected as input features for our proposed monitor. The experimental results show that the MFI accuracy reaches 100% in the estimated OSNR range. Furthermore, when treated as regression problem and classification problem, OSNR estimation with a root mean-square error (RMSE) of 0.68 dB and an accuracy of 98.7% are achieved, respectively. Unlike loss weight fixed MTL-ANN, loss weight adaptive MTL-ANN could search the optimal loss weight ratio automatically for different link configurations. Besides that, the number of estimated parameters can be easily expanded, which is attractive for multiple parameters estimation in future heterogeneous optical networks.

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

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References

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  1. Cisco, “Cisco visual networking index: forecast and trends, 2017-2022,” (Cisco White Paper, 2019). https://www.cisco.com/c/en/us/solutions/collateral/service-provider/global-cloud-index-gci/white-paper-c11-738085.pdf .
  2. K. Roberts, Q. Zhuge, I. Monga, S. Gareau, and C. Laperle, “Beyond 100 Gb/s: Capacity, Flexibility, and Network Optimization,” J. Opt. Commun. Netw. 9(4), C12–C24 (2017).
    [Crossref]
  3. Q. Zhuge and W. Hu, “Application of Machine Learning in Elastic Optical Networks,” in 2018 European Conference on Optical Communication (ECOC) (IEEE, 2018), pp. 1–3.
  4. A. Yi, L. Yan, H. Liu, L. Jiang, Y. Pan, B. Luo, and W. Pan, “Modulation format identification and OSNR monitoring using density distributions in Stokes axes for digital coherent receivers,” Opt. Express 27(4), 4471 (2019).
    [Crossref]
  5. 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]
  6. W. Freude, R. Schmogrow, B. Nebendahl, M. Winter, A. Josten, D. Hillerkuss, S. Koenig, J. Meyer, M. Dreschmann, M. Huebner, C. Koos, J. Becker, and J. Leuthold, “Quality metrics for optical signals: Eye diagram, Q-factor, OSNR, EVM and BER,” in 2012 14th International Conference on Transparent Optical Networks (ICTON) (2012), pp. 1–4.
  7. I. Tomkos, S. Azodolmolky, J. Solé-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]
  8. S. M. Bilal, G. Bosco, Z. Dong, A. P. T. Lau, and C. Lu, “Blind modulation format identification for digital coherent receivers,” Opt. Express 23(20), 26769 (2015).
    [Crossref]
  9. Z. Wan, J. Li, L. Shu, S. Fu, Y. Fan, F. Yin, Y. Zhou, Y. Dai, and K. Xu, “64-Gb/s SSB-PAM4 Transmission Over 120-km Dispersion-Uncompensated SSMF With Blind Nonlinear Equalization, Adaptive Noise-Whitening Postfilter and MLSD,” J. Lightwave Technol. 35(23), 5193–5200 (2017).
    [Crossref]
  10. 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 25(15), 17767 (2017).
    [Crossref]
  11. Z. Wan, Z. Yu, L. Shu, Y. Zhao, H. Zhang, and K. Xu, “Intelligent optical performance monitor using multi-task learning based artificial neural network,” Opt. Express 27(8), 11281 (2019).
    [Crossref]
  12. D. Wang, M. Wang, M. Zhang, Z. Zhang, H. Yang, J. Li, J. Li, and X. Chen, “Cost-effective and data size–adaptive OPM at intermediated node using convolutional neural network-based image processor,” Opt. Express 27(7), 9403 (2019).
    [Crossref]
  13. 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 23(23), 30337 (2015).
    [Crossref]
  14. W. Zhang, D. Zhu, Z. He, N. Zhang, X. Zhang, H. Zhang, and Y. Li, “Identifying modulation formats through 2D Stokes planes with deep neural networks,” Opt. Express 26(18), 23507 (2018).
    [Crossref]
  15. Z. Dong, A. P. T. Lau, and C. Lu, “OSNR monitoring for QPSK and 16-QAM systems in presence of fiber nonlinearities for digital coherent receivers,” Opt. Express 20(17), 19520 (2012).
    [Crossref]
  16. S. Fu, Z. Xu, J. Lu, H. Jiang, Q. Wu, Z. Hu, M. Tang, D. Liu, and C. C.-K. Chan, “Modulation format identification enabled by the digital frequency-offset loading technique for hitless coherent transceiver,” Opt. Express 26(6), 7288 (2018).
    [Crossref]
  17. L. Baker-Meflah, B. Thomsen, J. Mitchell, and P. Bayvel, “Simultaneous chromatic dispersion, polarization-mode-dispersion and OSNR monitoring at 40Gbit/s,” Opt. Express 16(20), 15999–16004 (2008).
    [Crossref]
  18. 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]
  19. Z. Wang, A. Yang, P. Guo, and P. He, “OSNR and nonlinear noise power estimation for optical fiber communication systems using LSTM based deep learning technique,” Opt. Express 26(16), 21346 (2018).
    [Crossref]
  20. R. Cipolla, Y. Gal, and A. Kendall, “Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (IEEE, 2018), pp. 7482–7491.
  21. A. T. Le and K. Araki, “A group of modulation schemes for adaptive modulation,” in 2008 11th IEEE Singapore International Conference on Communication Systems (IEEE, 2008), pp. 864–869.
  22. Z. Yu, H. Chen, M. Chen, S. Yang, and S. Xie, “Bandwidth Improvement Using Adaptive Loading Scheme in Optical Direct-Detection OFDM,” IEEE J. Quantum Electron. 52(10), 1–6 (2016).
    [Crossref]
  23. R. A. Caruana, “Multitask Learning: A Knowledge-Based Source of Inductive Bias,” in Machine Learning Proceedings 1993 (Elsevier, 1993), pp. 41–48.
  24. M. Long, Z. Cao, J. Wang, and P. S. Yu, “Learning Multiple Tasks with Multilinear Relationship Networks,” arXiv:1506.02117 [cs] (2015).
  25. A. Kendall and Y. Gal, “What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?” arXiv:1703.04977 [cs] (2017).
  26. D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” arXiv:1412.6980 [cs] (2014).
  27. M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” arXiv:1603.04467 [cs] (2016).

2019 (3)

2018 (4)

2017 (3)

2016 (2)

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]

Z. Yu, H. Chen, M. Chen, S. Yang, and S. Xie, “Bandwidth Improvement Using Adaptive Loading Scheme in Optical Direct-Detection OFDM,” IEEE J. Quantum Electron. 52(10), 1–6 (2016).
[Crossref]

2015 (2)

2014 (1)

I. Tomkos, S. Azodolmolky, J. Solé-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 (1)

2008 (1)

Abadi, M.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” arXiv:1603.04467 [cs] (2016).

Agarwal, A.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” arXiv:1603.04467 [cs] (2016).

Al-Arashi, W. H.

Araki, K.

A. T. Le and K. Araki, “A group of modulation schemes for adaptive modulation,” in 2008 11th IEEE Singapore International Conference on Communication Systems (IEEE, 2008), pp. 864–869.

Azodolmolky, S.

I. Tomkos, S. Azodolmolky, J. Solé-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]

Ba, J.

D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” arXiv:1412.6980 [cs] (2014).

Baker-Meflah, L.

Barham, P.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” arXiv:1603.04467 [cs] (2016).

Bayvel, P.

Becker, J.

W. Freude, R. Schmogrow, B. Nebendahl, M. Winter, A. Josten, D. Hillerkuss, S. Koenig, J. Meyer, M. Dreschmann, M. Huebner, C. Koos, J. Becker, and J. Leuthold, “Quality metrics for optical signals: Eye diagram, Q-factor, OSNR, EVM and BER,” in 2012 14th International Conference on Transparent Optical Networks (ICTON) (2012), pp. 1–4.

Bilal, S. M.

Bosco, G.

Brevdo, E.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” arXiv:1603.04467 [cs] (2016).

Cao, Z.

M. Long, Z. Cao, J. Wang, and P. S. Yu, “Learning Multiple Tasks with Multilinear Relationship Networks,” arXiv:1506.02117 [cs] (2015).

Careglio, D.

I. Tomkos, S. Azodolmolky, J. Solé-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. A.

R. A. Caruana, “Multitask Learning: A Knowledge-Based Source of Inductive Bias,” in Machine Learning Proceedings 1993 (Elsevier, 1993), pp. 41–48.

Chan, C. C.-K.

Chen, H.

Z. Yu, H. Chen, M. Chen, S. Yang, and S. Xie, “Bandwidth Improvement Using Adaptive Loading Scheme in Optical Direct-Detection OFDM,” IEEE J. Quantum Electron. 52(10), 1–6 (2016).
[Crossref]

Chen, M.

Z. Yu, H. Chen, M. Chen, S. Yang, and S. Xie, “Bandwidth Improvement Using Adaptive Loading Scheme in Optical Direct-Detection OFDM,” IEEE J. Quantum Electron. 52(10), 1–6 (2016).
[Crossref]

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.

Chen, Z.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” arXiv:1603.04467 [cs] (2016).

Cipolla, R.

R. Cipolla, Y. Gal, and A. Kendall, “Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (IEEE, 2018), pp. 7482–7491.

Citro, C.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” arXiv:1603.04467 [cs] (2016).

Corrado, G. S.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” arXiv:1603.04467 [cs] (2016).

Dai, Y.

Davis, A.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” arXiv:1603.04467 [cs] (2016).

Dean, J.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” arXiv:1603.04467 [cs] (2016).

Devin, M.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” arXiv:1603.04467 [cs] (2016).

Dong, Z.

Dreschmann, M.

W. Freude, R. Schmogrow, B. Nebendahl, M. Winter, A. Josten, D. Hillerkuss, S. Koenig, J. Meyer, M. Dreschmann, M. Huebner, C. Koos, J. Becker, and J. Leuthold, “Quality metrics for optical signals: Eye diagram, Q-factor, OSNR, EVM and BER,” in 2012 14th International Conference on Transparent Optical Networks (ICTON) (2012), pp. 1–4.

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.

Freude, W.

W. Freude, R. Schmogrow, B. Nebendahl, M. Winter, A. Josten, D. Hillerkuss, S. Koenig, J. Meyer, M. Dreschmann, M. Huebner, C. Koos, J. Becker, and J. Leuthold, “Quality metrics for optical signals: Eye diagram, Q-factor, OSNR, EVM and BER,” in 2012 14th International Conference on Transparent Optical Networks (ICTON) (2012), pp. 1–4.

Fu, S.

Gal, Y.

A. Kendall and Y. Gal, “What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?” arXiv:1703.04977 [cs] (2017).

R. Cipolla, Y. Gal, and A. Kendall, “Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (IEEE, 2018), pp. 7482–7491.

Gareau, S.

Ghemawat, S.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” arXiv:1603.04467 [cs] (2016).

Goodfellow, I.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” arXiv:1603.04467 [cs] (2016).

Guo, P.

Harp, A.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” arXiv:1603.04467 [cs] (2016).

He, P.

He, Z.

Hillerkuss, D.

W. Freude, R. Schmogrow, B. Nebendahl, M. Winter, A. Josten, D. Hillerkuss, S. Koenig, J. Meyer, M. Dreschmann, M. Huebner, C. Koos, J. Becker, and J. Leuthold, “Quality metrics for optical signals: Eye diagram, Q-factor, OSNR, EVM and BER,” in 2012 14th International Conference on Transparent Optical Networks (ICTON) (2012), pp. 1–4.

Hu, W.

Q. Zhuge and W. Hu, “Application of Machine Learning in Elastic Optical Networks,” in 2018 European Conference on Optical Communication (ECOC) (IEEE, 2018), pp. 1–3.

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]

Huebner, M.

W. Freude, R. Schmogrow, B. Nebendahl, M. Winter, A. Josten, D. Hillerkuss, S. Koenig, J. Meyer, M. Dreschmann, M. Huebner, C. Koos, J. Becker, and J. Leuthold, “Quality metrics for optical signals: Eye diagram, Q-factor, OSNR, EVM and BER,” in 2012 14th International Conference on Transparent Optical Networks (ICTON) (2012), pp. 1–4.

Irving, G.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” arXiv:1603.04467 [cs] (2016).

Isard, M.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” arXiv:1603.04467 [cs] (2016).

Jia, Y.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” arXiv:1603.04467 [cs] (2016).

Jiang, H.

Jiang, L.

Josten, A.

W. Freude, R. Schmogrow, B. Nebendahl, M. Winter, A. Josten, D. Hillerkuss, S. Koenig, J. Meyer, M. Dreschmann, M. Huebner, C. Koos, J. Becker, and J. Leuthold, “Quality metrics for optical signals: Eye diagram, Q-factor, OSNR, EVM and BER,” in 2012 14th International Conference on Transparent Optical Networks (ICTON) (2012), pp. 1–4.

Jozefowicz, R.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” arXiv:1603.04467 [cs] (2016).

Kaiser, L.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” arXiv:1603.04467 [cs] (2016).

Kendall, A.

R. Cipolla, Y. Gal, and A. Kendall, “Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (IEEE, 2018), pp. 7482–7491.

A. Kendall and Y. Gal, “What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?” arXiv:1703.04977 [cs] (2017).

Khan, F. N.

Kingma, D. P.

D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” arXiv:1412.6980 [cs] (2014).

Koenig, S.

W. Freude, R. Schmogrow, B. Nebendahl, M. Winter, A. Josten, D. Hillerkuss, S. Koenig, J. Meyer, M. Dreschmann, M. Huebner, C. Koos, J. Becker, and J. Leuthold, “Quality metrics for optical signals: Eye diagram, Q-factor, OSNR, EVM and BER,” in 2012 14th International Conference on Transparent Optical Networks (ICTON) (2012), pp. 1–4.

Koos, C.

W. Freude, R. Schmogrow, B. Nebendahl, M. Winter, A. Josten, D. Hillerkuss, S. Koenig, J. Meyer, M. Dreschmann, M. Huebner, C. Koos, J. Becker, and J. Leuthold, “Quality metrics for optical signals: Eye diagram, Q-factor, OSNR, EVM and BER,” in 2012 14th International Conference on Transparent Optical Networks (ICTON) (2012), pp. 1–4.

Kudlur, M.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” arXiv:1603.04467 [cs] (2016).

Laperle, C.

Lau, A. P. T.

Le, A. T.

A. T. Le and K. Araki, “A group of modulation schemes for adaptive modulation,” in 2008 11th IEEE Singapore International Conference on Communication Systems (IEEE, 2008), pp. 864–869.

Leuthold, J.

W. Freude, R. Schmogrow, B. Nebendahl, M. Winter, A. Josten, D. Hillerkuss, S. Koenig, J. Meyer, M. Dreschmann, M. Huebner, C. Koos, J. Becker, and J. Leuthold, “Quality metrics for optical signals: Eye diagram, Q-factor, OSNR, EVM and BER,” in 2012 14th International Conference on Transparent Optical Networks (ICTON) (2012), pp. 1–4.

Levenberg, J.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” arXiv:1603.04467 [cs] (2016).

Li, J.

Li, Y.

Liu, D.

Liu, H.

Long, M.

M. Long, Z. Cao, J. Wang, and P. S. Yu, “Learning Multiple Tasks with Multilinear Relationship Networks,” arXiv:1506.02117 [cs] (2015).

Lu, C.

Lu, J.

Luo, B.

Mane, D.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” arXiv:1603.04467 [cs] (2016).

Meyer, J.

W. Freude, R. Schmogrow, B. Nebendahl, M. Winter, A. Josten, D. Hillerkuss, S. Koenig, J. Meyer, M. Dreschmann, M. Huebner, C. Koos, J. Becker, and J. Leuthold, “Quality metrics for optical signals: Eye diagram, Q-factor, OSNR, EVM and BER,” in 2012 14th International Conference on Transparent Optical Networks (ICTON) (2012), pp. 1–4.

Mitchell, J.

Monga, I.

Monga, R.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” arXiv:1603.04467 [cs] (2016).

Moore, S.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” arXiv:1603.04467 [cs] (2016).

Murray, D.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” arXiv:1603.04467 [cs] (2016).

Nebendahl, B.

W. Freude, R. Schmogrow, B. Nebendahl, M. Winter, A. Josten, D. Hillerkuss, S. Koenig, J. Meyer, M. Dreschmann, M. Huebner, C. Koos, J. Becker, and J. Leuthold, “Quality metrics for optical signals: Eye diagram, Q-factor, OSNR, EVM and BER,” in 2012 14th International Conference on Transparent Optical Networks (ICTON) (2012), pp. 1–4.

Olah, C.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” arXiv:1603.04467 [cs] (2016).

Palkopoulou, E.

I. Tomkos, S. Azodolmolky, J. Solé-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]

Pan, W.

Pan, Y.

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]

Roberts, K.

Schmogrow, R.

W. Freude, R. Schmogrow, B. Nebendahl, M. Winter, A. Josten, D. Hillerkuss, S. Koenig, J. Meyer, M. Dreschmann, M. Huebner, C. Koos, J. Becker, and J. Leuthold, “Quality metrics for optical signals: Eye diagram, Q-factor, OSNR, EVM and BER,” in 2012 14th International Conference on Transparent Optical Networks (ICTON) (2012), pp. 1–4.

Schuster, M.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” arXiv:1603.04467 [cs] (2016).

Shlens, J.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” arXiv:1603.04467 [cs] (2016).

Shu, L.

Solé-Pareta, J.

I. Tomkos, S. Azodolmolky, J. Solé-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]

Steiner, B.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” arXiv:1603.04467 [cs] (2016).

Sui, Q.

Sutskever, I.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” arXiv:1603.04467 [cs] (2016).

Talwar, K.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” arXiv:1603.04467 [cs] (2016).

Tan, M. C.

Tang, M.

Thomsen, B.

Tomkos, I.

I. Tomkos, S. Azodolmolky, J. Solé-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]

Tucker, P.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” arXiv:1603.04467 [cs] (2016).

Vanhoucke, V.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” arXiv:1603.04467 [cs] (2016).

Vasudevan, V.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” arXiv:1603.04467 [cs] (2016).

Viegas, F.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” arXiv:1603.04467 [cs] (2016).

Vinyals, O.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” arXiv:1603.04467 [cs] (2016).

Wan, Z.

Wang, D.

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]

M. Long, Z. Cao, J. Wang, and P. S. Yu, “Learning Multiple Tasks with Multilinear Relationship Networks,” arXiv:1506.02117 [cs] (2015).

Wang, M.

Wang, Z.

Warden, P.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” arXiv:1603.04467 [cs] (2016).

Wattenberg, M.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” arXiv:1603.04467 [cs] (2016).

Wicke, M.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” arXiv:1603.04467 [cs] (2016).

Winter, M.

W. Freude, R. Schmogrow, B. Nebendahl, M. Winter, A. Josten, D. Hillerkuss, S. Koenig, J. Meyer, M. Dreschmann, M. Huebner, C. Koos, J. Becker, and J. Leuthold, “Quality metrics for optical signals: Eye diagram, Q-factor, OSNR, EVM and BER,” in 2012 14th International Conference on Transparent Optical Networks (ICTON) (2012), pp. 1–4.

Wu, Q.

Xie, S.

Z. Yu, H. Chen, M. Chen, S. Yang, and S. Xie, “Bandwidth Improvement Using Adaptive Loading Scheme in Optical Direct-Detection OFDM,” IEEE J. Quantum Electron. 52(10), 1–6 (2016).
[Crossref]

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, Z.

Yan, L.

Yang, A.

Yang, H.

Yang, S.

Z. Yu, H. Chen, M. Chen, S. Yang, and S. Xie, “Bandwidth Improvement Using Adaptive Loading Scheme in Optical Direct-Detection OFDM,” IEEE J. Quantum Electron. 52(10), 1–6 (2016).
[Crossref]

Yi, A.

Yin, F.

Yu, C.

Yu, P. S.

M. Long, Z. Cao, J. Wang, and P. S. Yu, “Learning Multiple Tasks with Multilinear Relationship Networks,” arXiv:1506.02117 [cs] (2015).

Yu, Y.

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 23(23), 30337 (2015).
[Crossref]

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” arXiv:1603.04467 [cs] (2016).

Yu, Z.

Z. Wan, Z. Yu, L. Shu, Y. Zhao, H. Zhang, and K. Xu, “Intelligent optical performance monitor using multi-task learning based artificial neural network,” Opt. Express 27(8), 11281 (2019).
[Crossref]

Z. Yu, H. Chen, M. Chen, S. Yang, and S. Xie, “Bandwidth Improvement Using Adaptive Loading Scheme in Optical Direct-Detection OFDM,” IEEE J. Quantum Electron. 52(10), 1–6 (2016).
[Crossref]

Zhang, H.

Zhang, M.

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]

Zhang, Z.

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]

Zhao, Y.

Zheng, X.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” arXiv:1603.04467 [cs] (2016).

Zhong, K.

Zhou, X.

Zhou, Y.

Zhu, D.

Zhuge, Q.

K. Roberts, Q. Zhuge, I. Monga, S. Gareau, and C. Laperle, “Beyond 100 Gb/s: Capacity, Flexibility, and Network Optimization,” J. Opt. Commun. Netw. 9(4), C12–C24 (2017).
[Crossref]

Q. Zhuge and W. Hu, “Application of Machine Learning in Elastic Optical Networks,” in 2018 European Conference on Optical Communication (ECOC) (IEEE, 2018), pp. 1–3.

IEEE J. Quantum Electron. (1)

Z. Yu, H. Chen, M. Chen, S. Yang, and S. Xie, “Bandwidth Improvement Using Adaptive Loading Scheme in Optical Direct-Detection OFDM,” IEEE J. Quantum Electron. 52(10), 1–6 (2016).
[Crossref]

IEEE Photonics J. (1)

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]

J. Lightwave Technol. (2)

J. Opt. Commun. Netw. (1)

Opt. Express (11)

Z. Wang, A. Yang, P. Guo, and P. He, “OSNR and nonlinear noise power estimation for optical fiber communication systems using LSTM based deep learning technique,” Opt. Express 26(16), 21346 (2018).
[Crossref]

A. Yi, L. Yan, H. Liu, L. Jiang, Y. Pan, B. Luo, and W. Pan, “Modulation format identification and OSNR monitoring using density distributions in Stokes axes for digital coherent receivers,” Opt. Express 27(4), 4471 (2019).
[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 23(23), 30337 (2015).
[Crossref]

D. Wang, M. Wang, M. Zhang, Z. Zhang, H. Yang, J. Li, J. Li, and X. Chen, “Cost-effective and data size–adaptive OPM at intermediated node using convolutional neural network-based image processor,” Opt. Express 27(7), 9403 (2019).
[Crossref]

W. Zhang, D. Zhu, Z. He, N. Zhang, X. Zhang, H. Zhang, and Y. Li, “Identifying modulation formats through 2D Stokes planes with deep neural networks,” Opt. Express 26(18), 23507 (2018).
[Crossref]

L. Baker-Meflah, B. Thomsen, J. Mitchell, and P. Bayvel, “Simultaneous chromatic dispersion, polarization-mode-dispersion and OSNR monitoring at 40Gbit/s,” Opt. Express 16(20), 15999–16004 (2008).
[Crossref]

Z. Dong, A. P. T. Lau, and C. Lu, “OSNR monitoring for QPSK and 16-QAM systems in presence of fiber nonlinearities for digital coherent receivers,” Opt. Express 20(17), 19520 (2012).
[Crossref]

S. M. Bilal, G. Bosco, Z. Dong, A. P. T. Lau, and C. Lu, “Blind modulation format identification for digital coherent receivers,” Opt. Express 23(20), 26769 (2015).
[Crossref]

S. Fu, Z. Xu, J. Lu, H. Jiang, Q. Wu, Z. Hu, M. Tang, D. Liu, and C. C.-K. Chan, “Modulation format identification enabled by the digital frequency-offset loading technique for hitless coherent transceiver,” Opt. Express 26(6), 7288 (2018).
[Crossref]

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 25(15), 17767 (2017).
[Crossref]

Z. Wan, Z. Yu, L. Shu, Y. Zhao, H. Zhang, and K. Xu, “Intelligent optical performance monitor using multi-task learning based artificial neural network,” Opt. Express 27(8), 11281 (2019).
[Crossref]

Proc. IEEE (1)

I. Tomkos, S. Azodolmolky, J. Solé-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]

Other (10)

R. Cipolla, Y. Gal, and A. Kendall, “Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (IEEE, 2018), pp. 7482–7491.

A. T. Le and K. Araki, “A group of modulation schemes for adaptive modulation,” in 2008 11th IEEE Singapore International Conference on Communication Systems (IEEE, 2008), pp. 864–869.

R. A. Caruana, “Multitask Learning: A Knowledge-Based Source of Inductive Bias,” in Machine Learning Proceedings 1993 (Elsevier, 1993), pp. 41–48.

M. Long, Z. Cao, J. Wang, and P. S. Yu, “Learning Multiple Tasks with Multilinear Relationship Networks,” arXiv:1506.02117 [cs] (2015).

A. Kendall and Y. Gal, “What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?” arXiv:1703.04977 [cs] (2017).

D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” arXiv:1412.6980 [cs] (2014).

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” arXiv:1603.04467 [cs] (2016).

Cisco, “Cisco visual networking index: forecast and trends, 2017-2022,” (Cisco White Paper, 2019). https://www.cisco.com/c/en/us/solutions/collateral/service-provider/global-cloud-index-gci/white-paper-c11-738085.pdf .

Q. Zhuge and W. Hu, “Application of Machine Learning in Elastic Optical Networks,” in 2018 European Conference on Optical Communication (ECOC) (IEEE, 2018), pp. 1–3.

W. Freude, R. Schmogrow, B. Nebendahl, M. Winter, A. Josten, D. Hillerkuss, S. Koenig, J. Meyer, M. Dreschmann, M. Huebner, C. Koos, J. Becker, and J. Leuthold, “Quality metrics for optical signals: Eye diagram, Q-factor, OSNR, EVM and BER,” in 2012 14th International Conference on Transparent Optical Networks (ICTON) (2012), pp. 1–4.

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Figures (12)

Fig. 1.
Fig. 1. Nine schematic constellation diagrams of modulation format adaptive M-QAM scheme.
Fig. 2.
Fig. 2. Modulation format adaptive M-QAM schemes. (a) Heat map of constellation diagrams at X-polarization with an OSNR of 25 dB after polarization de-multiplexing, (b) Amplitude histograms (AHs) of constellation diagrams.
Fig. 3.
Fig. 3. AHs after curve fitting for nine modulation formats at different OSNR (10, 15, 20, 25 dB for QPSK, 6-QAM, 8-QAM, 12-QAM; 15, 20, 25, 30 dB for 16-QAM, 24-QAM; 20, 25, 30, 35 dB for 32-QAM, 48-QAM, 64-QAM).
Fig. 4.
Fig. 4. Schematic structure of loss weight adaptive MTL-ANN.
Fig. 5.
Fig. 5. (a) Experimental setup of coherent PDM system with nine modulation format adaptive M-QAM signals, (b) DSP configuration with two proposed OPM.
Fig. 6.
Fig. 6. (a) OSNR estimated RMSE versus neurons in shared hidden layer for loss weight adaptive MTL-ANN, (b) True OSNRs versus estimated OSNRs of loss weight adaptive MTL-ANN (Average, maximum and minimum value from five random initialization).
Fig. 7.
Fig. 7. OSNR accuracy versus hidden neurons in shared hidden layer for loss weight fixed and adaptive MTL-ANN (Average, maximum and minimum value from five random initialization).
Fig. 8.
Fig. 8. (a) MFI accuracy and (b) OSNR accuracy versus epochs for loss weight fixed and adaptive MTL-ANN (Average, maximum and minimum value from five random initialization).
Fig. 9.
Fig. 9. OSNR accuracy for different modulation format in loss weight adaptive MTL-ANN (Average value from five random initialization).
Fig. 10.
Fig. 10. MFI accuracy and OSNR accuracy versus epochs for loss weight adaptive MTL-ANN without regularization term (Average, maximum and minimum value from five random initialization).
Fig. 11.
Fig. 11. OSNR accuracy versus loss weight ratio (OSNR to MFI) for loss weight fixed MTL-ANN in simulation.
Fig. 12.
Fig. 12. OSNR accuracy versus data number in test set for loss weight adaptive MTL-ANN in simulation (Average, maximum and minimum value from five random initialization).

Tables (3)

Tables Icon

Table 1. MFI accuracy for different modulation formats

Tables Icon

Table 2. Comparison of regression and classification problem for OSNR monitoring

Tables Icon

Table 3. Loss weight ratio for loss weight adaptive MTL-ANN in random initialization

Equations (9)

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p ( y | f W ( x ) ) = N ( f W ( x ) , σ 2 ) = 1 σ 2 π exp ( ( y f W ( x ) ) 2 2 σ 2 )
p ( y = c | f W ( x ) , σ ) = Softmax ( 1 σ 2 f W ( x ) ) = exp ( 1 σ 2 f c W ( x ) ) c exp ( 1 σ 2 f c W ( x ) )
p ( y 1 , , y K | f W ( x ) ) = p ( y 1 | f W ( x ) ) p ( y K | f W ( x ) )
L ( W ) = ln p ( y | f W ( x ) )
L ( W, σ 1 , σ 2 ) = ln p ( y 1 = c , y 2 | f W ( x ) ) = ln p ( y 1 = c | f W ( x ) ) ln p ( y 2 | f W ( x ) ) 1 σ 1 2 f c W ( x ) + ln ( c exp ( 1 σ 1 2 f c W ( x ) ) ) + 1 2 σ 2 2 | | y 2 f W ( x ) | | 2 + ln ( σ 2 )
L ( W, σ 1 , σ 2 ) = 1 σ 1 2 L 1 ( W ) + 1 2 σ 2 2 L 2 ( W ) + ln ( σ 2 ) 1 σ 1 2 ln ( c exp ( f c W ( x ) ) ) + ln ( c exp ( 1 σ 1 2 f c W ( x ) ) ) = 1 σ 1 2 L 1 ( W ) + 1 2 σ 2 2 L 2 ( W ) + ln ( σ 2 ) + ln ( c exp ( 1 σ 1 2 f c W ( x ) ) ( c exp ( f c W ( x ) ) ) 1 σ 1 2 ) 1 σ 1 2 L 1 ( W ) + 1 2 σ 2 2 L 2 ( W ) + ln ( σ 1 ) + ln ( σ 2 )
L ( W ) = i = 1 K ( 1 σ i 2 L i ( W ) + ln ( σ i 2 ) )
l r d e c a y = l r i n i t i a l l r f i n i a l e p o c h s
RMSE ( f ( x [ n ] ) ,   y [ n ] ) = 1 N n = 1 N { f ( x [ n ] ) y [ n ] } 2

Metrics