Abstract

A cost-effective and data size-adaptive optical performance monitoring (OPM) scheme is proposed, which is based on asynchronous delay-tap plot (ADTP) using convolutional neural network (CNN) from the perspective of image processing. First, we design an OPM framework, based on the electrical domain-processing technique for the future optical networks. These networks include coherent detection-based end-to-end channel monitoring at destination node and direct detection-based transmission link monitoring at intermediate node. Aiming at the link monitoring, CNN is applied to recognize and analyze ADTP images that are converted from two-dimension (2D) digital vectors, so that adaptive to the stable algorithm structure. In simulation system, three high-order modulation formats, 16 quadrature amplitude modulation (QAM), 32QAM, 64QAM, are investigated for optical signal-to-noise ratio (OSNR) estimation and modulation format identification (MFI). The 100% accuracies under different chromatic dispersions (CDs) at different iteration epochs are obtained. Compared with asynchronous amplitude histograms (AAH)-based method, the better accuracy and faster convergence rate are achieved, especially in terms of strong CDs. Additionally, the experimental system is also conducted of 16QAM and 64QAM signals. Based on the partially-trained CNN model from simulation, the OSNR estimation accuracies of 16QAM and 64QAM are 97.81% and 96.56%, respectively. The maximum standard deviation is less than 0.45 dB and the MFI accuracies is 99.84%, presenting the satisfactory results and proving the feasibility of ADTP-based image processor for link monitoring at intermediate nodes.

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

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References

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2019 (1)

2018 (4)

2017 (3)

2016 (3)

2015 (1)

2014 (1)

2012 (3)

2010 (1)

T. S. R. Shen, K. Meng, A. P. T. Lau, and Z. Y. Dong, “Optical performance monitoring using artificial neural network trained with asynchronous amplitude histograms,” IEEE Photonics Technol. Lett. 22(22), 1665–1667 (2010).
[Crossref]

2009 (1)

2008 (1)

B. Kozicki, O. Takuya, and T. Hidehiko, “Optical performance monitoring of phase-modulated signals using asynchronous amplitude histogram analysis,” IEEE J.lightw.technol 26(10), 1353–1361 (2008).
[Crossref]

2006 (1)

2003 (1)

2002 (1)

1997 (1)

S. Lawrence, C. L. Giles, and A. D. Back, “Face recognition: a convolutional neural-network approach,” IEEE Trans. Neural Netw. 8(1), 98–113 (1997).
[Crossref] [PubMed]

Al-Arashi, W. H.

Anderson, T. B.

S. D. Dods and T. B. Anderson, “Optical Performance Monitoring Technique Using Delay Tap Asynchronous Waveform Sampling,” in Proc. Optical Fiber Commun. Conf. (OFC), 2007, OThP5.

Back, A. D.

S. Lawrence, C. L. Giles, and A. D. Back, “Face recognition: a convolutional neural-network approach,” IEEE Trans. Neural Netw. 8(1), 98–113 (1997).
[Crossref] [PubMed]

Chen, W.

Chen, X.

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, X. Chen, D. Wang, M. Zhang, and Z. Li, “Modulation format recognition and OSNR estimation using CNN-based deep learning,” IEEE Photonics Technol. Lett. 29(19), 1667–1670 (2017).
[Crossref]

Choi, H. Y.

Chung, Y. C.

Cui, S.

G. Yin, S. Cui, C. Ke, and D. Liu, “Reference Optical Spectrum Based In-band OSNR Monitoring Method for EDFA Amplified Multi-span Optical Fiber Transmission System with Cascaded Filtering Effect,” IEEE Photonics J. 10(3), 1–10 (2018).
[Crossref]

Cui, Y.

D. Wang, M. Zhang, Z. Li, J. Li, M. Fu, Y. Cui, X. Chen, D. Wang, M. Zhang, and Z. Li, “Modulation format recognition and OSNR estimation using CNN-based deep learning,” IEEE Photonics Technol. Lett. 29(19), 1667–1670 (2017).
[Crossref]

Dods, S. D.

S. D. Dods and T. B. Anderson, “Optical Performance Monitoring Technique Using Delay Tap Asynchronous Waveform Sampling,” in Proc. Optical Fiber Commun. Conf. (OFC), 2007, OThP5.

Dong, Z.

Dong, Z. Y.

T. S. R. Shen, K. Meng, A. P. T. Lau, and Z. Y. Dong, “Optical performance monitoring using artificial neural network trained with asynchronous amplitude histograms,” IEEE Photonics Technol. Lett. 22(22), 1665–1667 (2010).
[Crossref]

Dreschmann, M.

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

Freude, W.

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

Fu, M.

D. Wang, M. Zhang, Z. Li, J. Li, M. Fu, Y. Cui, X. Chen, D. Wang, M. Zhang, and Z. Li, “Modulation format recognition and OSNR estimation using CNN-based deep learning,” IEEE Photonics Technol. Lett. 29(19), 1667–1670 (2017).
[Crossref]

Gao, M.

Giles, C. L.

S. Lawrence, C. L. Giles, and A. D. Back, “Face recognition: a convolutional neural-network approach,” IEEE Trans. Neural Netw. 8(1), 98–113 (1997).
[Crossref] [PubMed]

Han, J.

Hauske, F. N.

He, Z.

Hidehiko, T.

B. Kozicki, O. Takuya, and T. Hidehiko, “Optical performance monitoring of phase-modulated signals using asynchronous amplitude histogram analysis,” IEEE J.lightw.technol 26(10), 1353–1361 (2008).
[Crossref]

Hillerkuss, D.

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

Hinton, G. E.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in International Conference on Neural Information Processing Systems, 1097–1105 (2012).

Hoshida, T.

Huang, Z.

Huebner, M.

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

Ji, X.

Ji, Y.

Jones, R.

Josten, A.

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

Kato, T.

Ke, C.

G. Yin, S. Cui, C. Ke, and D. Liu, “Reference Optical Spectrum Based In-band OSNR Monitoring Method for EDFA Amplified Multi-span Optical Fiber Transmission System with Cascaded Filtering Effect,” IEEE Photonics J. 10(3), 1–10 (2018).
[Crossref]

Khan, F. N.

Koenig, S.

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

Kong, D.

Kozicki, B.

B. Kozicki, O. Takuya, and T. Hidehiko, “Optical performance monitoring of phase-modulated signals using asynchronous amplitude histogram analysis,” IEEE J.lightw.technol 26(10), 1353–1361 (2008).
[Crossref]

Krizhevsky, A.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in International Conference on Neural Information Processing Systems, 1097–1105 (2012).

Kuschnerov, M.

Lankl, B.

Lau, A. P.

Lau, A. P. T.

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), 17767–17776 (2017).
[Crossref] [PubMed]

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. 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), 19520–19534 (2012).
[Crossref] [PubMed]

F. N. Khan, T. S. R. Shen, Y. Zhou, A. P. T. Lau, and C. Lu, “Optical performance monitoring using artificial neural networks trained with empirical moments of asynchronously sampled signal amplitudes,” IEEE Photonics Technol. Lett. 24(12), 982–984 (2012).
[Crossref]

T. S. R. Shen, K. Meng, A. P. T. Lau, and Z. Y. Dong, “Optical performance monitoring using artificial neural network trained with asynchronous amplitude histograms,” IEEE Photonics Technol. Lett. 22(22), 1665–1667 (2010).
[Crossref]

Lawrence, S.

S. Lawrence, C. L. Giles, and A. D. Back, “Face recognition: a convolutional neural-network approach,” IEEE Trans. Neural Netw. 8(1), 98–113 (1997).
[Crossref] [PubMed]

Lee, J. H.

Lee, Y.

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, X. Chen, D. Wang, M. Zhang, and Z. Li, “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, Z. Li, J. Li, M. Fu, Y. Cui, X. Chen, D. Wang, M. Zhang, and Z. Li, “Modulation format recognition and OSNR estimation using CNN-based deep learning,” IEEE Photonics Technol. Lett. 29(19), 1667–1670 (2017).
[Crossref]

Lin, Y.

Liu, D.

G. Yin, S. Cui, C. Ke, and D. Liu, “Reference Optical Spectrum Based In-band OSNR Monitoring Method for EDFA Amplified Multi-span Optical Fiber Transmission System with Cascaded Filtering Effect,” IEEE Photonics J. 10(3), 1–10 (2018).
[Crossref]

Liu, J.

Z. Wang and J. Liu, “A review of object detection based on convolutional neural network,” in Control Conference, 11104–11109 (2017).

Lu, C.

Ma, Y.

Meng, K.

T. S. R. Shen, K. Meng, A. P. T. Lau, and Z. Y. Dong, “Optical performance monitoring using artificial neural network trained with asynchronous amplitude histograms,” IEEE Photonics Technol. Lett. 22(22), 1665–1667 (2010).
[Crossref]

Meyer, J.

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

Morikawa, H.

Nebendahl, B.

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

Piels, M.

Qiu, J.

Schäeffer, C. G.

Schmogrow, R.

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

Shake, I.

Shen, G.

Shen, T. S. R.

F. N. Khan, T. S. R. Shen, Y. Zhou, A. P. T. Lau, and C. Lu, “Optical performance monitoring using artificial neural networks trained with empirical moments of asynchronously sampled signal amplitudes,” IEEE Photonics Technol. Lett. 24(12), 982–984 (2012).
[Crossref]

T. S. R. Shen, K. Meng, A. P. T. Lau, and Z. Y. Dong, “Optical performance monitoring using artificial neural network trained with asynchronous amplitude histograms,” IEEE Photonics Technol. Lett. 22(22), 1665–1667 (2010).
[Crossref]

Shin, S. K.

Song, C.

Spinnler, B.

Sui, Q.

Sutskever, I.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in International Conference on Neural Information Processing Systems, 1097–1105 (2012).

Takara, H.

Takuya, O.

B. Kozicki, O. Takuya, and T. Hidehiko, “Optical performance monitoring of phase-modulated signals using asynchronous amplitude histogram analysis,” IEEE J.lightw.technol 26(10), 1353–1361 (2008).
[Crossref]

Tan, M. C.

Tanimura, T.

Tao Lau, A. P.

Tian, Y.

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, X. Chen, D. Wang, M. Zhang, and Z. Li, “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, Z. Li, J. Li, M. Fu, Y. Cui, X. Chen, D. Wang, M. Zhang, and Z. Li, “Modulation format recognition and OSNR estimation using CNN-based deep learning,” IEEE Photonics Technol. Lett. 29(19), 1667–1670 (2017).
[Crossref]

Wang, S.

Wang, Z.

Z. Wang and J. Liu, “A review of object detection based on convolutional neural network,” in Control Conference, 11104–11109 (2017).

Watanabe, S.

Winter, M.

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

Wu, J.

Yang, H.

Yin, G.

G. Yin, S. Cui, C. Ke, and D. Liu, “Reference Optical Spectrum Based In-band OSNR Monitoring Method for EDFA Amplified Multi-span Optical Fiber Transmission System with Cascaded Filtering Effect,” IEEE Photonics J. 10(3), 1–10 (2018).
[Crossref]

Yu, C.

Yu, M.

Zhang, H.

Zhang, J.

Zhang, M.

D. Wang, M. Zhang, Z. Li, J. Li, M. Fu, Y. Cui, X. Chen, D. Wang, M. Zhang, and Z. Li, “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, Z. Li, J. Li, M. Fu, Y. Cui, X. Chen, D. Wang, M. Zhang, and Z. Li, “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]

Zhang, N.

Zhang, W.

Zhang, X.

Zhao, Y.

Zhong, K.

Zhou, X.

Zhou, Y.

Zhu, D.

Zibar, D.

IEEE J.lightw.technol (1)

B. Kozicki, O. Takuya, and T. Hidehiko, “Optical performance monitoring of phase-modulated signals using asynchronous amplitude histogram analysis,” IEEE J.lightw.technol 26(10), 1353–1361 (2008).
[Crossref]

IEEE Photonics J. (1)

G. Yin, S. Cui, C. Ke, and D. Liu, “Reference Optical Spectrum Based In-band OSNR Monitoring Method for EDFA Amplified Multi-span Optical Fiber Transmission System with Cascaded Filtering Effect,” IEEE Photonics J. 10(3), 1–10 (2018).
[Crossref]

IEEE Photonics Technol. Lett. (3)

T. S. R. Shen, K. Meng, A. P. T. Lau, and Z. Y. Dong, “Optical performance monitoring using artificial neural network trained with asynchronous amplitude histograms,” IEEE Photonics Technol. Lett. 22(22), 1665–1667 (2010).
[Crossref]

F. N. Khan, T. S. R. Shen, Y. Zhou, A. P. T. Lau, and C. Lu, “Optical performance monitoring using artificial neural networks trained with empirical moments of asynchronously sampled signal amplitudes,” IEEE Photonics Technol. Lett. 24(12), 982–984 (2012).
[Crossref]

D. Wang, M. Zhang, Z. Li, J. Li, M. Fu, Y. Cui, X. Chen, D. Wang, M. Zhang, and Z. Li, “Modulation format recognition and OSNR estimation using CNN-based deep learning,” IEEE Photonics Technol. Lett. 29(19), 1667–1670 (2017).
[Crossref]

IEEE Trans. Neural Netw. (1)

S. Lawrence, C. L. Giles, and A. D. Back, “Face recognition: a convolutional neural-network approach,” IEEE Trans. Neural Netw. 8(1), 98–113 (1997).
[Crossref] [PubMed]

J. Lightwave Technol. (6)

J. Opt. Commun. Netw. (3)

Opt. Express (7)

J. Zhang, W. Chen, M. Gao, Y. Ma, Y. Zhao, W. Chen, and G. Shen, “Intelligent adaptive coherent optical receiver based on convolutional neural network and clustering algorithm,” Opt. Express 26(14), 18684–18698 (2018).
[Crossref] [PubMed]

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Figures (15)

Fig. 1
Fig. 1 The proposed electrical domain-based OPM frame across the whole optical network: (a) heterogeneous optical network scenario; (b) end-to-end channel monitoring at destination node; (c) transmission link monitoring at intermediate node. CD-OPM: coherent detection-based OPM; DD-OPM: direct detection-based OPM.
Fig. 2
Fig. 2 The generation principle of AAH. Two 1D vectors of AAH with sizes of 1 × m and 1 × n requires different ANN structures, where mn.
Fig. 3
Fig. 3 The collected AAHs and ADTPs of 16QAM, 32QAM, and 64QAM at OSNRs of 20 dB, 25 dB, and 30 dB under CD of 640 ps/nm: (a) AAH; (b) ADTP.
Fig. 4
Fig. 4 The generation principle of ADTP. Two 2D vectors of ADTP with sizes of m × m and n × n are transformed into two images with the fixed pixel size of i × i, where mn.
Fig. 5
Fig. 5 CNN-based image processor for ADTP analysis. Input layer: ADTP images with pixel size of 32 × 32. Convolution layer 1(C1): sixteen 32 × 32 feature maps generated by sixteen 5 × 5 kernels. Pool layer (P1): sixteen 16 × 16 feature maps after subsampling from 2 × 2 region. C2: thirty-two 16 × 16 feature maps generated by thirty-two 5 × 5 kernels. P2: thirty-two 8 × 8 feature maps after subsampling from 2 × 2 region. Fully connected layers consisting of two layers with 512 and 256 neurons.
Fig. 6
Fig. 6 Simulation setup. TL: tunable laser; PRBS: pseudo-random binary sequence; EDFA: erbium-doped fiber amplifier; VOA: variable optical attenuator; OBPF: optical band pass filter; PD: photodetector.
Fig. 7
Fig. 7 Comparison among three ADTP images for 16QAM at OSNR of 25 dB.
Fig. 8
Fig. 8 (a) Accuracy of OSNR estimation as a function of epochs under CDs of 0, 1280, and 1920 ps/nm; (b) accuracy of OSNR estimation as a function of epochs with different training data quantities: 200, 400, and 600 for each OSNR.
Fig. 9
Fig. 9 Comparison between ADTP with CNN and AAH with DNN: (a) accuracy of OSNR estimation as a function of epochs; mean value and standard deviation of estimated OSNR based on (b) AAH with DNN and (c) ADTP with CNN.
Fig. 10
Fig. 10 The OSNR accuracies as a function of epochs after 120 km fiber transmission: (a) 16QAM; (b) 32QAM; (c) 64QAM; (d) combined three formats together.
Fig. 11
Fig. 11 The OSNR accuracies as a function of epochs after 120 km fiber transmission at different sampling rates for 16QAM: (a) 1/4 symbol rate; (b) 1/10 symbol rate; (c) 1/20 symbol rate.
Fig. 12
Fig. 12 The accuracies of MFI at different epochs for both training and testing process: (a) small data size composed of 2240 training data and 960 testing data; (b) large data size composed of 6720 training data and 2880 testing data.
Fig. 13
Fig. 13 Experimental setup. ECL: external cavity laser; AWG: arbitrary waveform generator; ASE: amplified spontaneous emission; OC: optical coupler; LO: local oscillation; A/D: analog-to-digital converter; OSC: oscilloscope; GPU: graphics processing unit.
Fig. 14
Fig. 14 Experimental results for ADTP-based OSNR estimation by CNN: (a) 16QAM; (b) 64QAM.
Fig. 15
Fig. 15 The detailed internal architecture of CNN, and all the feature maps are displayed for input layer, convolution layers, pooling layer, and fully-connected layer.

Tables (2)

Tables Icon

Table 1 Testing accuracies of ADTP-based MFI technique using CNN with training data size of 2240 and testing data size of 960 for each format.

Tables Icon

Table 2 Testing accuracies of ADTP-based MFI technique using CNN with training data size of 6720 and testing data size of 2880 for each format.

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