Abstract

An intelligent constellation diagram analyzer is proposed to implement both modulation format recognition (MFR) and optical signal-to-noise rate (OSNR) estimation by using convolution neural network (CNN)-based deep learning technique. With the ability of feature extraction and self-learning, CNN can process constellation diagram in its raw data form (i.e., pixel points of an image) from the perspective of image processing, without manual intervention nor data statistics. The constellation diagram images of six widely-used modulation formats over a wide OSNR range (15~30 dB and 20~35 dB) are obtained from a constellation diagram generation module in oscilloscope. Both simulation and experiment are conducted. Compared with other 4 traditional machine learning algorithms, CNN achieves the better accuracies and is obviously superior to other methods at the cost of O(n) computation complexity and less than 0.5 s testing time. For OSNR estimation, the high accuracies are obtained at epochs of 200 (95% for 64QAM, and over 99% for other five formats); for MFR, 100% accuracies are achieved even with less training data at lower epochs. The experimental results show that the OSNR estimation errors for all the signals are less than 0.7 dB. Additionally, the effects of multiple factors on CNN performance are comprehensively investigated, including the training data size, image resolution, and network structure. The proposed technique has the potential to be embedded in the test instrument to perform intelligent signal analysis or applied for optical performance monitoring.

© 2017 Optical Society of America

Full Article  |  PDF Article
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[Crossref]

2016 (7)

D. Wang, M. Zhang, Z. Cai, Y. Cui, Z. Li, H. Han, M. Fu, and B. Luo, “Combatting nonlinear phase noise in coherent optical systems with an optimized decision processor based on machine learning,” Opt. Commun. 369, 199–208 (2016).
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[Crossref]

D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis, “Mastering the game of Go with deep neural networks and tree search,” Nature 529(7587), 484–489 (2016).
[Crossref] [PubMed]

F. N. Khan, K. Zhong, W. H. Al-Arashi, C. Yu, C. Lu, and A. P. T. Lau, “Modulation Format Identification in Coherent Receivers Using Deep Machine Learning,” IEEE Photonics Technol. Lett. 28(17), 1886–1889 (2016).
[Crossref]

S. Amiralizadeh, A. Yekani, and L. A. Rusch, “Discrete multi-tone transmission with optimized QAM constellations for short-reach optical communications,” J. Lightwave Technol. 34(15), 3515–3522 (2016).
[Crossref]

D. Zibar, M. Piels, R. Jones, and C. G. Schäeffer, “Machine learning techniques in optical communication,” J. Lightwave Technol. 34(6), 1442–1452 (2016).
[Crossref]

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]

2015 (3)

A. Abdiansah and R. Wardoyo, “Time Complexity Analysis of Support Vector Machines (SVM) in LibSVM,” Int. J. Comput. Appl. 128(3), 1–7 (2015).

J. Schmidhuber, “Deep learning in neural networks: an overview,” Neural Netw. 61, 85–117 (2015).
[Crossref] [PubMed]

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015).
[Crossref] [PubMed]

2014 (2)

2012 (4)

G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, and B. Kingsbury, “Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups,” IEEE Signal Process. Mag. 29(6), 82–97 (2012).
[Crossref]

D. Zibar, O. Winther, N. Franceschi, R. Borkowski, A. Caballero, V. Arlunno, M. N. Schmidt, N. G. Gonzales, B. Mao, Y. Ye, K. J. Larsen, and I. T. Monroy, “Nonlinear impairment compensation using expectation maximization for dispersion managed and unmanaged PDM 16-QAM transmission,” Opt. Express 20(26), B181–B196 (2012).
[Crossref] [PubMed]

R. Schmogrow, B. Nebendahl, M. Winter, A. Josten, D. Hillerkuss, S. Koenig, J. Meyer, M. Dreschmann, M. Huebner, C. Koos, J. Becker, W. Freude, and J. Leuthold, “Error vector magnitude as a performance measure for advanced modulation formats,” IEEE Photonics Technol. Lett. 24(1), 61–63 (2012).
[Crossref]

P. J. Winzer, “High-spectral-efficiency optical modulation formats,” J. Lightwave Technol. 30(24), 3824–3835 (2012).
[Crossref]

2010 (2)

N. G. Gonzalez, D. Zibar, A. Caballero, and I. T. Monroy, “Experimental 2.5-Gb/s QPSK WDM Phase-Modulated Radio-Over-Fiber Link With Digital Demodulation by a K -Means Algorithm,” IEEE Photonics Technol. Lett. 22(5), 335–337 (2010).
[Crossref]

F. N. Khan, A. P. T. Lau, Z. Li, C. Lu, and P. K. A. Wai, “Statistical Analysis of Optical Signal-to-Noise Ratio Monitoring Using Delay-Tap Sampling,” IEEE Photonics Technol. Lett. 22(3), 149–151 (2010).
[Crossref]

2009 (1)

2008 (1)

2005 (1)

G. Breed, “Analyzing signals using the eye diagram,” High Freq. Electron. 4(11), 50–53 (2005).

2004 (1)

Abdiansah, A.

A. Abdiansah and R. Wardoyo, “Time Complexity Analysis of Support Vector Machines (SVM) in LibSVM,” Int. J. Comput. Appl. 128(3), 1–7 (2015).

Al-Arashi, W. H.

F. N. Khan, K. Zhong, W. H. Al-Arashi, C. Yu, C. Lu, and A. P. T. Lau, “Modulation Format Identification in Coherent Receivers Using Deep Machine Learning,” IEEE Photonics Technol. Lett. 28(17), 1886–1889 (2016).
[Crossref]

M. C. Tan, F. N. Khan, W. H. Al-Arashi, Y. Zhou, and A. P. T. Lau, “Simultaneous optical performance monitoring and modulation format/bit-rate identification using principal component analysis,” J. Opt. Commun. Netw. 6(5), 441–448 (2014).
[Crossref]

Amiralizadeh, S.

Andrekson, P. A.

Antonoglou, I.

D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis, “Mastering the game of Go with deep neural networks and tree search,” Nature 529(7587), 484–489 (2016).
[Crossref] [PubMed]

Arlunno, V.

Bach, R.

Becker, J.

R. Schmogrow, B. Nebendahl, M. Winter, A. Josten, D. Hillerkuss, S. Koenig, J. Meyer, M. Dreschmann, M. Huebner, C. Koos, J. Becker, W. Freude, and J. Leuthold, “Error vector magnitude as a performance measure for advanced modulation formats,” IEEE Photonics Technol. Lett. 24(1), 61–63 (2012).
[Crossref]

Bengio, Y.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015).
[Crossref] [PubMed]

Blumenthal, D.

Borkowski, R.

Breed, G.

G. Breed, “Analyzing signals using the eye diagram,” High Freq. Electron. 4(11), 50–53 (2005).

Caballero, A.

D. Zibar, O. Winther, N. Franceschi, R. Borkowski, A. Caballero, V. Arlunno, M. N. Schmidt, N. G. Gonzales, B. Mao, Y. Ye, K. J. Larsen, and I. T. Monroy, “Nonlinear impairment compensation using expectation maximization for dispersion managed and unmanaged PDM 16-QAM transmission,” Opt. Express 20(26), B181–B196 (2012).
[Crossref] [PubMed]

N. G. Gonzalez, D. Zibar, A. Caballero, and I. T. Monroy, “Experimental 2.5-Gb/s QPSK WDM Phase-Modulated Radio-Over-Fiber Link With Digital Demodulation by a K -Means Algorithm,” IEEE Photonics Technol. Lett. 22(5), 335–337 (2010).
[Crossref]

Cai, Z.

D. Wang, M. Zhang, Z. Cai, Y. Cui, Z. Li, H. Han, M. Fu, and B. Luo, “Combatting nonlinear phase noise in coherent optical systems with an optimized decision processor based on machine learning,” Opt. Commun. 369, 199–208 (2016).
[Crossref]

D. Wang, M. Zhang, M. Fu, Z. Cai, Z. Li, H. Han, Y. Cui, and B. Luo, “Nonlinearity Mitigation Using a Machine Learning Detector Based on k-Nearest Neighbors,” IEEE Photonics Technol. Lett. 28(19), 2102–2105 (2016).
[Crossref]

Chen, X.

D. Wang, M. Zhang, Z. Li, C. Song, M. Fu, J. Li, and X. Chen, “System impairment compensation in coherent optical communications by using a bio-inspired detector based on artificial neural network and genetic algorithm,” Opt. Commun. 399, 1–12 (2017).
[Crossref]

Cui, Y.

D. Wang, M. Zhang, M. Fu, Z. Cai, Z. Li, H. Han, Y. Cui, and B. Luo, “Nonlinearity Mitigation Using a Machine Learning Detector Based on k-Nearest Neighbors,” IEEE Photonics Technol. Lett. 28(19), 2102–2105 (2016).
[Crossref]

D. Wang, M. Zhang, Z. Cai, Y. Cui, Z. Li, H. Han, M. Fu, and B. Luo, “Combatting nonlinear phase noise in coherent optical systems with an optimized decision processor based on machine learning,” Opt. Commun. 369, 199–208 (2016).
[Crossref]

Dahl, G. E.

G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, and B. Kingsbury, “Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups,” IEEE Signal Process. Mag. 29(6), 82–97 (2012).
[Crossref]

Deng, L.

G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, and B. Kingsbury, “Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups,” IEEE Signal Process. Mag. 29(6), 82–97 (2012).
[Crossref]

Dieleman, S.

D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis, “Mastering the game of Go with deep neural networks and tree search,” Nature 529(7587), 484–489 (2016).
[Crossref] [PubMed]

Diniz, J. C.

Djordjevic, I. B.

Dong, Z.

Dreschmann, M.

R. Schmogrow, B. Nebendahl, M. Winter, A. Josten, D. Hillerkuss, S. Koenig, J. Meyer, M. Dreschmann, M. Huebner, C. Koos, J. Becker, W. Freude, and J. Leuthold, “Error vector magnitude as a performance measure for advanced modulation formats,” IEEE Photonics Technol. Lett. 24(1), 61–63 (2012).
[Crossref]

Einstein, D.

Franceschi, N.

Freude, W.

R. Schmogrow, B. Nebendahl, M. Winter, A. Josten, D. Hillerkuss, S. Koenig, J. Meyer, M. Dreschmann, M. Huebner, C. Koos, J. Becker, W. Freude, and J. Leuthold, “Error vector magnitude as a performance measure for advanced modulation formats,” IEEE Photonics Technol. Lett. 24(1), 61–63 (2012).
[Crossref]

Fu, M.

D. Wang, M. Zhang, Z. Li, C. Song, M. Fu, J. Li, and X. Chen, “System impairment compensation in coherent optical communications by using a bio-inspired detector based on artificial neural network and genetic algorithm,” Opt. Commun. 399, 1–12 (2017).
[Crossref]

D. Wang, M. Zhang, M. Fu, Z. Cai, Z. Li, H. Han, Y. Cui, and B. Luo, “Nonlinearity Mitigation Using a Machine Learning Detector Based on k-Nearest Neighbors,” IEEE Photonics Technol. Lett. 28(19), 2102–2105 (2016).
[Crossref]

D. Wang, M. Zhang, Z. Cai, Y. Cui, Z. Li, H. Han, M. Fu, and B. Luo, “Combatting nonlinear phase noise in coherent optical systems with an optimized decision processor based on machine learning,” Opt. Commun. 369, 199–208 (2016).
[Crossref]

Gonzales, N. G.

Gonzalez, N. G.

N. G. Gonzalez, D. Zibar, A. Caballero, and I. T. Monroy, “Experimental 2.5-Gb/s QPSK WDM Phase-Modulated Radio-Over-Fiber Link With Digital Demodulation by a K -Means Algorithm,” IEEE Photonics Technol. Lett. 22(5), 335–337 (2010).
[Crossref]

Graepel, T.

D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis, “Mastering the game of Go with deep neural networks and tree search,” Nature 529(7587), 484–489 (2016).
[Crossref] [PubMed]

Grewe, D.

D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis, “Mastering the game of Go with deep neural networks and tree search,” Nature 529(7587), 484–489 (2016).
[Crossref] [PubMed]

Guez, A.

D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis, “Mastering the game of Go with deep neural networks and tree search,” Nature 529(7587), 484–489 (2016).
[Crossref] [PubMed]

Han, H.

D. Wang, M. Zhang, Z. Cai, Y. Cui, Z. Li, H. Han, M. Fu, and B. Luo, “Combatting nonlinear phase noise in coherent optical systems with an optimized decision processor based on machine learning,” Opt. Commun. 369, 199–208 (2016).
[Crossref]

D. Wang, M. Zhang, M. Fu, Z. Cai, Z. Li, H. Han, Y. Cui, and B. Luo, “Nonlinearity Mitigation Using a Machine Learning Detector Based on k-Nearest Neighbors,” IEEE Photonics Technol. Lett. 28(19), 2102–2105 (2016).
[Crossref]

Hassabis, D.

D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis, “Mastering the game of Go with deep neural networks and tree search,” Nature 529(7587), 484–489 (2016).
[Crossref] [PubMed]

Hillerkuss, D.

R. Schmogrow, B. Nebendahl, M. Winter, A. Josten, D. Hillerkuss, S. Koenig, J. Meyer, M. Dreschmann, M. Huebner, C. Koos, J. Becker, W. Freude, and J. Leuthold, “Error vector magnitude as a performance measure for advanced modulation formats,” IEEE Photonics Technol. Lett. 24(1), 61–63 (2012).
[Crossref]

Hinton, G.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015).
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Figures (16)

Fig. 1
Fig. 1 The relationship among artificial intelligent, machine learning, and deep learning. CNN: convolutional neural network; RNN: recurrent neural network; RBM: restricted Boltzmann machine.
Fig. 2
Fig. 2 Schematic diagram of CNN-based constellation diagram analyzer. Input layer: constellation diagram images with pixel size of 28 × 28. Convolution layer 1 (C1): six 24 × 24 feature maps generated by six 5 × 5 kernels. Pooling layer 1 (P1): six 12 × 12 feature maps after subsampling from 2 × 2 region. C2: twelve 8 × 8 feature maps generated by twelve 5 × 5 kernels. P2: twelve 4 × 4 feature maps after subsampling from 2 × 2 region. Fully connected layer 1 (F1): 192 nodes transformed from the all pixels of P2, i.e., 12 × 4 × 4 = 192. Output layer fully connected with all nodes of F1 and consisting of 22 nodes, among which 6 nodes for modulation format recognition and 16 nodes for OSNR estimation. Note that all the activation functions in the CNN are sigmoid function.
Fig. 3
Fig. 3 (a) Schematic diagram of 2D convolution: a 2 × 2 kernel convolves with a 3 × 4 input image to produce a 2 × 3 feature map [32]; (b) max pooling: a 4 × 4 convolved feature map is divided into four disjoint 2 × 2 regions, and take the maximum of each region to generate a 2 × 2 pooled feature map.
Fig. 4
Fig. 4 The constellation diagram images of QPSK and 16QAM signals at OSNR of 15, 20, 25 dB. At the same position of each constellation image, a 5 × 5 region is extracted and enlarged.
Fig. 5
Fig. 5 Simulation setup. CW: continuous wave; PRBS: pseudo-random binary sequence; EDFA: erbium-doped fiber amplifier; VOA: variable optical attenuator; ASE: amplified spontaneous emission; OC: optical coupler; OBPF: optical band pass filter; LO: local oscillation; A/D: analog-to-digital converter.
Fig. 6
Fig. 6 The collected constellation diagram images of QPSK, 8PSK, 8QAM, 16QAM, 32QAM, 64QAM at 16 OSNR values ranging from 15 to 30 dB at the step of 1 dB (except for 64QAM, which requires higher OSNR values ranging from 20 to 35 dB).
Fig. 7
Fig. 7 Accuracy of OSNR estimation as a function of epochs for QPSK, 8PSK, 8QAM, 16QAM, 32QAM, 64QAM.
Fig. 8
Fig. 8 Performance comparison between CNN and other four machine algorithms: decision tree: maximum number of splits is 100; BP-ANN: percentage of training data is 70%, activation function is sigmoid function, maximum failure number is 3, numbers of neurons in input, hidden, and out layers are 784, 50, 16; DW-KNN: number of neighbors is10, distance weight: squared inverse; SVM: kernel is radial basis function (RBF), multiclass method is one to all, kernel scale is 10; CNN: number of epochs is fixed as 200, other parameters are kept as above.
Fig. 9
Fig. 9 The percentages of error samples at each OSNR for (a) QPSK, (b) 8PSK, (c) 8QAM, (d) 16QAM, (e) 32QAM, and (f) 64QAM.
Fig. 10
Fig. 10 Confusion matrix for MFR under different training data, the epoch is set to 1 and the test data is kept as 1600: (a) training data of each format is 800, 7.6% 16QAM misclassified as 64QAM; (b) training data is 1600 and the total accuracy is 100%.
Fig. 11
Fig. 11 The MFR accuracies at different epochs for different training data sizes. The training data sizes of each format are 160, 200, 320, 400, and 800.
Fig. 12
Fig. 12 (a) The OSNR estimation accuracy at different epochs for input images with different resolutions of 16 × 16, 28 × 28, 40 × 40, 56 × 56, 84 × 84; (b) for the resolution of 84 × 84, the OSNR estimation accuracy at different epochs for the subsample scales of 4 and 2 in pooling layers.
Fig. 13
Fig. 13 The OSNR estimation accuracy at different epochs with different network structures of (3, 6), (6, 12), (12, 24), (24, 36): (a) QPSK and (b) 16QAM.
Fig. 14
Fig. 14 Experimental setup: ECL: external cavity laser; AWG: arbitrary waveform generator; EDFA: erbium-doped fiber amplifier; VOA: variable optical attenuator; ASE: amplified spontaneous emission; OC: optical coupler; LO: local oscillation; A/D: analog-to-digital converter; OSA: optical spectra analyzer; OSC: oscilloscope.
Fig. 15
Fig. 15 Experimental results for OSNR estimation by CNN: (a) QPSK and (b) 16QAM.
Fig. 16
Fig. 16 The feature maps generated in convolution layer1, pooling layer1, convolution layer2, and pooling layer2: (a) QSPK with OSNR of 20 dB; (b) 16QAM with OSNR of 20 dB.

Tables (2)

Tables Icon

Table 1 the model complexity of machine learning algorithms, n is the size of training data set

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Table 2 The training time and test time for different algorithms

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