Abstract

In a cognitive, heterogeneous, optical network, it would be important to identify physical layer information, especially the modulation formats of transmitted signals. The modulation format information is also indispensable for carrier-phase-recovery in a coherent optical receiver. Because constellation diagrams of modulation signals are susceptible to various noises, we utilize a convolutional neural network to process the amplitude data after the modulation-format-agnostic clock recovery. Furthermore, for the carrier-phase-recovered data, we use the clustering method based on a fast search and find the density peaks to classify the constellation clusters and use the k-nearest-neighbor method to label the samples. The proposed receiver system has a simple architecture to identify the modulation format based on the amplitude information and can track fast changes of the signals to improve the accuracy of the symbol decision. We have demonstrated this experimentally and have achieved remarkable BER improvement.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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References

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    [Crossref] [PubMed]
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    [Crossref]
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2018 (3)

I. A. Alimi, A. L. Teixeira, and P. P. Monteiro, “Toward an efficient C-RAN optical fronthaul for the future networks: A tutorial on technologies requirements challenges and solutions,” IEEE Comm. Surv. and Tutor. 20(1), 708–769 (2018).
[Crossref]

F. Liu, Y. Lin, Y. Yu, and L. P. Barry, “Parallelized kalman filters for mitigation of the excess phase noise of fast tunable lasers in coherent optical communication systems,” IEEE Photonics J. 10(1), 1–11 (2018).

W. Chen, J. Zhang, M. Gao, and G. Shen, “Performance improvement of 64-QAM coherent optical communication system by optimizing symbol decision boundary based on support vector machine,” Opt. Commun. 410, 1–7 (2018).
[Crossref]

2017 (6)

2016 (2)

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]

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]

2015 (3)

2014 (2)

2012 (2)

W. Wei, C. Wang, and J. Yu, “Cognitive optical networks: Key drivers, enabling techniques, and adaptive bandwidth services,” Commun. Mag. 50(1), 106–113 (2012).
[Crossref]

F. N. Khan, Y. Zhou, A. P. T. Lau, and C. Lu, “Modulation format identification in heterogeneous fiber-optic networks using artificial neural networks,” Opt. Express 20(11), 12422–12431 (2012).
[Crossref] [PubMed]

Adles, E. J.

Aguado, J. C.

Aguado, J.C.

E. Palkopoulou, I. Stiakogiannakis, D. Klonidis, T. Jiménez, N. Fernández, J.C. Aguado, J. López, Y. Ye, and I. Tomkos, “Cognitive Heterogeneous Reconfigurable Optical Network: A techno-economic evaluation,” in IEEE Future Network and Mobile Summit (2013), pp. 1–10.

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]

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), 30337–30346 (2015).
[Crossref] [PubMed]

Alimi, I. A.

I. A. Alimi, A. L. Teixeira, and P. P. Monteiro, “Toward an efficient C-RAN optical fronthaul for the future networks: A tutorial on technologies requirements challenges and solutions,” IEEE Comm. Surv. and Tutor. 20(1), 708–769 (2018).
[Crossref]

Barry, L. P.

F. Liu, Y. Lin, Y. Yu, and L. P. Barry, “Parallelized kalman filters for mitigation of the excess phase noise of fast tunable lasers in coherent optical communication systems,” IEEE Photonics J. 10(1), 1–11 (2018).

Ben Yoo, S. J.

Bilal, S. M.

Borkowski, R.

Bosco, G.

Caballero, A.

Cai, Z.

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]

Chan, V. W. S.

V. W. S. Chan and E. Jang, “Cognitive all-optical fiber network architecture,” in 2017 19th International Conference on Transparent Optical Networks (ICTON) (2017), pp. 1–4.
[Crossref]

Chen, W.

W. Chen, J. Zhang, M. Gao, and G. Shen, “Performance improvement of 64-QAM coherent optical communication system by optimizing symbol decision boundary based on support vector machine,” Opt. Commun. 410, 1–7 (2018).
[Crossref]

J. Zhang, W. Chen, M. Gao, and G. Shen, “K-means-clustering-based fiber nonlinearity equalization techniques for 64-QAM coherent optical communication system,” Opt. Express 25(22), 27570–27580 (2017).
[Crossref] [PubMed]

J. Zhang, M. Gao, W. Chen, and G. Shen, “Non-data-aided k-nearest neighbors technique for optical fiber nonlinearity mitigation,” J. Lightwave Technol., in press (2018).

Chen, X.

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

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

Cui, Y.

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

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]

Dennis, M. L.

Dong, Z.

Doran, N.

Durán, R. J.

Ellis, A.

Fernández, N.

R. Borkowski, R. J. Durán, C. Kachris, D. Siracusa, A. Caballero, N. Fernández, D. Klonidis, A. Francescon, T. Jiménez, J. C. Aguado, I. Miguel, E. Salvadori, I. Tomkos, R. M. Lorenzo, and I. T. Monroy, “Cognitive Optical Network Testbed: EU Project CHRON,” J. Opt. Commun. Netw. 7(2), A344–A355 (2015).
[Crossref]

E. Palkopoulou, I. Stiakogiannakis, D. Klonidis, T. Jiménez, N. Fernández, J.C. Aguado, J. López, Y. Ye, and I. Tomkos, “Cognitive Heterogeneous Reconfigurable Optical Network: A techno-economic evaluation,” in IEEE Future Network and Mobile Summit (2013), pp. 1–10.

Francescon, A.

Fu, M.

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

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]

Gao, M.

W. Chen, J. Zhang, M. Gao, and G. Shen, “Performance improvement of 64-QAM coherent optical communication system by optimizing symbol decision boundary based on support vector machine,” Opt. Commun. 410, 1–7 (2018).
[Crossref]

J. Zhang, W. Chen, M. Gao, and G. Shen, “K-means-clustering-based fiber nonlinearity equalization techniques for 64-QAM coherent optical communication system,” Opt. Express 25(22), 27570–27580 (2017).
[Crossref] [PubMed]

J. Zhang, M. Gao, W. Chen, and G. Shen, “Non-data-aided k-nearest neighbors technique for optical fiber nonlinearity mitigation,” J. Lightwave Technol., in press (2018).

Giacoumidis, E.

Guo, C.

Han, H.

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]

Jang, E.

V. W. S. Chan and E. Jang, “Cognitive all-optical fiber network architecture,” in 2017 19th International Conference on Transparent Optical Networks (ICTON) (2017), pp. 1–4.
[Crossref]

Jiménez, T.

R. Borkowski, R. J. Durán, C. Kachris, D. Siracusa, A. Caballero, N. Fernández, D. Klonidis, A. Francescon, T. Jiménez, J. C. Aguado, I. Miguel, E. Salvadori, I. Tomkos, R. M. Lorenzo, and I. T. Monroy, “Cognitive Optical Network Testbed: EU Project CHRON,” J. Opt. Commun. Netw. 7(2), A344–A355 (2015).
[Crossref]

E. Palkopoulou, I. Stiakogiannakis, D. Klonidis, T. Jiménez, N. Fernández, J.C. Aguado, J. López, Y. Ye, and I. Tomkos, “Cognitive Heterogeneous Reconfigurable Optical Network: A techno-economic evaluation,” in IEEE Future Network and Mobile Summit (2013), pp. 1–10.

Johnson, W. R.

Kachris, C.

Khan, F. N.

Klonidis, D.

R. Borkowski, R. J. Durán, C. Kachris, D. Siracusa, A. Caballero, N. Fernández, D. Klonidis, A. Francescon, T. Jiménez, J. C. Aguado, I. Miguel, E. Salvadori, I. Tomkos, R. M. Lorenzo, and I. T. Monroy, “Cognitive Optical Network Testbed: EU Project CHRON,” J. Opt. Commun. Netw. 7(2), A344–A355 (2015).
[Crossref]

E. Palkopoulou, I. Stiakogiannakis, D. Klonidis, T. Jiménez, N. Fernández, J.C. Aguado, J. López, Y. Ye, and I. Tomkos, “Cognitive Heterogeneous Reconfigurable Optical Network: A techno-economic evaluation,” in IEEE Future Network and Mobile Summit (2013), pp. 1–10.

Laio, A.

A. Rodriguez and A. Laio, “Machine learning. Clustering by fast search and find of density peaks,” Science 344(6191), 1492–1496 (2014).
[Crossref] [PubMed]

Lau, A. P. T.

Li, J.

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]

X. Mai, J. Liu, X. Wu, Q. Zhang, C. Guo, Y. Yang, and Z. Li, “Stokes space modulation format classification based on non-iterative clustering algorithm for coherent optical receivers,” Opt. Express 25(3), 2038–2050 (2017).
[Crossref] [PubMed]

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

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]

Lin, Y.

F. Liu, Y. Lin, Y. Yu, and L. P. Barry, “Parallelized kalman filters for mitigation of the excess phase noise of fast tunable lasers in coherent optical communication systems,” IEEE Photonics J. 10(1), 1–11 (2018).

Liu, F.

F. Liu, Y. Lin, Y. Yu, and L. P. Barry, “Parallelized kalman filters for mitigation of the excess phase noise of fast tunable lasers in coherent optical communication systems,” IEEE Photonics J. 10(1), 1–11 (2018).

Liu, G.

Liu, J.

López, J.

E. Palkopoulou, I. Stiakogiannakis, D. Klonidis, T. Jiménez, N. Fernández, J.C. Aguado, J. López, Y. Ye, and I. Tomkos, “Cognitive Heterogeneous Reconfigurable Optical Network: A techno-economic evaluation,” in IEEE Future Network and Mobile Summit (2013), pp. 1–10.

Lorenzo, R. M.

Lu, C.

Lu, H.

Luo, B.

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]

Mai, X.

McKenna, T. P.

Menyuk, C. R.

Mhatli, S.

Miguel, I.

Monroy, I. T.

Monteiro, P. P.

I. A. Alimi, A. L. Teixeira, and P. P. Monteiro, “Toward an efficient C-RAN optical fronthaul for the future networks: A tutorial on technologies requirements challenges and solutions,” IEEE Comm. Surv. and Tutor. 20(1), 708–769 (2018).
[Crossref]

Palkopoulou, E.

E. Palkopoulou, I. Stiakogiannakis, D. Klonidis, T. Jiménez, N. Fernández, J.C. Aguado, J. López, Y. Ye, and I. Tomkos, “Cognitive Heterogeneous Reconfigurable Optical Network: A techno-economic evaluation,” in IEEE Future Network and Mobile Summit (2013), pp. 1–10.

Proietti, R.

Rodriguez, A.

A. Rodriguez and A. Laio, “Machine learning. Clustering by fast search and find of density peaks,” Science 344(6191), 1492–1496 (2014).
[Crossref] [PubMed]

Salvadori, E.

Shen, G.

W. Chen, J. Zhang, M. Gao, and G. Shen, “Performance improvement of 64-QAM coherent optical communication system by optimizing symbol decision boundary based on support vector machine,” Opt. Commun. 410, 1–7 (2018).
[Crossref]

J. Zhang, W. Chen, M. Gao, and G. Shen, “K-means-clustering-based fiber nonlinearity equalization techniques for 64-QAM coherent optical communication system,” Opt. Express 25(22), 27570–27580 (2017).
[Crossref] [PubMed]

J. Zhang, M. Gao, W. Chen, and G. Shen, “Non-data-aided k-nearest neighbors technique for optical fiber nonlinearity mitigation,” J. Lightwave Technol., in press (2018).

Siracusa, D.

Sluz, J. E.

Song, C.

Sova, R. M.

Stephens, M.

Stiakogiannakis, I.

E. Palkopoulou, I. Stiakogiannakis, D. Klonidis, T. Jiménez, N. Fernández, J.C. Aguado, J. López, Y. Ye, and I. Tomkos, “Cognitive Heterogeneous Reconfigurable Optical Network: A techno-economic evaluation,” in IEEE Future Network and Mobile Summit (2013), pp. 1–10.

Tan, M. C.

Taylor, M. G.

Teixeira, A. L.

I. A. Alimi, A. L. Teixeira, and P. P. Monteiro, “Toward an efficient C-RAN optical fronthaul for the future networks: A tutorial on technologies requirements challenges and solutions,” IEEE Comm. Surv. and Tutor. 20(1), 708–769 (2018).
[Crossref]

Tomkos, I.

R. Borkowski, R. J. Durán, C. Kachris, D. Siracusa, A. Caballero, N. Fernández, D. Klonidis, A. Francescon, T. Jiménez, J. C. Aguado, I. Miguel, E. Salvadori, I. Tomkos, R. M. Lorenzo, and I. T. Monroy, “Cognitive Optical Network Testbed: EU Project CHRON,” J. Opt. Commun. Netw. 7(2), A344–A355 (2015).
[Crossref]

E. Palkopoulou, I. Stiakogiannakis, D. Klonidis, T. Jiménez, N. Fernández, J.C. Aguado, J. López, Y. Ye, and I. Tomkos, “Cognitive Heterogeneous Reconfigurable Optical Network: A techno-economic evaluation,” in IEEE Future Network and Mobile Summit (2013), pp. 1–10.

Tsokanos, A.

Venkat, R. A.

Wang, C.

W. Wei, C. Wang, and J. Yu, “Cognitive optical networks: Key drivers, enabling techniques, and adaptive bandwidth services,” Commun. Mag. 50(1), 106–113 (2012).
[Crossref]

Wang, D.

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

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

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]

Wei, J.

Wei, W.

W. Wei, C. Wang, and J. Yu, “Cognitive optical networks: Key drivers, enabling techniques, and adaptive bandwidth services,” Commun. Mag. 50(1), 106–113 (2012).
[Crossref]

Wu, X.

Yang, Y.

Ye, Y.

E. Palkopoulou, I. Stiakogiannakis, D. Klonidis, T. Jiménez, N. Fernández, J.C. Aguado, J. López, Y. Ye, and I. Tomkos, “Cognitive Heterogeneous Reconfigurable Optical Network: A techno-economic evaluation,” in IEEE Future Network and Mobile Summit (2013), pp. 1–10.

Yu, C.

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]

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), 30337–30346 (2015).
[Crossref] [PubMed]

Yu, J.

W. Wei, C. Wang, and J. Yu, “Cognitive optical networks: Key drivers, enabling techniques, and adaptive bandwidth services,” Commun. Mag. 50(1), 106–113 (2012).
[Crossref]

Yu, Y.

F. Liu, Y. Lin, Y. Yu, and L. P. Barry, “Parallelized kalman filters for mitigation of the excess phase noise of fast tunable lasers in coherent optical communication systems,” IEEE Photonics J. 10(1), 1–11 (2018).

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), 30337–30346 (2015).
[Crossref] [PubMed]

Zhang, J.

W. Chen, J. Zhang, M. Gao, and G. Shen, “Performance improvement of 64-QAM coherent optical communication system by optimizing symbol decision boundary based on support vector machine,” Opt. Commun. 410, 1–7 (2018).
[Crossref]

J. Zhang, W. Chen, M. Gao, and G. Shen, “K-means-clustering-based fiber nonlinearity equalization techniques for 64-QAM coherent optical communication system,” Opt. Express 25(22), 27570–27580 (2017).
[Crossref] [PubMed]

J. Zhang, M. Gao, W. Chen, and G. Shen, “Non-data-aided k-nearest neighbors technique for optical fiber nonlinearity mitigation,” J. Lightwave Technol., in press (2018).

Zhang, K.

Zhang, M.

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

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

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Figures (14)

Fig. 1
Fig. 1 The flow chart of intelligent adaptive coherent optical receiver.
Fig. 2
Fig. 2 Schematic diagram of the CNN-based modulation format identification.
Fig. 3
Fig. 3 (i) Constellation clusters of the 16-QAM signal classified by the IACA (ii) the decision graph ρ vs δ (iii) the calculated γ in a descending order of the 50 data in (i).
Fig. 4
Fig. 4 (i) Blind K-means labeling (ii) Training-sequence assisted K-means labeling.
Fig. 5
Fig. 5 Constellation diagrams of the 64-QAM signal (i) with more noise (ii) with less noise.
Fig. 6
Fig. 6 Proposed intelligent receiver with the machine-learning algorithms.
Fig. 7
Fig. 7 The collected constellation diagram images of 4/8/16/32/64-QAM signals with the OSNR variation range of 8-23 dB, 10-25 dB, 12-27 dB, 15-30 dB and 17−32 dB at 1-dB step.
Fig. 8
Fig. 8 Accuracy of modulation format estimation versus the number of epoch for 4/8/16/32/64-QAM signals.
Fig. 9
Fig. 9 BER versus SNR for 4/16/64/128/256-QAM with slight phase rotation. Inserts are the constellation diagrams of 256-QAM signals and the corresponding decision graph of the clustering centers.
Fig. 10
Fig. 10 BER versus laser linewidth for 16-QAM signal. Insert is the corresponding constellation diagram.
Fig. 11
Fig. 11 Experimental setup: LD: laser diode; AWG: arbitrary waveform generator; EDFA: erbium-doped fiber amplifier; VOA: variable optical attenuator; LO: local oscillator.
Fig. 12
Fig. 12 Measured identification accuracy and BER curves of 75-Gb/s 64-QAM signal versus the launched signal power into 130-km SSMF. Insets are the clock-recovered diagrams with the signal power at (a) and (b).
Fig. 13
Fig. 13 Measured BER curves of 75-Gb/s 64-QAM signal versus the launched signal power into 130-km SSMF and insets are the constellation diagrams with the launched signal power of −7.17 dBm and 4.82 dBm.
Fig. 14
Fig. 14 Measured BER versus I/Q phase skew of the modulator. Insets are the constellation diagrams of the corresponding skews.

Equations (6)

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Y n =σ( w n m X + n b n m ),
ρ i = j I s e ( d ij d c ) 2 ,
δ qi ={ min q j j<i { d q i q j }, i2; max j2 { δ q j }, i=1.
p i = x i i=1 k | x i | 2 k
dc=ω d f( N(N1) 50 )
γ i = ρ i δ i , i I s