Abstract

We experimentally demonstrate simultaneous optical signal-to-noise ratio (OSNR) monitoring and modulation format identification (MFI) in heterogeneous fiber-optic networks by using principal component analysis (PCA) and statistical distance measurement based pattern recognition on scatter plots obtained through asynchronous single channel sampling (ASCS). The proposed technique enables OSNR monitoring for several commonly-used modulation formats with mean OSNR estimation error of 1 dB and without requiring any information about the signal type during the online monitoring process. In addition, it successfully demonstrates the identification of unknown modulation formats of the received signals with an overall accuracy of 98.46%. The effects of chromatic dispersion (CD) on the performance of proposed technique are also analyzed. Due to the use of a single low-speed asynchronous sampling device in the proposed technique, the implementation complexity and cost of the monitoring devices can be significantly reduced.

© 2015 Optical Society of America

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References

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    [Crossref]
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    [Crossref]
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    [Crossref]
  4. C. K. Chan, Optical Performance Monitoring (Academic, 2010).
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    [Crossref]
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    [Crossref]
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    [Crossref]
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    [Crossref]
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    [Crossref] [PubMed]
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    [Crossref] [PubMed]
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    [Crossref]
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    [Crossref]
  17. X. Wu, J. A. Jargon, R. A. Skoog, L. Paraschis, and A. E. Willner, “Applications of artificial neural networks in optical performance monitoring,” J. Lightwave Technol. 27(16), 3580–3589 (2009).
    [Crossref]
  18. S. Cui, S. He, J. Shang, C. Ke, S. Fu, and D. Liu, “Method to improve the performance of the optical modulation format identification system based on asynchronous amplitude histogram,” Opt. Fiber Technol. 23, 13–17 (2015).
    [Crossref]
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    [Crossref] [PubMed]

2015 (2)

Y. Yu and C. Yu, “Optical signal to noise ratio monitoring using variable phase difference phase portrait with software synchronization,” Opt. Express 23(9), 11284–11289 (2015).
[Crossref] [PubMed]

S. Cui, S. He, J. Shang, C. Ke, S. Fu, and D. Liu, “Method to improve the performance of the optical modulation format identification system based on asynchronous amplitude histogram,” Opt. Fiber Technol. 23, 13–17 (2015).
[Crossref]

2014 (2)

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]

F. N. Khan, Y. Zhou, Q. Sui, and A. P. T. Lau, “Non-data-aided joint bit-rate and modulation format identification for next-generation heterogeneous optical networks,” Opt. Fiber Technol. 20(2), 68–74 (2014).
[Crossref]

2012 (1)

2010 (3)

2009 (1)

2008 (2)

2007 (1)

2004 (1)

V. Perlibakas, “Distance measures for PCA-based face recognition,” Pattern Recognit. Lett. 25(6), 711–724 (2004).
[Crossref]

Al-Arashi, W. H.

Choi, H. G.

Chung, Y. C.

Cui, S.

S. Cui, S. He, J. Shang, C. Ke, S. Fu, and D. Liu, “Method to improve the performance of the optical modulation format identification system based on asynchronous amplitude histogram,” Opt. Fiber Technol. 23, 13–17 (2015).
[Crossref]

Fu, S.

S. Cui, S. He, J. Shang, C. Ke, S. Fu, and D. Liu, “Method to improve the performance of the optical modulation format identification system based on asynchronous amplitude histogram,” Opt. Fiber Technol. 23, 13–17 (2015).
[Crossref]

He, S.

S. Cui, S. He, J. Shang, C. Ke, S. Fu, and D. Liu, “Method to improve the performance of the optical modulation format identification system based on asynchronous amplitude histogram,” Opt. Fiber Technol. 23, 13–17 (2015).
[Crossref]

Hidehiko, T.

Jargon, J. A.

Ke, C.

S. Cui, S. He, J. Shang, C. Ke, S. Fu, and D. Liu, “Method to improve the performance of the optical modulation format identification system based on asynchronous amplitude histogram,” Opt. Fiber Technol. 23, 13–17 (2015).
[Crossref]

Khan, F. N.

Kitayama, K.

Kozicki, B.

Lau, A. P. T.

Liu, D.

S. Cui, S. He, J. Shang, C. Ke, S. Fu, and D. Liu, “Method to improve the performance of the optical modulation format identification system based on asynchronous amplitude histogram,” Opt. Fiber Technol. 23, 13–17 (2015).
[Crossref]

Lu, C.

Maruta, A.

Mukherjee, B.

Nag, A.

Pan, Z.

Z. Pan, C. Yu, and A. E. Willner, “Optical performance monitoring for the next generation optical communication networks,” Opt. Fiber Technol. 16(1), 20–45 (2010).
[Crossref]

Paraschis, L.

Perlibakas, V.

V. Perlibakas, “Distance measures for PCA-based face recognition,” Pattern Recognit. Lett. 25(6), 711–724 (2004).
[Crossref]

Shang, J.

S. Cui, S. He, J. Shang, C. Ke, S. Fu, and D. Liu, “Method to improve the performance of the optical modulation format identification system based on asynchronous amplitude histogram,” Opt. Fiber Technol. 23, 13–17 (2015).
[Crossref]

Skoog, R. A.

Sui, Q.

F. N. Khan, Y. Zhou, Q. Sui, and A. P. T. Lau, “Non-data-aided joint bit-rate and modulation format identification for next-generation heterogeneous optical networks,” Opt. Fiber Technol. 20(2), 68–74 (2014).
[Crossref]

Takushima, Y.

Takuya, O.

Tan, M. C.

Tornatore, M.

Willner, A. E.

Z. Pan, C. Yu, and A. E. Willner, “Optical performance monitoring for the next generation optical communication networks,” Opt. Fiber Technol. 16(1), 20–45 (2010).
[Crossref]

X. Wu, J. A. Jargon, R. A. Skoog, L. Paraschis, and A. E. Willner, “Applications of artificial neural networks in optical performance monitoring,” J. Lightwave Technol. 27(16), 3580–3589 (2009).
[Crossref]

Wu, X.

Yu, C.

Y. Yu and C. Yu, “Optical signal to noise ratio monitoring using variable phase difference phase portrait with software synchronization,” Opt. Express 23(9), 11284–11289 (2015).
[Crossref] [PubMed]

Z. Pan, C. Yu, and A. E. Willner, “Optical performance monitoring for the next generation optical communication networks,” Opt. Fiber Technol. 16(1), 20–45 (2010).
[Crossref]

Yu, Y.

Zhou, Y.

J. Lightwave Technol. (3)

J. Opt. Commun. Netw. (1)

J. Opt. Netw. (1)

Opt. Express (4)

Opt. Fiber Technol. (3)

S. Cui, S. He, J. Shang, C. Ke, S. Fu, and D. Liu, “Method to improve the performance of the optical modulation format identification system based on asynchronous amplitude histogram,” Opt. Fiber Technol. 23, 13–17 (2015).
[Crossref]

F. N. Khan, Y. Zhou, Q. Sui, and A. P. T. Lau, “Non-data-aided joint bit-rate and modulation format identification for next-generation heterogeneous optical networks,” Opt. Fiber Technol. 20(2), 68–74 (2014).
[Crossref]

Z. Pan, C. Yu, and A. E. Willner, “Optical performance monitoring for the next generation optical communication networks,” Opt. Fiber Technol. 16(1), 20–45 (2010).
[Crossref]

Pattern Recognit. Lett. (1)

V. Perlibakas, “Distance measures for PCA-based face recognition,” Pattern Recognit. Lett. 25(6), 711–724 (2004).
[Crossref]

Other (6)

A. C. Rencher and W. F. Christensen, Methods of Multivariate Analysis, 3rd ed. (Wiley, 2012).

C. K. Chan, Optical Performance Monitoring (Academic, 2010).

S. D. Dods and T. B. Anderson, “Optical performance monitoring technique using delay tap asynchronous waveform sampling,” in Proc. Optical Fiber Comm. Conf. (OFC, 2006), paper OThP5.
[Crossref]

I. T. Monroy, D. Zibar, N. G. Gonzalez, and R. Borkowski, “Cognitive heterogeneous reconfigurable optical networks (CHRON): Enabling technologies and techniques,” in Proc. 13th Int. Conf. on Transparent Optical Networks (ICTON, 2011), paper Th.A1.2.
[Crossref]

Y. Yu and C. Yu, “OSNR monitoring by using single sampling channel generated 2-D phase portrait,” in Proc. Optical Fiber Comm. Conf. (OFC, 2014), paper OTh2A.49.
[Crossref]

J. A. Jargon, X. Wu, and A. E. Willner, “Optical performance monitoring using artificial neural networks trained with parameters derived from delay-tap asynchronous sampling,” in Proc. Optical Fiber Comm. Conf. (OFC, 2009), paper OThH1.

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Figures (7)

Fig. 1
Fig. 1 Conceptual diagarm of ASCS technique with a scatter plot on the right.
Fig. 2
Fig. 2 Experimental scatter plots for three modulation formats obtained using ASCS with Δt = ½ Tsymbol. The first row corresponds to OSNR = 25 dB while the second row corresponds to OSNR = 15 dB.
Fig. 3
Fig. 3 Experimental setup used for joint OSNR monitroing and MFI.
Fig. 4
Fig. 4 (a) A few eigenvalues λi plotted in descending order. (b) Parameter Θ versus number of PCs selected.
Fig. 5
Fig. 5 Mean OSNR estimation error as a function of number of features used for four different distance measures. The estimation error is less than 1 dB for Euclidean/Manhattan distance when the number of features used is 11.
Fig. 6
Fig. 6 True versus estimated OSNRs for (a) NRZ-OOK, (b) NRZ-DPSK, and (c) RZ-DPSK signals using Euclidean distance measure with 11 features.
Fig. 7
Fig. 7 Effect of number of features used on the overall identification accuracy for four different distance measures. The best identification accuracy achieved is 98.46% using Euclidean distance measure with 8 features.

Tables (1)

Tables Icon

Table 1 Identification accuracies for different modulation formats using Euclidean distance measure with 8 features.

Equations (13)

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T sampling =n T symbol +τ
T total =kn T symbol +kτ =m T symbol +Δt
Γ= 1 M j=1 M φ j
C= 1 M j=1 M ψ j ψ j T =Ψ Ψ T
C μ i = λ i μ i for i=1,2,... N 2
Θ= i=1 K λ i / i=1 N 2 λ i >P
ψ k=1 K w k μ k w k = μ k T ψ for k=1,2,...K
D min = min j=1,2,.....,M { D( X, Y j ) }
Euclidean distance: D( X,Y )= XY = i=1 K ( x i y i ) 2
Manhattan distance: D( X,Y )= i=1 K | x i y i |
Canberra distance: D( X,Y )= i=1 K | x i y i | | x i |+| y i |
Mahalanobis distance: D( X,Y )= i=1 K z i x i y i where, z i = λ i λ i + α 2 , α=0.25, and λ i are the corresponding eigenvalues of the covariance matrix obtained during PCA
( OSNR estimated , Modulation format estimated )= argmin j=1,2,.....,M { D( X, Y j ) }

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