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Comparison of optical performance monitoring techniques using artificial neural networks

  • New applications of Artificial Neural Networks in Modeling & Control
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Abstract

In this paper, we make an overview of three techniques that have used artificial neural networks (ANNs) to model impairments in optical fiber. A comparison between a linear partial least squares regression algorithm and ANN is also shown. We demonstrate that nonlinear modeling is required for multi-impairment monitoring in optical fiber when using Parametric Asynchronous Eye Diagram (PAED). Results demonstrating the accuracy of PAED are also shown. A comparison between PAED and Synchronous Eye Diagrams is also demonstrated, for NRZ, RZ and QPSK modulated signals. We show that PAED can provide comprehensible diagrams for QPSK modulated signals, under a certain range of chromatic dispersion.

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References

  1. Kilper DC, Bach R, Blumenthal DJ, Einstein D, Landolsi T, Ostar L, Preiss M, Willner AE (2004) Optical performance monitoring. J Lightwave Technol 22(1):294 http://jlt.osa.org/abstract.cfm?URI=jlt-22-1-294

    Google Scholar 

  2. Willner A, Wu X, Yang JY (2010) In: Optical performance monitoring. Academic Press, Oxford, pp. 1–19. doi:10.1016/B978-0-12-374950-5.00001-8

  3. Tsai KT, Way WI (2005) Chromatic-dispersion monitoring using an optical delay-and-add filter. IEEE/OSA J Lightwave Technol 23(11):3737. doi:10.1109/JLT.2005.856230

    Article  Google Scholar 

  4. Gordon J, Kogelnik H (2000) PMD fundamentals: polarization mode dispersion in optical fibers. Proc Natl Acad Sci 97(9):4541

    Article  Google Scholar 

  5. Ghatak A, Thyagarajan K (1998) An introduction to fiber optics. Cambridge University Press, Cambridge http://books.google.pt/books?id=pG34VZMil7IC

  6. Ribeiro V, Costa L, Lima M, Teixeira ALJ (2012) Optical performance monitoring using the novel parametric asynchronous eye diagram. Opt Express 20(9):9851. doi:10.1364/OE.20.009851, http://www.opticsexpress.org/abstract.cfm?URI=oe-20-9-9851

  7. Ribeiro VM, Lima M, Teixeira A (2012) In: Optical fiber communication conference (Optical Society of America), p. JW2A.33. http://www.opticsinfobase.org/abstract.cfm?URI=OFC-2012-JW2A.33

  8. Shen T, Meng K, Lau A, Dong ZY (2010) Optical performance monitoring using artificial neural network trained with asynchronous amplitude histograms. Photonics Technol Lett IEEE 22(22):1665. doi:10.1109/LPT.2010.2078804

    Google Scholar 

  9. Jargon JA, Wu X, Willner AE (2009) In: Optical fiber communication conference. (Optical Society of America), p. OThH1 http://www.opticsinfobase.org/abstract.cfm?URI=OFC-2009-OThH1

  10. Wu X, Jargon J, Christen L, Willner A (2008) In: IEEE lasers and electro-optics society, 2008. LEOS 2008. 21st annual meeting of the, pp. 543–544. doi:10.1109/LEOS.2008.4688732

  11. Dods S, Anderson T (2006) In: Optical fiber communication conference, 2006 and the 2006 national fiber optic engineers conference. OFC 2006, p. 3 doi:10.1109/OFC.2006.215890

  12. Anderson T, Kowalczyk A, Clarke K, Dods S, Hewitt D, Li J (2009) Multi impairment monitoring for optical networks. J Lightwave Technol 27(16):3729. doi:10.1109/JLT.2009.2025052

    Article  Google Scholar 

  13. Wu X, Jargon J, Paraschis L, Willner A (2011) Ann-based optical performance monitoring of qpsk signals using parameters derived from balanced-detected asynchronous diagrams. Photonics Technol Lett, IEEE 23(4):248. doi:10.1109/LPT.2010.2098025

    Article  Google Scholar 

  14. Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157

    MATH  Google Scholar 

  15. Zhang GP (2000) Neural networks for classification: a survey. Syst Man Cybern Part C: Appl Rev, IEEE Trans on 30(4):451

    Article  Google Scholar 

Download references

Acknowledgments

The grant SFRH/BD/69577/2010 from the Portuguese Foundation for Science and Technology is acknowledged.

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Authors and Affiliations

  1. Instituto de Telecomunicações, Campus Universitário de Santiago, 3810-193, Aveiro, Portugal

    Vítor Ribeiro, Mário Lima & António Teixeira

Authors
  1. Vítor Ribeiro
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  2. Mário Lima
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  3. António Teixeira
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Correspondence to Vítor Ribeiro.

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Ribeiro, V., Lima, M. & Teixeira, A. Comparison of optical performance monitoring techniques using artificial neural networks. Neural Comput & Applic 23, 583–589 (2013). https://doi.org/10.1007/s00521-013-1405-z

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  • DOI: https://doi.org/10.1007/s00521-013-1405-z

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