Skip to main content

Advertisement

SpringerLink
Log in

Modulation format identification in heterogeneous fiber-optic networks using artificial neural networks and genetic algorithms

  • Published:
Photonic Network Communications Aims and scope Submit manuscript

Abstract

The use of genetic algorithm (GA) to simplify the structures of artificial neural network-based modulation format identification is proposed in next-generation dynamic and heterogeneous fiber-optic networks. Simulation results show that with 80 asynchronous amplitude histogram bins, by virtue of GA, the identification error rate decreases from 4.24 to 1.04 %.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

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

    Article  Google Scholar 

  2. Monroy, I.T., Zibar, D., Gonzalez, N.G., Borkowski, R.: Cognitive heterogeneous reconfigurable optical networks (CHRON): enabling technologies and techniques. In: Proceedings of the ICTON (2011), paper Th.A1.2

  3. Nag, A., Tornatore, M., Mukherjee, B.: Optical network design with mixed line rates and multiple modulation formats. J. Lightwave Technol. 28(4), 466–475 (2010)

    Article  Google Scholar 

  4. Chung, Y.C.: Optical performance monitoring techniques; current status and future challenges. In: Proceedings of the ECOC (2008), paper We.1.D.1

  5. Kilper, D.C., Bach, R., Blumenthal, D.J., Einstein, D., Landolsi, T., Ostar, L., Preiss, M., Willner, A.E.: Optical performance monitoring. J. Lightwave Technol. 22(1), 294–304 (2004)

    Article  Google Scholar 

  6. Saleh, A., Simmons, J.M.: Evolution toward the next-generation core optical network. J. Lightwave Technol. 24(9), 3303–3321 (2006)

    Article  Google Scholar 

  7. Liu, J., Dong, Z., Zhong, K., Lau, A.P.T., Lu, C., Lu, Y.: Modulation format identification based on received signal power distributions for digital coherent receivers. In: Proceedings of the OFC (2014), Th4D.3

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

    Article  Google Scholar 

  9. Borkowski, R., Zibar, D., Caballero, A., Arlunno, V., Monroy, I.T.: Stokes space-based optical modulation format recognition for digital coherent receivers. IEEE Photonics Lett. 25(21), 2129–2132 (2013)

    Article  Google Scholar 

  10. Khan, F.N., Zhou, Y., Lau, A.P.T., Lu, C.: Modulation format identification in heterogeneous fiber-optic networks using artificial neural networks. Opt. Express 20(11), 12422–12431 (2012)

    Article  Google Scholar 

  11. Khan, F.N., Shen, T.S.R., Zhou, Y., Lau, A.P.T., Lu, C.: 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)

    Article  Google Scholar 

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

    Article  Google Scholar 

  13. Hassoun, M.H.: Fundamentals of Artificial Neural Networks. MIT Press, Cambridge (1995)

    MATH  Google Scholar 

  14. Zhang, Q.J., Gupta, K.C.: Neural Networks for RF and Microwave Design. Artech, Boston (2000)

    Google Scholar 

  15. Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Netw. 2(5), 359–366 (1989)

    Article  Google Scholar 

  16. Aouiti, C., Alimi, A.M., Karray, F., Maalej, A.: A hierarchical genetic algorithm for the design of the beta basis function neural network. In: Proceedings of the 2002 International Joint Conference on Neural Networks, pp. 1246–1251 (2002)

  17. Ling, S.H., Lam, H.K., Leung, F.H.F., Tam, P.K.S.: A novel GA-based neural network for short-term load forecasting. In: Proceedings of the 2002 International Joint Conference on Neural Networks, pp. 2761–2766 (2002)

  18. Hanik, N., Gladisch, A., Caspar, C., Strebel, B.: Application of amplitude histograms to monitor performance of optical channels. Electron. Lett. 35(5), 403–404 (1999)

    Article  Google Scholar 

  19. Kozicki, B., Takuya, O., Hidehiko, T.: Optical performance monitoring of phase-modulated signals using asynchronous amplitude histogram analysis. J. Lightwave Technol. 26(10), 1353–1361 (2008)

    Article  Google Scholar 

  20. Venkatesan, D., Kannan, K., Saravanan, R.: A genetic algorithm-based artificial neural network model for the optimization of machining processes. J. Neural Comput. Appl. 18(2), 135–140 (2009)

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to acknowledge the support of National Natural Science Foundation of China (NSFC) under Grant Nos. (61307092, 61435006), New Century Excellent Talents in University (NCET-12-0679), National High Technology 863 Research and Development Program of China (No. 2013AA013300).

Author information

Authors and Affiliations

  1. Institute of Photonics Technology, Jinan University, Guangzhou, 510632, Guangdong Province, People’s Republic of China

    Shuting Zhang, Yingqing Peng, Qi Sui, Jianping Li & Zhaohui Li

Authors
  1. Shuting Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yingqing Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qi Sui
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jianping Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhaohui Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhaohui Li.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, S., Peng, Y., Sui, Q. et al. Modulation format identification in heterogeneous fiber-optic networks using artificial neural networks and genetic algorithms. Photon Netw Commun 32, 246–252 (2016). https://doi.org/10.1007/s11107-016-0606-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11107-016-0606-7

Keywords

Search

Navigation