Modulation Scheme Identification Based on Artificial Neural Network Algorithms for Optical Communication System

Mustafa A. Jalil, Jenan Ayad, Hanan J. Abdulkareem

Abstract


Higher-order modulation schemes in optical communication systems that suffer from several impairments can use artificial intelligence (AI) algorithms, among other possible techniques, to mitigate these issues. In this paper, several techniques for optical communication systems have been proposed to enhance the performance of dual-polarization (DP) M-ary Quadrature Amplitude Modulation (M-QAM) as DP-16-QAM, DP-64-QAM, DP-128-QAM, and DP-256-QAM with 240Gbps data rate. Artificial neural networks (ANNs) with seven different training algorithms have been applied to optimize the optical communication system. A high optimization of modulation format identification (MFI) with accuracy up to 100% was obtained at about 13 dB OSNR and at 22 dB OSNR for the DP-265-QAM format.

Keywords


artificial neural network (ANN); back-propagation; optical system optimization; modulation format identification (MFI)

Full Text:

PDF

References


Schmogrow, R., Nebendahl, B., Winter, M., Josten, A., Hillerkuss, D., Koenig, S., Meyer, J., Dreschmann, M., Huebner, M., Koos, C., Becker, J., Freude, W. & Leuthold, J., Error Vector Magnitude as a Performance Measure for Advanced Modulation Formats, IEEE Photonics Technology Letters, 24(1), pp. 61-63, 2012.

Dong, Z., Lau, A.P.T. & Lu, C., OSNR Monitoring for QPSK and 16-QAM Systems in Presence of Fiber Nonlinearities for Digital Coherent Receivers, Optics Express, 20(17), pp. 19520-19534, Oct. 2012. DOI: 10.1364/OE.20.019520

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, Optical Fiber Communication Conference Optical Society of America Technical Digest, paper Th4D.3, 2014. DOI: 10.1364/OFC.2014.Th4D.3.

Mata, J., Miguel, I.D., Durán, R.J., Merayo, N., Singh, S.K., Jukan, A. & Chamania, M., Artificial intelligence (AI) Methods in optical Networks: A Comprehensive Survey, Optical Switching and Networking, 28, pp. 43-57, 2018.

Wu, X., Jargon, J., Skoog, R., Paraschis, L. & Willner, A., Applications of Artificial Neural Networks in Optical Performance Monitoring, Journal of Lightwave Technology, 27(16), pp. 3580-3589, 2009.

Eriksson, T.A., Bulow, H. & Leven, A. Applying Neural Networks in Optical Communication Systems: Possible Pitfalls, IEEE Photonics Technology Letters, 29(23), pp. 2091-2094, Jan. 2017.

Guesmi, L., Fathallah, H. & Menif, M., Modulation Format Recognition Using Artificial Neural Networks for the Next Generation Optical Networks, in Advanced Applications for Artificial Neural Networks, Adel El-Shahat (ed.), IntechOpen, London, United Kingdom, pp. 11-27, 2018. DOI:10.5772/INTECHOPEN.70954.

Tanimura, T., Hoshida, T., Kato, T., Watanabe, S., & Morikawa, H., Convolutional Neural Network-Based Optical Performance Monitoring for Optical Transport Networks, Journal of Optical Communications and Networking, 11(1), pp. A52-A59, 2019. DOI: 10.1364/JOCN.11.000A52.

Khan, F.N., Zhong, K., Al-Arashi, W.H., Yu, C., Lu, C. & Lau, A.P.T., Modulation Format Identification in Coherent Receivers Using Deep Machine Learning, IEEE Photonics Technology Letters, 28(17), pp. 1886-1889, Jan. 2016.

Jargon, J.A., Wu, X., Choi, H.Y., Chung, Y.C. & Willner, A.E., Optical Performance Monitoring of QPSK Data Channels by Use of Neural Networks Trained with Parameters Derived from Asynchronous Constellation Diagrams, Optics Express, 18(5), pp. 4931-4938, 2010.

Wang, Z., Yang, A., Guo, P. & He, P., OSNR and Nonlinear Noise Power Estimation for Optical Fiber Communication Systems Using LSTM based Deep Learning Technique, Optics Express, 26(16), pp. 21346-21357, Mar. 2018.

Wang, D., Zhang, M., Li, Z., Li, J., Fu, M., Cui, Y. & Chen, X., Modulation Format Recognition and OSNR Estimation Using CNN-Based Deep Learning, IEEE Photonics Technology Letters, 29(19), pp. 1667-1670, Jan. 2017.

Zhang, Q.W., Liu, M., Zhou, H., Chen, J., Cao, B.Y., Song, Y.X., Zhang, J.J., Li, Y.C. & Wang, M., Artificial Neural Network based Modulation Identification for Elastic Optical Networks, 23rd Opto-Electronics and Communications Conference (OECC), Jeju Island, Korea (South), pp. 1-2, , 2018. DOI: 10.1109/OECC.2018.8729911.

Han, J., Kamber, M. & Pei, J., Data Mining: Concepts and Techniques. 3rd ed., San Francisco: Elsevier Science, Morgan Kaufman, pp.393-442, 2012. DOI: 10.1016/B978-0-12-381479-1.00001-0

Zurada, J.M., Introduction to Artificial Neural Systems, St. Paul, MN: PWS Publ. Comp., 1995.

Zhang, S., Peng, Y., Sui, Q., Li, J. & Li, Z.,Modulation Format Identification in Heterogeneous Fiber-Optic Networks Using Artificial Neural Networks and Genetic Algorithms, Photonic Network Communications, 32(2), pp. 246-252, 2016.

Wlodzislaw, D. & Jankowski, N., Transfer Functions: Hidden Possibilities for Better Neural Networks, 9th European Symposium on Artificial Neural Networks Conference (ESANN), 2001.

Battiti, R., First and Second Order Methods for Learning: Between Steepest Descent and Newton's Method, Neural Computation, 4(2), 141-166, 1992. DOI: 10.1162/neco.1992.4.2.141.

Hagan, M.T., Demuth, H.B. & Beale, M.H., Neural Network Design, Boston, MA: PWS Publishing, 1996.

Dennis, J.E. & Schnabel, R.B., Numerical Methods for Unconstrained Optimization and Nonlinear Equations, Englewood Cliffs, New Jersey: Prentice-Hall, 1983.

Riedmiller, M. & Braun, H., A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm, Proceedings of the IEEE International Conference on Neural Networks, 1993. DOI: 10.1109/ICNN.1993.298623.

lMoller, M.F., A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning, Neural Networks, 6(4), pp. 525-533, 1993.




DOI: http://dx.doi.org/10.5614%2Fitbj.ict.res.appl.2020.14.1.5

Refbacks

  • »


Contact Information:

LPPM – ITB, 

Center for Research and Community Services (CRCS) Building Floor 7th, 
Jl. Ganesha No. 10 Bandung 40132, Indonesia,

Tel. +62-22-86010080,

Fax.: +62-22-86010051;

e-mail: jictra@lppm.itb.ac.id.