Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30753
Optical Signal-To-Noise Ratio Monitoring Based on Delay Tap Sampling Using Artificial Neural Network

Authors: Feng Wang, Shencheng Ni, Shuying Han, Shanhong You

Abstract:

With the development of optical communication, optical performance monitoring (OPM) has received more and more attentions. Since optical signal-to-noise ratio (OSNR) is directly related to bit error rate (BER), it is one of the important parameters in optical networks. Recently, artificial neural network (ANN) has been greatly developed. ANN has strong learning and generalization ability. In this paper, a method of OSNR monitoring based on delay-tap sampling (DTS) and ANN has been proposed. DTS technique is used to extract the eigenvalues of the signal. Then, the eigenvalues are input into the ANN to realize the OSNR monitoring. The experiments of 10 Gb/s non-return-to-zero (NRZ) on–off keying (OOK), 20 Gb/s pulse amplitude modulation (PAM4) and 20 Gb/s return-to-zero (RZ) differential phase-shift keying (DPSK) systems are demonstrated for the OSNR monitoring based on the proposed method. The experimental results show that the range of OSNR monitoring is from 15 to 30 dB and the root-mean-square errors (RMSEs) for 10 Gb/s NRZ-OOK, 20 Gb/s PAM4 and 20 Gb/s RZ-DPSK systems are 0.36 dB, 0.45 dB and 0.48 dB respectively. The impact of chromatic dispersion (CD) on the accuracy of OSNR monitoring is also investigated in the three experimental systems mentioned above.

Keywords: Artificial Neural Network, ANN, chromatic dispersion, optical signal-to-noise ratio, OSNR, delay-tap sampling

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 45

References:


[1] Kozicki B, Maruta A, Kitayama K I, “Experimental demonstration of optical performance monitoring for RZ-DPSK signals using delay-tap sampling method,” Optics Express, 2008, 16(6):3566-3576.
[2] J.H. Lee, D.K. Jung, C.H. Kim, Y.C.Chung, “OSNR monitoring technique using polarization-nulling method,” IEEE Photonics Technology Letters, 2001, 13(1):88–90.
[3] Wu X , Jargon J A , Skoog R A , et al., “Applications of Artificial Neural Networks in Optical Performance Monitoring,” Journal of Lightwave Technology, 2009, 27(16):3580-3589.
[4] J. A. Jargon, X. X. Wu, H. Y. Choi, Y. C. Chung, and A. E. Willner, “Optical performance monitoring of QPSK data channels by use of neural networks trained with parameters derived from asynchronous constellation diagrams,” Optics Express, 2010, 18, pp. 4931-4938.
[5] Khan F N, Lau A P T, Li Z, et al., “OSNR monitoring for RZ-DQPSK systems using half-symbol delay-tap sampling technique,” IEEE Photonics Technology Letters, 2010, 22(11):823-825.
[6] Khan F, Zhong K, Zhou X, et al., “Joint OSNR monitoring and modulation format identification in digital coherent receivers using deep neural Networks,” Optics Express, 2017, 25(15):17767.
[7] Abadi, Martín, et al., “TensorFlow: A system for large-scale machine learning,” 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI), 2016.
[8] S. Ioffe and C. Szegedy, “Batch normalization: accelerating deep network training by reducing internal covariate shift,” 32nd International Conference on Machine Learning (ICML), 2015.