{"title":"Optical Signal-To-Noise Ratio Monitoring Based on Delay Tap Sampling Using Artificial Neural Network","authors":"Feng Wang, Shencheng Ni, Shuying Han, Shanhong You","volume":165,"journal":"International Journal of Electronics and Communication Engineering","pagesStart":266,"pagesEnd":271,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10011458","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.<\/p>\r\n","references":"[1]\tKozicki B, Maruta A, Kitayama K I, \u201cExperimental demonstration of optical performance monitoring for RZ-DPSK signals using delay-tap sampling method,\u201d Optics Express, 2008, 16(6):3566-3576.\r\n[2]\tJ.H. Lee, D.K. Jung, C.H. Kim, Y.C.Chung, \u201cOSNR monitoring technique using polarization-nulling method,\u201d IEEE Photonics Technology Letters, 2001, 13(1):88\u201390.\r\n[3]\tWu X , Jargon J A , Skoog R A , et al., \u201cApplications of Artificial Neural Networks in Optical Performance Monitoring,\u201d Journal of Lightwave Technology, 2009, 27(16):3580-3589.\r\n[4]\tJ. A. Jargon, X. X. Wu, H. Y. Choi, Y. C. Chung, and A. E. Willner, \u201cOptical performance monitoring of QPSK data channels by use of neural networks trained with parameters derived from asynchronous constellation diagrams,\u201d Optics Express, 2010, 18, pp. 4931-4938.\r\n[5]\tKhan F N, Lau A P T, Li Z, et al., \u201cOSNR monitoring for RZ-DQPSK systems using half-symbol delay-tap sampling technique,\u201d IEEE Photonics Technology Letters, 2010, 22(11):823-825.\r\n[6]\tKhan F, Zhong K, Zhou X, et al., \u201cJoint OSNR monitoring and modulation format identification in digital coherent receivers using deep neural Networks,\u201d Optics Express, 2017, 25(15):17767.\r\n[7]\tAbadi, Mart\u00edn, et al., \u201cTensorFlow: A system for large-scale machine learning,\u201d 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI), 2016. \r\n[8]\tS. Ioffe and C. Szegedy, \u201cBatch normalization: accelerating deep network training by reducing internal covariate shift,\u201d 32nd International Conference on Machine Learning (ICML), 2015.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 165, 2020"}