Accurate QoT estimation for the optimized design of optical transport network based on advanced deep learning model
Tài liệu tham khảo
Gerstel, 2012, Elastic optical networking: A new dawn for the optical layer?, IEEE Commun. Mag., 50, s12, 10.1109/MCOM.2012.6146481
Dong, 2016, Optical performance monitoring: A review of current and future technologies, J. Lightwave Technol., 34, 525, 10.1109/JLT.2015.2480798
Liu, 2020, Ai-based modeling and monitoring techniques for future intelligent elastic optical networks, Appl. Sci., 10, 363, 10.3390/app10010363
Musumeci, 2018, An overview on application of machine learning techniques in optical networks, IEEE Commun. Surveys Tutor., 21, 1383, 10.1109/COMST.2018.2880039
Mata, 2018, Artificial intelligence (ai) methods in optical networks: A comprehensive survey, Optical Switching Networking, 28, 43, 10.1016/j.osn.2017.12.006
Poggiolini, 2012, The gn model of non-linear propagation in uncompensated coherent optical systems, J. Lightwave Technol., 30, 3857, 10.1109/JLT.2012.2217729
Sambo, 2010, Lightpath establishment assisted by offline qot estimation in transparent optical networks, J. Optical Commun. Networking, 2, 928, 10.1364/JOCN.2.000928
Morais, 2018, Evaluating machine learning models for qot estimation, 1
Ayassi, 2020, An overview on machine learning-based solutions to improve lightpath qot estimation, 1
Pointurier, 2021, Machine learning techniques for quality of transmission estimation in optical networks, J. Opt. Commun. Networking, 13, B60, 10.1364/JOCN.417434
Ghobadi, 2016, Optical layer failures in a large backbone, 461
S. Oda, M. Miyabe, S. Yoshida, T. Katagiri, Y. Aoki, J.C. Rasmussen, M. Birk, K. Tse, A learning living network for open roadm networks, in: ECOC 2016
42nd European Conference on Optical Communication, VDE, 2016, pp. 1-3.
A.E. Willner, Z. Pan, C. Yu, Optical performance monitoring, in: Optical fiber telecommunications VB, Elsevier, 2008, pp. 233–292.
Khan, 2012, Optical performance monitoring using artificial neural networks trained with empirical moments of asynchronously sampled signal amplitudes, IEEE Photonics Technol. Lett., 24, 982, 10.1109/LPT.2012.2190762
Wang, 2016, Nonlinearity mitigation using a machine learning detector based on k-nearest neighbors, IEEE Photonics Technol. Lett., 28, 2102, 10.1109/LPT.2016.2555857
Rottondi, 2018, Machine-learning method for quality of transmission prediction of unestablished lightpaths, J. Opt. Commun. Networking, 10, A286, 10.1364/JOCN.10.00A286
S. Yan, F.N. Khan, A. Mavromatis, Q. Fan, H. Frank, R. Nejabati, A.P.T. Lau, D. Simeonidou, Field trial of machine-learning-assisted and sdn-based optical network management, in: Optical Fiber Communication Conference, Optical Society of America, 2019, pp. M2E–1.
S. Aladin, C. Tremblay, Cognitive tool for estimating the qot of new lightpaths, in: Optical Fiber Communication Conference, Optical Society of America, 2018, pp. M3A–3.
Pointurier, 2011, Cross-layer monitoring in transparent optical networks, J. Opt. Commun. Networking, 3, 189, 10.1364/JOCN.3.000189
Proietti, 2019, Experimental demonstration of machine-learning-aided qot estimation in multi-domain elastic optical networks with alien wavelengths, J. Opt. Commun. Networking, 11, A1, 10.1364/JOCN.11.0000A1
Sartzetakis, 2019, Accurate quality of transmission estimation with machine learning, J. Opt. Commun. Networking, 11, 140, 10.1364/JOCN.11.000140
Yan, 2017, Field trial of machine-learning-assisted and sdn-based optical network planning with network-scale monitoring database, 1
Jiménez, 2013, A cognitive quality of transmission estimator for core optical networks, J. Lightwave Technol., 31, 942, 10.1109/JLT.2013.2240257
De Miguel, 2013, Cognitive dynamic optical networks, J. Opt. Commun. Networking, 5, A107, 10.1364/JOCN.5.00A107
Yu, 2019, Model transfer of qot prediction in optical networks based on artificial neural networks, J. Opt. Commun. Networking, 11, C48, 10.1364/JOCN.11.000C48
Khan, 2019, An optical communication’s perspective on machine learning and its applications, J. Lightwave Technol., 37, 493, 10.1109/JLT.2019.2897313
Morais, 2018, Machine learning models for estimating quality of transmission in dwdm networks, J. Opt. Commun. Networking, 10, D84, 10.1364/JOCN.10.000D84
R. Proietti, X. Chen, A. Castro, G. Liu, H. Lu, K. Zhang, J. Guo, Z. Zhu, L. Velasco, S.B. Yoo, Experimental demonstration of cognitive provisioning and alien wavelength monitoring in multi-domain eon, in: Optical Fiber Communication Conference, Optical Society of America, 2018, pp. W4F–7.
Wang, 2017, Modulation format recognition and osnr estimation using cnn-based deep learning, IEEE Photonics Technol. Lett., 29, 1667, 10.1109/LPT.2017.2742553
Panayiotou, 2017, Performance analysis of a data-driven quality-of-transmission decision approach on a dynamic multicast-capable metro optical network, J. Opt. Commun. Networking, 9, 98, 10.1364/JOCN.9.000098
Panayiotou, 2016, A data-driven qot decision approach for multicast connections in metro optical networks, 1
Wang, 2018, Osnr and nonlinear noise power estimation for optical fiber communication systems using lstm based deep learning technique, Optics Express, 26, 21346, 10.1364/OE.26.021346
Tanimura, 2016, Osnr monitoring by deep neural networks trained with asynchronously sampled data, 1
Khan, 2017, Joint osnr monitoring and modulation format identification in digital coherent receivers using deep neural networks, Optics Exp., 25, 17767, 10.1364/OE.25.017767
T. Tanimura, T. Hoshida, T. Kato, S. Watanabe, H. Morikawa, Data-analytics-based optical performance monitoring technique for optical transport networks, in: Optical Fiber Communication Conference, Optical Society of America, 2018, pp. Tu3E–3.
Chouman, 2021, Forecasting lightpath qot with deep neural networks: Optical Fiber Communications Conference and Exhibition (OFC), IEEE, 2021, 1
Microsoft, Wide-area optical backbone performance,https://www.microsoft.com/en-us/research/project/microsofts-wide-area-optical-backbone/, 2017.