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AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks

Shanghai Institute for Advanced Communication and Data Science, State Key Laboratory of Advanced Optical Communication Systems and Networks, Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
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Appl. Sci. 2020, 10(1), 363; https://doi.org/10.3390/app10010363
Received: 27 November 2019 / Revised: 17 December 2019 / Accepted: 27 December 2019 / Published: 3 January 2020
(This article belongs to the Special Issue Optics for AI and AI for Optics)
With the development of 5G technology, high definition video and internet of things, the capacity demand for optical networks has been increasing dramatically. To fulfill the capacity demand, low-margin optical network is attracting attentions. Therefore, planning tools with higher accuracy are needed and accurate models for quality of transmission (QoT) and impairments are the key elements to achieve this. Moreover, since the margin is low, maintaining the reliability of the optical network is also essential and optical performance monitoring (OPM) is desired. With OPM, controllers can adapt the configuration of the physical layer and detect anomalies. However, considering the heterogeneity of the modern optical network, it is difficult to build such accurate modeling and monitoring tools using traditional analytical methods. Fortunately, data-driven artificial intelligence (AI) provides a promising path. In this paper, we firstly discuss the requirements for adopting AI approaches in optical networks. Then, we review various recent progress of AI-based QoT/impairments modeling and monitoring schemes. We categorize these proposed methods by their functions and summarize advantages and challenges of adopting AI methods for these tasks. We discuss the problems remained for deploying AI-based methods to a practical system and present some possible directions for future investigation. View Full-Text
Keywords: optical transmission; optical networks; machine learning; artificial intelligence; quality of transmission; optical performance monitoring; failure management optical transmission; optical networks; machine learning; artificial intelligence; quality of transmission; optical performance monitoring; failure management
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MDPI and ACS Style

Liu, X.; Lun, H.; Fu, M.; Fan, Y.; Yi, L.; Hu, W.; Zhuge, Q. AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks. Appl. Sci. 2020, 10, 363. https://doi.org/10.3390/app10010363

AMA Style

Liu X, Lun H, Fu M, Fan Y, Yi L, Hu W, Zhuge Q. AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks. Applied Sciences. 2020; 10(1):363. https://doi.org/10.3390/app10010363

Chicago/Turabian Style

Liu, Xiaomin, Huazhi Lun, Mengfan Fu, Yunyun Fan, Lilin Yi, Weisheng Hu, and Qunbi Zhuge. 2020. "AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks" Applied Sciences 10, no. 1: 363. https://doi.org/10.3390/app10010363

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