Journal
APPLIED SCIENCES-BASEL
Volume 10, Issue 1, Pages -Publisher
MDPI
DOI: 10.3390/app10010363
Keywords
optical transmission; optical networks; machine learning; artificial intelligence; quality of transmission; optical performance monitoring; failure management
Categories
Funding
- NSFC [61801291]
- Shanghai Rising-Star Program [19QA1404600]
- National Key R&D Program of China [2018YFB1801203]
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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.
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