Abstract
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.
Highlights
The progress of 5G mobile networks, internet of things and cloud services has raised high demands and new requirements for the capacity and reliability of optical networks
Since impairments like chromatic dispersion (CD) and polarization-mode dispersion (PMD) can be compensated in the receiver, we focus on the impairments that may cause performance degradation
We review many previous works on machine learning (ML) aided modeling and monitoring techniques in elastic optical networks
Summary
The progress of 5G mobile networks, internet of things and cloud services has raised high demands and new requirements for the capacity and reliability of optical networks. Optical performance monitoring (OPM) is a key building block, which enables network controllers to adjust link configurations according to the real-time status of a system. They should not necessitate expensive external devices, and one OPM block is preferable to monitor multiple impairments It may be difficult for analytical models to achieve these two goals simultaneously but ML-aided methods can help to fulfill these requirements. For the generation EON, applications of ML techniques for modeling and monitoring can provide strong support to build a reliable and intelligent optical network with lower design margins. The large number of data makes it more challenging to discover useful information from them In this case, data-driven ML methods are essential tools for network planning and management, but these methods should be improved to be cost-effective and reliable for deployment.
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