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SCIENCE CHINA Information Sciences, Volume 63, Issue 6: 160301(2020) https://doi.org/10.1007/s11432-020-2871-2

Artificial intelligence-driven autonomous optical networks: 3S architecture and key technologies

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  • ReceivedJan 10, 2020
  • AcceptedApr 13, 2020
  • PublishedMay 13, 2020

Abstract

In the optical networks, the dynamicity, the complexity and the heterogeneity have dramatically increased owing to the deployment of advanced coherent techniques, and the optical cross-connect technologies and diverse network infrastructures pose great challenges in the optical network management and maintenance for the network operators. In this review, we propose a “3S" architecture for AI-driven autonomous optical network, which can aid the optical networks operated in “self-aware” of network status, “self-adaptive” of network control, and “self-managed” of network operations. To support these functions, a number of artificial intelligence (AI)-driven techniques have been investigated to improve the flexibility and the reliability from the device aspect to network aspect. Adaptative erbium-doped fiber amplifier (EDFA) controlling is an example for the device aspect, which provides a power self-adaptive capability according to the network condition. From the link aspect, adaptive fiber nonlinearity compensation, optical monitoring performance and quality of transmission estimation are developed to monitor and alleviate the link-dependent signal impairments in an automatic way. From the network aspect, traffic prediction and network state analysis methods provide the self-awareness, while automatic resource allocation and network fault management powered by AI enhance the self-adaptiveness and self-management capabilities. Benefit from the sufficient network management data, powerful data-mining capability and matured computation units, these AI techniques have great potentials to provide autonomous features for optical networks, including the network resource scheduling and the network customization.


Acknowledgment

This work was supported by National Key RD Program of China (Grant No. 2018YFB1800802) and National Natural Science Foundation of China (Grant No. 61871051).


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