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An overview of ML-based applications for next generation optical networks

Abstract

Over the past few decades, the demand for the capacity and reliability of optical networks has continued to grow. In the meantime, optical networks with larger knowledge scales have become sources of numerous heterogeneous data. In order to handle these new challenges, many issues need to be resolved, among which the low-margin optical networks design, power optimization, routing and wavelength assignment (RWA), failure management are quite important. However, the use of traditional algorithms in the above four applications shows some shortcomings. Fortunately, artificial intelligence (AI), especially machine learning (ML), is regarded as one of the most promising methods to overcome these shortcomings. In this study, we review the applications of ML methods in solving these four issues. Although many ML-based researches have emerged, the applications of ML techniques in optical networks still face challenges. Therefore, we also discuss some possible future directions of investigating ML-based approaches in optical networks.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant No. 61801291), Shanghai Rising-Star Program (Grant No. 19QA1404600), and National Key R&D Program of China (Grant No. 2018YFB-1801200).

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Correspondence to Qunbi Zhuge.

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Gao, R., Liu, L., Liu, X. et al. An overview of ML-based applications for next generation optical networks. Sci. China Inf. Sci. 63, 160302 (2020). https://doi.org/10.1007/s11432-020-2874-y

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  • DOI: https://doi.org/10.1007/s11432-020-2874-y

Keywords

  • optical networks
  • artificial intelligence
  • machine learning
  • system margin
  • power optimization
  • routing and wavelength assignment
  • failure management