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Improving multi-step prediction performance of multi-channel QoT over optical backbone networks: deep echo state attention network

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Abstract

Multi-channel transmission mode is the mainstream in real optical system scenarios, and its precise prediction of the optical channel quality of transmission (QoT) can provide guidance for the connections routing and margins allocation, avoiding network resources waste and unavailable connection establishment. However, current multi-channel QoT predictions devote to single-step modeling. It is difficult to grasp the state changes of the optical channel for a period of time in the future, thereby hardly enabling early warnings for abnormal channel conditions and timely maintenance deployment. To tackle this issue, we propose a novel multi-step multi-channel QoT prediction framework, i.e., the deep echo state attention network (DESAN). Structurally, it consists of stacked reservoirs that are successively connected, supporting multi-level feature extraction of optical QoT signal. Specially, the attention mechanism (AM) is introduced for enhancing each reservoir’s state, which captures long-term QoT data features more effectively, meanwhile reducing the negative impact of redundant neurons as much as possible. Finally, aggregating the AM outputs of all reservoirs’ states is for the DESAN training. On the real-world optical-layer characteristic data from Microsoft optical backbone network, the simulation results show that our proposal can make a good tradeoff between sequential multi-step QoT modeling performance and efficiency. The statistical verification is further adopted to demonstrate our findings.

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Data availability

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work was supported in part by the key program of North China University of Science and Technology, under Grant No. ZD-YG-202317-23.

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Authors and Affiliations

  1. College of Artificial Intelligence, North China University of Science and Technology, Tangshan, 063210, China

    Xiaochuan Sun, Difei Cao, Mingxiang Hao, Zhigang Li & Yingqi Li

  2. Hebei Key Laboratory of Industrial Intelligent Perception, Tangshan, 063210, China

    Xiaochuan Sun, Difei Cao, Mingxiang Hao, Zhigang Li & Yingqi Li

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  1. Xiaochuan Sun
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  2. Difei Cao
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Correspondence to Yingqi Li.

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Sun, X., Cao, D., Hao, M. et al. Improving multi-step prediction performance of multi-channel QoT over optical backbone networks: deep echo state attention network. Opt Rev (2024). https://doi.org/10.1007/s10043-024-00873-9

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