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- W3100983211 abstract "Wide-area optical backbone network provides crucial support for the fifth-generation (5G) development. Precise quality of transmission (QoT) prediction is conducive to assisting in the automatic low-margin network configurations to meet the demands of the 5G network, such as high reliability, high efficiency, and low latency. At present, machine learning has been developed to capture the valuable information hidden in the QoT data, whereas it is still a challenging problem for these existing models to simultaneously ensure prediction accuracy and training efficiency. To address this issue, a novel QoT prediction method based on reservoir computing is proposed, considering echo state network and its variants. The basic idea is that nonlinearity can be dealt by means of linear regression in the high-dimension reservoir state space. On real-world QoT time series benchmark, the experimental results show that our proposals significantly outperform the state-of-the-art models for single-channel QoT prediction. Besides, their superiorities are reconfirmed by statistical analysis." @default.
- W3100983211 created "2020-11-23" @default.
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- W3100983211 date "2020-08-09" @default.
- W3100983211 modified "2023-10-18" @default.
- W3100983211 title "Towards High-efficient QoT Prediction of Wide-area Optical Backbone Network: A Reservoir Computing View" @default.
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- W3100983211 doi "https://doi.org/10.1109/iccc49849.2020.9238888" @default.
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