论文信息 - An ultra-fast method for gain and noise prediction of Raman amplifiers

An ultra-fast method for gain and noise prediction of Raman amplifiers

A machine learning method for prediction of Raman gain and noise spectra is presented: it guarantees high-accuracy (RMSE < 0.4 dB) and low computational complexity making it suitable for real-time implementation in future optical networks controllers.

[1] A. Carena,et al. Merit of Raman Pumping in Uniform and Uncompensated Links Supporting NyWDM Transmission , 2016, Journal of Lightwave Technology.

[2] Junhe Zhou,et al. Robust, compact, and flexible neural model for a fiber Raman amplifier , 2006, Journal of Lightwave Technology.

[3] Yiping Wang,et al. Robust, compact, and flexible neural model for a fiber Raman amplifier , 2006 .

[4] Henk Wymeersch,et al. Machine learning under the spotlight , 2017, Nature Photonics.

[5] Radford M. Neal. Pattern Recognition and Machine Learning , 2007, Technometrics.

[6] Chao Lu,et al. An Optical Communication's Perspective on Machine Learning and Its Applications , 2019, Journal of Lightwave Technology.

[7] A. Carena,et al. Machine Learning-Based Raman Amplifier Design , 2018, 2019 Optical Fiber Communications Conference and Exhibition (OFC).

[8] João Pedro,et al. Maximizing the Capacity of Installed Optical Fiber Infrastructure Via Wideband Transmission , 2018, 2018 20th International Conference on Transparent Optical Networks (ICTON).

[9] Hao Jiang,et al. Optimal Design of Gain-Flattened Raman Fiber Amplifiers Using a Hybrid Approach Combining Randomized Neural Networks and Differential Evolution Algorithm , 2018, IEEE Photonics Journal.

[10] Chee Kheong Siew,et al. Extreme learning machine: Theory and applications , 2006, Neurocomputing.