23 citations · 29 across the 4 of their papers we have counts for
4 papers
Improved Physics-based Raman Amplifier Model in C+L Networks through Input Parameter Refinement
Yihao Zhang, Xiaomin Liu, Qizhi Qiu +4
We propose a n input parameter refinement scheme for the physics-based Raman amplifier model. Experiments over C+L band are conducted. Results show the scheme can lower the physica…
Building a digital twin of EDFA: a grey-box modeling approach
Yichen Liu, Xiaomin Liu, Yihao Zhang +6
To enable intelligent and self-driving optical networks, high-accuracy physical layer models are required. The dynamic wavelength-dependent gain effects of non-constant-pump erbium…
A Grey-box Launch-profile Aware Model for C+L Band Raman Amplification
Yihao Zhang, Xiaomin Liu, Yichen Liu +3
Based on the physical features of Raman amplification, we propose a three-step modelling scheme based on neural networks (NN) and linear regression. Higher accuracy, less data requ…
Physics-informed EDFA Gain Model Based on Active Learning
Xiaomin Liu, Yuli Chen, Yihao Zhang +4
We propose a physics-informed EDFA gain model based on the active learning method. Experimental results show that the proposed modelling method can reach a higher optimal accuracy…