no code implementations • 24 Jun 2022 • Yihao Zhang, Xiaomin Liu, Yichen Liu, Lilin Yi, Weisheng Hu, Qunbi Zhuge
Based on the physical features of Raman amplification, we propose a three-step modelling scheme based on neural networks (NN) and linear regression.
no code implementations • 22 Jun 2022 • Qi Wu, Yixiao Zhu, Hexun Jiang, Qunbi Zhuge, Weisheng Hu
For cost-sensitive short-reach optical networks, some advanced single-polarization (SP) optical field recovery schemes are recently proposed to avoid chromatic dispersion-induced power fading effect, and improve the spectral efficiency for larger potential capacity.
no code implementations • 13 Jun 2022 • Xiaomin Liu, Yuli Chen, Yihao Zhang, Yichen Liu, Lilin Yi, Weisheng Hu, Qunbi Zhuge
We propose a physics-informed EDFA gain model based on the active learning method.
no code implementations • 23 Mar 2021 • Zhiqun Zhai, Hexun Jiang, Mengfan Fu, Lei Liu, Lilin Yi, Weisheng Hu, Qunbi Zhuge
In this paper, we propose a scheme that utilizes the optimization ability of artificial intelligence (AI) for optimal transceiver-joint equalization in compensating for the optical filtering impairments caused by wavelength selective switches (WSS).
no code implementations • 21 Dec 2020 • Yiwen Wu, Mengfan Fu, Huazhi Lun, Lilin Yi, Weisheng Hu, Qunbi Zhuge
We propose a degenerated hierarchical look-up table (DH-LUT) scheme to compensate component nonlinearities.
no code implementations • 24 Nov 2020 • Xiaomin Liu, Huazhi Lun, Ruoxuan Gao, Meng Cai, Lilin Yi, Weisheng Hu, Qunbi Zhuge
For further improving the capacity and reliability of optical networks, a closed-loop autonomous architecture is preferred.
no code implementations • 23 Nov 2018 • Qunbi Zhuge, Xiaobo Zeng, Huazhi Lun, Meng Cai, Xiaomin Liu, Weisheng Hu
In this paper, we present the application of machine learning (ML) in NLI modeling and monitoring.