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20212024
most citedPerturbation Theory-Aided Learned Digital Back-Propagation Scheme for Optical Fiber Nonlinearity Compensation

39 citations · 41 across the 5 of their papers we have counts for

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5 papers

cs.IT2024

Experimental Comparison of Average-Power Constrained and Peak-Power Constrained 64QAM under Optimal Clipping in 400Gbps Unamplified Coherent Links

Wing-Chau Ng, Chuandong Li

We experimentally demonstrated an end-to-end link budget optimization over clipping in 400Gbps unamplified links, showing that the clipped MB distribution outperforms the peak-powe…

eess.SP2022★ 1 cited

Learning for Perturbation-Based Fiber Nonlinearity Compensation

Shenghang Luo, Sunish Kumar Orappanpara Soman, Lutz Lampe +2

Several machine learning inspired methods for perturbation-based fiber nonlinearity (PBNLC) compensation have been presented in recent literature. We critically revisit acclaimed b…

eess.SP2021★ 39 cited

Perturbation Theory-Aided Learned Digital Back-Propagation Scheme for Optical Fiber Nonlinearity Compensation

Xiang Lin, Shenghang Luo, Sunish Kumar Orappanpara Soman +4

Derived from the regular perturbation treatment of the nonlinear Schrodinger equation, a machine learning-based scheme to mitigate the intra-channel optical fiber nonlinearity is p…

eess.SP2021★ 1 cited

Deep Neural Network Assisted Second-Order Perturbation-Based Nonlinearity Compensation

O. S. Sunish Kumar, Lutz Lampe, Shenghang Luo +3

We propose a fiber nonlinearity post-compensation technique using the DNN and the second-order perturbation theory. We achieve 1 dB Q-factor improvement for a 32 Gbaud PDM-64-QAM a…

cs.IT2021

Classification of MIMO Equalizers

Wing Chau Ng, Chuandong Li

In this theoretical work, the DSP-perceived channel in optical coherent communications is first simplified, based on which we categorize linear MIMO equalizers into four classes ac…