39 citations · 68 across the 4 of their papers we have counts for
4 papers
Deep Learning-Aided Perturbation Model-Based Fiber Nonlinearity Compensation
Shenghang Luo, Sunish Kumar Orappanpara Soman, Lutz Lampe +1
Fiber nonlinearity effects cap achievable rates and ranges in long-haul optical fiber communication links. Conventional nonlinearity compensation methods, such as perturbation theo…
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…
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…
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…