Learning for Perturbation-Based Fiber Nonlinearity Compensation
Several machine learning inspired methods for perturbation-based fiber nonlinearity (PBNLC) compensation have been presented in recent literature. We critically revisit acclaimed benefits of those over non-learned methods. Numerical results suggest that learned linear processing of perturbation triplets of PB-NLC is preferable over feedforward neural-network solutions.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Deep Neural Network Assisted Second-Order Perturbation-Based Nonlinearity Compensation
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 at 1200 km compared to the linear dispers…
Deep Learning-Aided Perturbation Model-Based Fiber Nonlinearity Compensation
Fiber nonlinearity effects cap achievable rates and ranges in long-haul optical fiber communication links. Conventional nonlinearity compensation methods, such as perturbation theory-based nonlinearity compensation (PB-N…
Deep LearningQuantizationPerturbation Theory-Aided Learned Digital Back-Propagation Scheme for Optical Fiber Nonlinearity Compensation
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 proposed. Referred to as the perturbation…
Second-Order Perturbation Theory-Based Digital Predistortion for Fiber Nonlinearity Compensation
The first-order (FO) perturbation theory-based nonlinearity compensation (PB-NLC) technique has been widely investigated to combat the detrimental effects of the intra-channel Kerr nonlinearity in polarization-multiplexe…
Joint PMD Tracking and Nonlinearity Compensation with Deep Neural Networks
Overcoming fiber nonlinearity is one of the core challenges limiting the capacity of optical fiber communication systems. Machine learning based solutions such as learned digital backpropagation (LDBP) and the recently p…
Transfer Learning