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Deep Learning-Aided Perturbation Model-Based Fiber Nonlinearity Compensation
Second, we advance the state-of-the-art of learned PB-NLC by proposing and designing a fully learned structure.
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.
Perturbation Theory-Aided Learned Digital Back-Propagation Scheme for Optical Fiber Nonlinearity Compensation
The proposed scheme is evaluated by numerical simulations of a single carrier optical fiber communication system operating at 32 Gbaud with 64-quadrature amplitude modulation and 20*80 km transmission distance.
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.
