End-to-End Deep Learning of Long-Haul Coherent Optical Fiber Communications via Regular Perturbation Model

07/26/2021

∙

We present a novel end-to-end autoencoder-based learning for coherent optical communications using a "parallelizable" perturbative channel model. We jointly optimized constellation shaping and nonlinear pre-emphasis achieving mutual information gain of 0.18 bits/sym./pol. simulating 64 GBd dual-polarization single-channel transmission over 30x80 km G.652 SMF link with EDFAs.

READ FULL TEXT

End-to-End Deep Learning of Long-Haul Coherent Optical Fiber Communications via Regular Perturbation Model

Model-Based Deep Learning of Joint Probabilistic and Geometric Shaping for Optical Communication

Deep-learning Autoencoder for Coherent and Nonlinear Optical Communication

Intra-Channel Nonlinearity Compensation Based on Second-Order Perturbation Theory

Introducing γ-lifting for Learning Nonlinear Pulse Shaping in Coherent Optical Communication

On Discrete-Time/Frequency-Periodic End-to-End Fiber-Optical Channel Models

Classification of MIMO Equalizers

Achievable Information Rates for Nonlinear Fiber Communication via End-to-end Autoencoder Learning

End-to-End Deep Learning of Long-Haul Coherent Optical Fiber Communications via Regular Perturbation Model

Related Research

Model-Based Deep Learning of Joint Probabilistic and Geometric Shaping for Optical Communication

Deep-learning Autoencoder for Coherent and Nonlinear Optical Communication

Intra-Channel Nonlinearity Compensation Based on Second-Order Perturbation Theory

Introducing γ-lifting for Learning Nonlinear Pulse Shaping in Coherent Optical Communication

On Discrete-Time/Frequency-Periodic End-to-End Fiber-Optical Channel Models

Classification of MIMO Equalizers

Achievable Information Rates for Nonlinear Fiber Communication via End-to-end Autoencoder Learning