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

no code implementations • 5 Apr 2022 • Vladislav Neskorniuk, Andrea Carnio, Domenico Marsella, Sergei K. Turitsyn, Jaroslaw E. Prilepsky, Vahid Aref

Autoencoder-based deep learning is applied to jointly optimize geometric and probabilistic constellation shaping for optical coherent communication.

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

no code implementations • 26 Jul 2021 • Vladislav Neskorniuk, Andrea Carnio, Vinod Bajaj, Domenico Marsella, Sergei K. Turitsyn, Jaroslaw E. Prilepsky, Vahid Aref

We present a novel end-to-end autoencoder-based learning for coherent optical communications using a "parallelizable" perturbative channel model.