We experimentally demonstrate the performance improvements obtained through End-to-End Deep Learning in noise and chromatic dispersion compensation of optical fiber transmission links when incorporating a physics-inspired activation function compared to state-of-the-art ReLU configurations.
Physics-inspired End-to-End Deep Learning for High-Performance Optical Fiber Transmission Links
De Marinis, L.;Contestabile, G.;
2023-01-01
Abstract
We experimentally demonstrate the performance improvements obtained through End-to-End Deep Learning in noise and chromatic dispersion compensation of optical fiber transmission links when incorporating a physics-inspired activation function compared to state-of-the-art ReLU configurations.File in questo prodotto:
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Physics-inspired End-to-End Deep Learning for High-Performance Optical Fiber Transmission Links [CLEO 2023] submitted.pdf
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