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Conferences > CLEO_SI > 2023 > SF1F > SF1F.6

Physics-inspired End-to-End Deep Learning for High-Performance Optical Fiber Transmission Links

I. Roumpos, L. De Marinis, G. Mourgias-Alexandris, M. Kirtas, N. Passalis, A. Tefas, G. Contestabile, K. Vyrsokinos, N. Pleros, and M. Moralis-Pegios

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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.

© 2023 The Author(s)

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