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arXiv · 2309.15747

Differentiable Machine Learning-Based Modeling for Directly-Modulated Lasers

Sergio Hernandez·Ognjen Jovanovic·Christophe Peucheret·Francesco Da Ros·Darko Zibar
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Abstract

End-to-end learning has become a popular method for joint transmitter and receiver optimization in optical communication systems. Such approach may require a differentiable channel model, thus hindering the optimization of links based on directly modulated lasers (DMLs). This is due to the DML behavior in the large-signal regime, for which no analytical solution is available. In this paper, this problem is addressed by developing and comparing differentiable machine learning-based surrogate models. The models are quantitatively assessed in terms of root mean square error and training/testing time. Once the models are trained, the surrogates are then tested in a numerical equalization setup, resembling a practical end-to-end scenario. Based on the numerical investigation conducted, the convolutional attention transformer is shown to outperform the other models considered.

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Sergio Hernandez, Ognjen Jovanovic, Christophe Peucheret, Francesco Da Ros, Darko Zibar. 2023-09-27. Differentiable Machine Learning-Based Modeling for Directly-Modulated Lasers. https://doi.org/10.1109/lpt.2024.3350993

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