Differentiable Machine Learning-Based Modeling for Directly-Modulated Lasers ...

• Main Authors: Hernandez, Sergio, Jovanovic, Ognjen, Peucheret, Christophe, Da Ros, Francesco, Zibar, Darko
• Format: Text
• Language: unknown
• Published: arXiv 2023
• Subjects: Signal Processing eess.SP; Information Theory cs.IT; FOS Electrical engineering, electronic engineering, information engineering; FOS Computer and information sciences; DML
• Online Access: https://dx.doi.org/10.48550/arxiv.2309.15747; https://arxiv.org/abs/2309.15747

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. ... : final version to Photonics Technology Letters (02/01/2024) ...