Differentiable Machine Learning-Based Modeling for Directly-Modulated Lasers

Sergio Hernandez Fernandez*, Ognjen Jovanovic, Christophe Peucheret, Francesco Da Ros, Darko Zibar

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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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 letter, 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.

Original languageEnglish
JournalIEEE Photonics Technology Letters
Volume36
Issue number4
Pages (from-to)266-269
ISSN1041-1135
DOIs
Publication statusPublished - 15 Feb 2024

Keywords

  • Directly modulated laser
  • Machine learning
  • Modeling
  • Optical communication
  • Transformer

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