Differentiable Machine Learning-Based Modeling for Directly-Modulated Lasers
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866913355331010560 |
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| author | Hernandez, Sergio Jovanovic, Ognjen Peucheret, Christophe Da Ros, Francesco Zibar, Darko |
| author_facet | Hernandez, Sergio Jovanovic, Ognjen Peucheret, Christophe Da Ros, Francesco Zibar, Darko |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_15747 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Differentiable Machine Learning-Based Modeling for Directly-Modulated Lasers Hernandez, Sergio Jovanovic, Ognjen Peucheret, Christophe Da Ros, Francesco Zibar, Darko Signal Processing Information Theory 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. |
| title | Differentiable Machine Learning-Based Modeling for Directly-Modulated Lasers |
| topic | Signal Processing Information Theory |
| url | https://arxiv.org/abs/2309.15747 |