Experimental End-to-End Optimization of Directly Modulated Laser-based IM/DD Transmission
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866915694499594240 |
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| author | Hernandez, Sergio Peucheret, Christophe Da Ros, Francesco Zibar, Darko |
| author_facet | Hernandez, Sergio Peucheret, Christophe Da Ros, Francesco Zibar, Darko |
| contents | Directly modulated lasers (DMLs) are an attractive technology for short-reach intensity modulation and direct detection communication systems. However, their complex nonlinear dynamics make the modeling and optimization of DML-based systems challenging. In this paper, we study the end-to-end optimization of DML-based systems based on a data-driven surrogate model trained on experimental data. The end-to-end optimization includes the pulse shaping and equalizer filters, the bias current and the modulation radio-frequency (RF) power applied to the laser. The performance of the end-to-end optimization scheme is tested on the experimental setup and compared to 4 different benchmark schemes based on linear and nonlinear receiver-side equalization. The results show that the proposed end-to-end scheme is able to deliver better performance throughout the studied symbol rates and transmission distances while employing lower modulation RF power, fewer filter taps and utilizing a smaller signal bandwidth. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_19910 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Experimental End-to-End Optimization of Directly Modulated Laser-based IM/DD Transmission Hernandez, Sergio Peucheret, Christophe Da Ros, Francesco Zibar, Darko Signal Processing Machine Learning Directly modulated lasers (DMLs) are an attractive technology for short-reach intensity modulation and direct detection communication systems. However, their complex nonlinear dynamics make the modeling and optimization of DML-based systems challenging. In this paper, we study the end-to-end optimization of DML-based systems based on a data-driven surrogate model trained on experimental data. The end-to-end optimization includes the pulse shaping and equalizer filters, the bias current and the modulation radio-frequency (RF) power applied to the laser. The performance of the end-to-end optimization scheme is tested on the experimental setup and compared to 4 different benchmark schemes based on linear and nonlinear receiver-side equalization. The results show that the proposed end-to-end scheme is able to deliver better performance throughout the studied symbol rates and transmission distances while employing lower modulation RF power, fewer filter taps and utilizing a smaller signal bandwidth. |
| title | Experimental End-to-End Optimization of Directly Modulated Laser-based IM/DD Transmission |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2508.19910 |