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Zenodo
2025
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| Online Access: | https://doi.org/10.1364/OE.468836 |
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| author | Oliveira, B. M. Neves, M. S. Guiomar, F. P. Medeiros, M. C. R. Monteiro, Paulo P. |
| author_facet | Oliveira, B. M. Neves, M. S. Guiomar, F. P. Medeiros, M. C. R. Monteiro, Paulo P. |
| contents | <p>With the increasing data rate requirements on short-reach links, the recent standardization of unamplified coherent optical systems is paving the way for a cost and power-effective solution, targeting a massive deployment in the near future. However, unamplified systems are introducing new challenges. Particularly, the performance is highly dependent on the peak-to-average power ratio (PAPR) of the transmitted signal, which puts at question the use of the typical constellation formats. In this work, we use an end-to-end deep learning framework to optimize the geometry of different constellation sizes, ranging from 8- to 128-ary constellations. In general, it is shown that the performance of these systems is maximized with constellations whose outer symbols are disposed in a square shape, owing to the minimization of the real-valued PAPR. Following this premise, we experimentally demonstrate that odd-bit constellations can be significantly optimized for unamplified coherent links, achieving power budget gains in the range of 0.5–3 dB through the geometric optimization of 8-, 32- and 128-ary constellations.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_1364_OE_468836 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | End-to-end deep learning of geometric shaping for unamplified coherent systems Oliveira, B. M. Neves, M. S. Guiomar, F. P. Medeiros, M. C. R. Monteiro, Paulo P. <p>With the increasing data rate requirements on short-reach links, the recent standardization of unamplified coherent optical systems is paving the way for a cost and power-effective solution, targeting a massive deployment in the near future. However, unamplified systems are introducing new challenges. Particularly, the performance is highly dependent on the peak-to-average power ratio (PAPR) of the transmitted signal, which puts at question the use of the typical constellation formats. In this work, we use an end-to-end deep learning framework to optimize the geometry of different constellation sizes, ranging from 8- to 128-ary constellations. In general, it is shown that the performance of these systems is maximized with constellations whose outer symbols are disposed in a square shape, owing to the minimization of the real-valued PAPR. Following this premise, we experimentally demonstrate that odd-bit constellations can be significantly optimized for unamplified coherent links, achieving power budget gains in the range of 0.5–3 dB through the geometric optimization of 8-, 32- and 128-ary constellations.</p> |
| title | End-to-end deep learning of geometric shaping for unamplified coherent systems |
| url | https://doi.org/10.1364/OE.468836 |