Recent Advances on Machine Learning-aided DSP for Short-reach and Long-haul Optical Communications
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arXiv
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| Hauptverfasser: | , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866929592545050624 |
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| author | Schmalen, Laurent Lauinger, Vincent Ney, Jonas Wehn, Norbert Matalla, Patrick Randel, Sebastian von Bank, Alexander Edelmann, Eike-Manuel |
| author_facet | Schmalen, Laurent Lauinger, Vincent Ney, Jonas Wehn, Norbert Matalla, Patrick Randel, Sebastian von Bank, Alexander Edelmann, Eike-Manuel |
| contents | In this paper, we highlight recent advances in the use of machine learning for implementing equalizers for optical communications. We highlight both algorithmic advances as well as implementation aspects using conventional and neuromorphic hardware. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_10101 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Recent Advances on Machine Learning-aided DSP for Short-reach and Long-haul Optical Communications Schmalen, Laurent Lauinger, Vincent Ney, Jonas Wehn, Norbert Matalla, Patrick Randel, Sebastian von Bank, Alexander Edelmann, Eike-Manuel Signal Processing Machine Learning In this paper, we highlight recent advances in the use of machine learning for implementing equalizers for optical communications. We highlight both algorithmic advances as well as implementation aspects using conventional and neuromorphic hardware. |
| title | Recent Advances on Machine Learning-aided DSP for Short-reach and Long-haul Optical Communications |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2411.10101 |