End-to-End Learning of Geometrical Shaping Maximizing Generalized Mutual Information
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2019
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866914649863094272 |
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| author | Gümüs, Kadir Alvarado, Alex Chen, Bin Häger, Christian Agrell, Erik |
| author_facet | Gümüs, Kadir Alvarado, Alex Chen, Bin Häger, Christian Agrell, Erik |
| contents | GMI-based end-to-end learning is shown to be highly nonconvex. We apply gradient descent initialized with Gray-labeled APSK constellations directly to the constellation coordinates. State-of-the-art constellations in 2D and 4D are found providing reach increases up to 26\% w.r.t. to QAM. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1912_05638 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | End-to-End Learning of Geometrical Shaping Maximizing Generalized Mutual Information Gümüs, Kadir Alvarado, Alex Chen, Bin Häger, Christian Agrell, Erik Signal Processing Artificial Intelligence Information Theory Machine Learning GMI-based end-to-end learning is shown to be highly nonconvex. We apply gradient descent initialized with Gray-labeled APSK constellations directly to the constellation coordinates. State-of-the-art constellations in 2D and 4D are found providing reach increases up to 26\% w.r.t. to QAM. |
| title | End-to-End Learning of Geometrical Shaping Maximizing Generalized Mutual Information |
| topic | Signal Processing Artificial Intelligence Information Theory Machine Learning |
| url | https://arxiv.org/abs/1912.05638 |