Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1364/ofc.2020.w3d.4 | OFC |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gümüs Kadir | 1 | 0 | 0.34 |
| Alex Alvarado | 2 | 110 | 27.09 |
| Chen Bin | 3 | 0 | 0.34 |
| Christian Häger | 4 | 1 | 3.06 |
| E. Agrell | 5 | 959 | 91.69 |