Name
Title | ||
|---|---|---|
Domain Adaptation: the Key Enabler of Neural Network Equalizers in Coherent Optical Systems |
Abstract | ||
|---|---|---|
We introduce the domain adaptation and randomization approach for calibrating neural network-based equalizers for real transmissions, using synthetic data. The approach renders up to 99% training process reduction, which we demonstrate in three experimental setups. (C) 2021 The Author(s) |
Year | Venue | DocType |
|---|---|---|
2022 | 2022 OPTICAL FIBER COMMUNICATIONS CONFERENCE AND EXHIBITION (OFC) | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
10 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pedro J. Freire | 1 | 0 | 0.34 |
| Bernhard Spinnler | 2 | 0 | 1.69 |
| Daniel Abode | 3 | 0 | 0.34 |
| Jaroslaw E. Prilepsky | 4 | 0 | 0.34 |
| Abdallah A. I. Ali | 5 | 0 | 0.34 |
| Nelson Costa | 6 | 0 | 7.44 |
| Wolfgang Schairer | 7 | 0 | 0.68 |
| Antonio Napoli | 8 | 1 | 8.93 |
| Andrew D. Ellis | 9 | 0 | 0.34 |
| Sergei K. Turitsyn | 10 | 0 | 0.34 |