Name
Abstract | ||
|---|---|---|
Conventional soft demappers designed for AWGN channels suffer from performance loss under realistic channels. We propose a neural network soft demapper and show a gain of 0.35dB in an 800Gb/s coherent transmission experiment using DP-32QAM. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1364/OFC.2020.W3D.2 | OFC |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maximilian Schaedler | 1 | 0 | 0.68 |
| Stefano Calabrò | 2 | 5 | 6.38 |
| Fabio Pittalà | 3 | 0 | 0.34 |
| Christian Bluemm | 4 | 0 | 0.68 |
| m kuschnerov | 5 | 5 | 3.63 |
| stephan pachnicke | 6 | 2 | 8.26 |