Name
Abstract | ||
|---|---|---|
End-to-end learning of communications systems is a fascinating novel concept that has so far only been validated by simulations for block-based transmissions. It allows learning of transmitter and receiver implementations as deep neural networks (NNs) that are optimized for an arbitrary differentiable end-to-end performance metric, e.g., block error rate (BLER). In this paper, we demonstrate that ... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/JSTSP.2017.2784180 | IEEE Journal of Selected Topics in Signal Processing |
Keywords | DocType | Volume |
Training,Receivers,Communication systems,Artificial neural networks,Hardware,Transmitters,Synchronization | Conference | 12 |
Issue | ISSN | Citations |
1 | 1932-4553 | 46 |
PageRank | References | Authors |
1.81 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sebastian Dörner | 1 | 54 | 2.98 |
| sebastian cammerer | 2 | 157 | 16.76 |
| Jakob Hoydis | 3 | 2121 | 112.59 |
| Stephan ten Brink | 4 | 2912 | 204.86 |