Name
Title | ||
|---|---|---|
CMDNet: Learning a Probabilistic Relaxation of Discrete Variables for Soft Detection With Low Complexity |
Abstract | ||
|---|---|---|
Following the great success of Machine Learning (ML), especially Deep Neural Networks (DNNs), in many research domains in 2010s, several ML-based approaches were proposed for detection in large inverse linear problems, e.g., massive MIMO systems. The main motivation behind is that the complexity of Maximum A-Posteriori (MAP) detection grows exponentially with system dimensions. Instead of using DN... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TCOMM.2021.3114682 | IEEE Transactions on Communications |
Keywords | DocType | Volume |
Complexity theory,MIMO communication,Probabilistic logic,Decoding,Optimization,Detectors,Computational modeling | Journal | 69 |
Issue | ISSN | Citations |
12 | 0090-6778 | 1 |
PageRank | References | Authors |
0.36 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Edgar Beck | 1 | 1 | 0.36 |
| Carsten Bockelmann | 2 | 279 | 24.67 |
| Armin Dekorsy | 3 | 513 | 57.91 |