Name
Title | ||
|---|---|---|
A Deep-Learning-Based Radio Resource Assignment Technique for 5G Ultra Dense Networks. |
Abstract | ||
|---|---|---|
Recently, deep learning has emerged as a state-of-the-art machine learning technique with promising potential to drive significant breakthroughs in a wide range of research areas. The application of deep learning for network traffic control, however, remains immature due to the difficulty in uniquely characterizing the network traffic features as an appropriate input and output dataset to the lear... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/MNET.2018.1800085 | IEEE Network |
Keywords | Field | DocType |
5G mobile communication,Array signal processing,MIMO communication,Telecommunication traffic,Dynamic scheduling,Traffic control,Networked traffic control,Predictive models,Deep learning | Base station,Beamforming,Computer science,MIMO,Computer network,Input/output,Radio Resource Control,Artificial intelligence,Deep learning,Throughput,Network traffic control,Distributed computing | Journal |
Volume | Issue | ISSN |
32 | 6 | 0890-8044 |
Citations | PageRank | References |
9 | 0.45 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yibo Zhou | 1 | 9 | 0.45 |
| Zubair Md. Fadlullah | 2 | 756 | 45.47 |
| Bomin Mao | 3 | 265 | 13.95 |
| Nei Kato | 4 | 3982 | 263.66 |