Name
Title | ||
|---|---|---|
The Deep Learning Vision for Heterogeneous Network Traffic Control: Proposal, Challenges, and Future Perspective. |
Abstract | ||
|---|---|---|
Recently, deep learning, an emerging machine learning technique, is garnering a lot of research attention in several computer science areas. However, to the best of our knowledge, its application to improve heterogeneous network traffic control (which is an important and challenging area by its own merit) has yet to appear because of the difficult challenge in characterizing the appropriate input ... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/MWC.2016.1600317WC | IEEE Wireless Communications |
Keywords | Field | DocType |
Machine learning,Neural networks,Heterogeneous networks,Routing,Google,Telecommunication traffic,Speech recognition | Open Shortest Path First,Competitive learning,Computer science,Computer network,Input/output,Time delay neural network,Artificial intelligence,Throughput,Deep learning,Artificial neural network,Distributed computing,Heterogeneous network,Machine learning | Journal |
Volume | Issue | ISSN |
24 | 3 | 1536-1284 |
Citations | PageRank | References |
25 | 0.94 | 4 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nei Kato | 1 | 3982 | 263.66 |
| Zubair Md. Fadlullah | 2 | 756 | 45.47 |
| Bomin Mao | 3 | 265 | 13.95 |
| Fengxiao Tang | 4 | 96 | 4.83 |
| Osamu Akashi | 5 | 219 | 23.80 |
| Takeru Inoue | 6 | 176 | 19.11 |
| Kimihiro Mizutani | 7 | 135 | 10.73 |