Name
Title | ||
|---|---|---|
Improving Traffic Forecasting for 5G Core Network Scalability: A Machine Learning Approach. |
Abstract | ||
|---|---|---|
5G is expected to provide network connectivity to not only classical devices (i.e., tablets, smartphones, etc.) but also to the IoT, which will drastically increase the traffic load carried over the network. 5G will mainly rely on NFV and SDN to build flexible and on-demand instances of functional networking entities via VNFs. Indeed, 3GPP is devising a new architecture for the core network, which... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/MNET.2018.1800104 | IEEE Network |
Keywords | Field | DocType |
5G mobile communication,Scalability,3GPP,Telecommunication traffic,Cloud computing,Networked control systems,Delays,Traffic control,Load management,Software defined networking,Smart devices,Dynamic scheduling | Load management,Computer science,Core network,Computer network,Cellular network,Artificial neural network,Dynamic priority scheduling,Software-defined networking,Cloud computing,Scalability,Distributed computing | Journal |
Volume | Issue | ISSN |
32 | 6 | 0890-8044 |
Citations | PageRank | References |
9 | 0.55 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Imad Alawe | 1 | 9 | 0.55 |
| Adlen Ksentini | 2 | 1085 | 93.20 |
| Y. Hadjadj-Aoul | 3 | 37 | 4.28 |
| Philippe Bertin | 4 | 64 | 9.44 |