Name
Title | ||
|---|---|---|
Citywide Cellular Traffic Prediction Based on Densely Connected Convolutional Neural Networks. |
Abstract | ||
|---|---|---|
With accurate traffic prediction, future cellular networks can make self-management and embrace intelligent and efficient automation. This letter devotes itself to citywide cellular traffic prediction and proposes a deep learning approach to model the nonlinear dynamics of wireless traffic. By treating traffic data as images, both the spatial and temporal dependence of cell traffic are well captur... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/LCOMM.2018.2841832 | IEEE Communications Letters |
Keywords | Field | DocType |
Computer architecture,Microprocessors,Wireless communication,Correlation,Predictive models,Convolution,Machine learning | Wireless,Computer science,Convolutional neural network,Cellular traffic,Mean squared error,Automation,Real-time computing,Parametric statistics,Artificial intelligence,Cellular network,Deep learning | Journal |
Volume | Issue | ISSN |
22 | 8 | 1089-7798 |
Citations | PageRank | References |
17 | 0.83 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chuanting Zhang | 1 | 39 | 4.76 |
| Haixia Zhang | 2 | 366 | 54.02 |
| Dongfeng Yuan | 3 | 80 | 9.09 |
| Minggao Zhang | 4 | 79 | 6.90 |