Name
Title | ||
|---|---|---|
Wavelet Transform Processing For Cellular Traffic Prediction In Machine Learning Networks |
Abstract | ||
|---|---|---|
The ability for cellular operators to closely predict the network traffic volume at various locations can be very important for their resource management and dynamic network control including offloading. This work investigate the analysis of the spatial-temporal information of cellular traffic flow and the prediction of cell-station traffic volumes. Based on the integration of K-means clustering, Elman Neural Network (Elman-NN), and wavelet decomposition methods, we characterize the performance comparison of traffic volume prediction. We tested our wavelet decomposition based machine learning approach using the real traffic data recorded at a district in a big city and demonstrated the gain over traditional approaches. |
Year | Venue | Keywords |
|---|---|---|
2015 | 2015 IEEE CHINA SUMMIT & INTERNATIONAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING | Elman neural network, traffic flow prediction, wavelet decomposition |
Field | DocType | Citations |
Dynamic network analysis,Data mining,Base station,Traffic generation model,Time series,Computer science,Cellular traffic,Artificial intelligence,Cluster analysis,Artificial neural network,Machine learning,Wavelet transform | Conference | 3 |
PageRank | References | Authors |
0.41 | 6 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yunjuan Zang | 1 | 3 | 0.41 |
| Feixiang Ni | 2 | 3 | 0.41 |
| Zhiyong Feng | 3 | 794 | 167.21 |
| Shuguang Cui | 4 | 5382 | 368.45 |
| Zhi Ding | 5 | 21 | 3.37 |