Research Article
Cluster Analysis Based Traffic Prediction Method from Real Traffic Traces in LTE Networks
@INPROCEEDINGS{10.4108/icst.chinacom.2014.256309, author={Shimeng Zhang and Da Ma and Jingbao Gao and Jun Gu and Xing Zhang}, title={Cluster Analysis Based Traffic Prediction Method from Real Traffic Traces in LTE Networks}, proceedings={9th International Conference on Communications and Networking in China}, publisher={IEEE}, proceedings_a={CHINACOM}, year={2015}, month={1}, keywords={lte cluster analysis network traffic resource forecast}, doi={10.4108/icst.chinacom.2014.256309} }- Shimeng Zhang
Da Ma
Jingbao Gao
Jun Gu
Xing Zhang
Year: 2015
Cluster Analysis Based Traffic Prediction Method from Real Traffic Traces in LTE Networks
CHINACOM
IEEE
DOI: 10.4108/icst.chinacom.2014.256309
Abstract
With the development and wide application of LTE technology, the behavior of mobile users has been changed significantly. Compared with traditional networks, LTE system has different traffic model. Therefore, analytical methods should also be changed. In this paper, the relationship between traffic and radio resources is analyzed combined with the real LTE network data. We found that the correlation between traffic and resources in LTE networks is much more complex than that in traditional networks like 2G and 3G, which can be exploited to predict their trends. So we employ a prediction method based on multi-dimensional correlation and cluster analysis to predict traffic and resources. By analyzing a large number of historical data from current LTE systems, this method can not only improve the prediction accuracy but also greatly reduce the complexity of the algorithm. Therefore, this paper provides some guides/guidelines for the planning of network resources.