Research Article
Using Deep Neural Networks for Forecasting Cell Congestion on LTE Networks: A Simple Approach
@INPROCEEDINGS{10.1007/978-3-319-76207-4_23, author={Pedro Torres and Hugo Marques and Paulo Marques and Jonathan Rodriguez}, title={Using Deep Neural Networks for Forecasting Cell Congestion on LTE Networks: A Simple Approach}, proceedings={Cognitive Radio Oriented Wireless Networks. 12th International Conference, CROWNCOM 2017, Lisbon, Portugal, September 20-21, 2017, Proceedings}, proceedings_a={CROWNCOM}, year={2018}, month={3}, keywords={LTE SON Machine learning Deep learning Forecasting}, doi={10.1007/978-3-319-76207-4_23} }- Pedro Torres
Hugo Marques
Paulo Marques
Jonathan Rodriguez
Year: 2018
Using Deep Neural Networks for Forecasting Cell Congestion on LTE Networks: A Simple Approach
CROWNCOM
Springer
DOI: 10.1007/978-3-319-76207-4_23
Abstract
Predicting short-term cellular load in LTE networks is of great importance for mobile operators as it assists in the efficient managing of network resources. Based on predicted behaviours, the network can be intended as a proactive system that enables reconfiguration when needed. Basically, it is the concept of self-organizing networks that ensures the requirements and the quality of service. This paper uses a dataset, provided by a mobile network operator, of collected downlink throughput samples from one cell in an area where cell congestion usually occurs and a Deep Neural Network (DNN) approach to perform short-term cell load forecasting. The results obtained indicate that DNN performs better results when compared to traditional approaches.
