Using deep neural networks for forecasting cell congestion on LTE networks: a simple approach

bookPart

Torres, Pedro
Marques, Hugo
Marques, Paulo
Rodriguez, Jonathan

Springer

TORRES, Pedro [et.al.] (2018) - Using deep neural networks for forecasting cell congestion on LTE networks: a simple approach. In: MARQUES, P. [et al.] (eds) - Cognitive radio oriented wireless networks. CrownCom 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Cham: Springer. ISBN 978-3-319-76206-7. Vol. 228, p. 276-286

978-3-319-76206-7

Using deep neural networks for forecasting cell congestion on LTE networks: a simple approach

LTE
SON
Machine learning
Deep learning
Forecasting

17787 POCI-01-0247-FEDER-MUSCLES

2018-05-09T14:02:19Z
2018-05-09T14:02:19Z
2018

“This is a pre-copyedited version of a contribution published in Marques P., Radwan A., Mumtaz S., Noguet D., Rodriguez J., Gundlach M. (eds) Cognitive Radio Oriented Wireless Networks published by Springer. The definitive authenticated version is available online via https://doi.org/10.1007/978-3-319-76207-4_23 "
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.
info:eu-repo/semantics/publishedVersion

restrictedAccess
http://creativecommons.org/licenses/by-nd/4.0/

eng