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.
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This work is funded by the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project Nr. 17787] (POCI-01-0247-FEDER-MUSCLES).
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Torres, P., Marques, H., Marques, P., Rodriguez, J. (2018). Using Deep Neural Networks for Forecasting Cell Congestion on LTE Networks: A Simple Approach. In: Marques, P., Radwan, A., Mumtaz, S., Noguet, D., Rodriguez, J., Gundlach, M. (eds) Cognitive Radio Oriented Wireless Networks. CrownCom 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 228. Springer, Cham. https://doi.org/10.1007/978-3-319-76207-4_23
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DOI: https://doi.org/10.1007/978-3-319-76207-4_23
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