Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.11/6075
Título: Using deep neural networks for forecasting cell congestion on LTE networks: a simple approach
Autor: Torres, Pedro
Marques, Hugo
Marques, Paulo
Rodriguez, Jonathan
Palavras-chave: LTE
SON
Machine learning
Deep learning
Forecasting
Data: 2018
Editora: Springer
Citação: 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 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 228. Cham: Springer. p. 276-286
Resumo: 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.
Descrição: “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 "
Peer review: yes
URI: http://hdl.handle.net/10400.11/6075
DOI: https://doi.org/10.1007/978-3-319-76207-4_23
ISBN: 978-3-319-76206-7
Versão do Editor: https://link.springer.com/chapter/10.1007/978-3-319-76207-4_23
Aparece nas colecções:ESTCB - Livros ou capítulos de livros

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