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

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

Publication

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

2018Book part

dc.contributor.author	Torres, Pedro
dc.contributor.author	Marques, Hugo
dc.contributor.author	Marques, Paulo
dc.contributor.author	Rodriguez, Jonathan
dc.date.accessioned	2018-05-09T14:02:19Z
dc.date.available	2018-05-09T14:02:19Z
dc.date.issued	2018
dc.description	“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 "	pt_PT
dc.description.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.	pt_PT
dc.description.version	info:eu-repo/semantics/publishedVersion	pt_PT
dc.identifier.citation	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	pt_PT
dc.identifier.doi	https://doi.org/10.1007/978-3-319-76207-4_23	pt_PT
dc.identifier.isbn	978-3-319-76206-7
dc.identifier.uri	http://hdl.handle.net/10400.11/6075
dc.language.iso	eng	pt_PT
dc.peerreviewed	yes	pt_PT
dc.publisher	Springer	pt_PT
dc.relation	17787 POCI-01-0247-FEDER-MUSCLES	pt_PT
dc.relation.publisherversion	https://link.springer.com/chapter/10.1007/978-3-319-76207-4_23	pt_PT
dc.rights.uri	http://creativecommons.org/licenses/by-nd/4.0/	pt_PT
dc.subject	LTE	pt_PT
dc.subject	SON	pt_PT
dc.subject	Machine learning	pt_PT
dc.subject	Deep learning	pt_PT
dc.subject	Forecasting	pt_PT
dc.title	Using deep neural networks for forecasting cell congestion on LTE networks: a simple approach	pt_PT
dc.type	book part
dspace.entity.type	Publication
oaire.citation.endPage	286	pt_PT
oaire.citation.startPage	276	pt_PT
oaire.citation.title	Cognitive Radio Oriented Wireless Networks	pt_PT
person.familyName	BAPTISTA TORRES
person.familyName	Marques
person.familyName	Marques
person.givenName	PEDRO MIGUEL
person.givenName	Hugo
person.givenName	Paulo
person.identifier	K-5331-2015
person.identifier.ciencia-id	2711-E707-519C
person.identifier.ciencia-id	6313-B906-ED27
person.identifier.orcid	0000-0003-4835-5022
person.identifier.orcid	0000-0001-5762-4912
person.identifier.orcid	0000-0002-1788-651X
person.identifier.scopus-author-id	56261515100
person.identifier.scopus-author-id	25225486200
person.identifier.scopus-author-id	7006399225
rcaap.rights	restrictedAccess	pt_PT
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