Processos de envelhecimento em portugal
cop. 2013
Data analytics for forecasting cell congestion on LTE networks
Type
conferenceObject
Creator
Identifier
TORRES, P. [et al.] (2017) - Data analytics for forecasting cell congestion on LTE networks. In Network Traffic Measurement and Analysis Conference (TMA), Dublin, 21-23 junho. [S.l.]: IEEE. pp. 1-6.
10.23919/TMA.2017.8002917
Title
Data analytics for forecasting cell congestion on LTE networks
Subject
LTE
SON
Machine Learning
Forecasting
SON
Machine Learning
Forecasting
Relation
info:eu-repo/grantAgreement/EC/H2020/644399/EU
17787 POCI-01-0247-FEDER-MUSCLES
17787 POCI-01-0247-FEDER-MUSCLES
Date
2018-05-09T14:23:42Z
2018-05-09T14:23:42Z
2017
2018-05-09T14:23:42Z
2017
Description
“© © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”
This paper presents a methodology for forecasting the average downlink throughput for an LTE cell by using real measurement data collected by multiple LTE probes. The approach uses data analytics techniques, namely forecasting algorithms to anticipate cell congestion events which can then be used by Self-Organizing Network (SON) strategies for triggering network re-configurations, such as shifting coverage and capacity to areas where they are most needed, before subscribers have been impacted by dropped calls or reduced data speeds. The presented implementation results show the prediction of network behaviour is possible with a high level of accuracy, effectively allowing SON strategies to be enforced in time.
info:eu-repo/semantics/publishedVersion
This paper presents a methodology for forecasting the average downlink throughput for an LTE cell by using real measurement data collected by multiple LTE probes. The approach uses data analytics techniques, namely forecasting algorithms to anticipate cell congestion events which can then be used by Self-Organizing Network (SON) strategies for triggering network re-configurations, such as shifting coverage and capacity to areas where they are most needed, before subscribers have been impacted by dropped calls or reduced data speeds. The presented implementation results show the prediction of network behaviour is possible with a high level of accuracy, effectively allowing SON strategies to be enforced in time.
info:eu-repo/semantics/publishedVersion
Access restrictions
restrictedAccess
http://creativecommons.org/licenses/by-nd/4.0/
http://creativecommons.org/licenses/by-nd/4.0/
Language
eng
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