Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.11/6076
Título: Data analytics for forecasting cell congestion on LTE networks
Autor: Torres, Pedro
Marques, Paulo
Marques, Hugo
Dionísio, Rogério
Alves, Tiago
Pereira, Luis
Ribeiro, Jorge
Palavras-chave: LTE
Machine Learning
Data: 2017
Citação: 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.
Resumo: 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.
Descrição: “© © 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.”
Peer review: yes
URI: http://hdl.handle.net/10400.11/6076
DOI: 10.23919/TMA.2017.8002917
Versão do Editor: https://ieeexplore.ieee.org/document/8002917/
Aparece nas colecções:ESTCB - Comunicações em encontros científicos e técnicos

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