Torres, PedroMarques, PauloMarques, HugoDionísio, Rogério PaisAlves, Tiago FerreiraPereira, Luis Miguel CardosoRibeiro, Jorge Miguel Afonso2018-05-092018-05-092017TORRES, 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.http://hdl.handle.net/10400.11/6076“© © 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.engLTESONMachine LearningForecastingconference object10.23919/TMA.2017.8002917