RT Journal Article
T1 QoE estimation in mobile networks using machine learning
A1 Mesquita, Jorge
A1 Osorio, Guillermo
A1 García-Diaz, María
A1 García Torres, Miguel
A1 Pinto Roa, Diego Pedro
A1 Núñez Castillo, Carlos Heriberto
A2 Universidad Nacional de Asunción - Facultad Politécnica
AB Quality of experience (QoE) can be defined as the overall level of acceptability of an application or service, as perceived by the end-user.The perceived QoE of mobile user plays a key role in the business of the telecom carriers. This work has focused in design a model capable of predicting the QoE of the end-user using a number of Machine Learning approaches, based on quality of service (QoS) metrics from different sources like the mobile device, the mobile network and also subjective metrics given by the user (QoE and Mood surveys) in a real life setup. An android app, a metric collection platform, a system for data processing and semi-automatic analysis of metrics has been developed as a part of this work. The experimental results show that by assembling a combined model of the algorithms with best observed individual performance, improvements in the overall performance of the prediction can be achieved.
YR 2019
FD 2019
LK http://hdl.handle.net/20.500.14066/3733
UL http://hdl.handle.net/20.500.14066/3733
LA eng
NO CONACYT – Consejo Nacional de Ciencia y Tecnología
DS MINDS@UW
RD 04-nov-2024