@inproceedings{97f3eed6b0cd4bafa65c5724faf3242b,
title = "A machine learning management model for QoE enhancement in next-generation wireless ecosystems",
abstract = "Next-generation wireless ecosystems are expected to comprise heterogeneous technologies and diverse deployment scenarios. Ensuring a good quality of service (QoS) will be one of the major challenges of next-generation wireless systems on account of a variety of factors that are beyond the control of network and service providers. In this context, ITU-T is working on updating the various Recommendations related to QoS and users' quality of experience (QoE). Considering the ITU-T QoS framework, we propose a methodology to develop a global QoS management model for next-generation wireless ecosystems taking advantage of big data and machine learning. The results from a case study conducted to validate the model in real-world Wi-Fi deployment scenarios are also presented.",
keywords = "Big data, Machine learning, QoBiz, QoE, QoS, Wi-Fi",
author = "Eva Ibarrola and Mark Davis and Camille Voisin and Ciara Close and Leire Cristobo",
note = "Publisher Copyright: {\textcopyright} IEEE-CONFERENCE. All rights reserved.; 10th ITU Academic Conference Kaleidoscope: Machine Learning for a 5G Future, ITU K 2018 ; Conference date: 26-11-2018 Through 28-11-2018",
year = "2018",
month = dec,
day = "31",
doi = "10.23919/ITU-WT.2018.8598032",
language = "English",
series = "10th ITU Academic Conference Kaleidoscope: Machine Learning for a 5G Future, ITU K 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "10th ITU Academic Conference Kaleidoscope",
address = "United States",
}