A machine learning management model for QoE enhancement in next-generation wireless ecosystems

Authors

Eva Ibarrola, University of the Basque Country
Mark Davis, Technological University DublinFollow
Camille Voisin, OptiWi-fi, Ireland
Ciara Close, OptiWi-fi, Ireland
Leire Cristobo, University of the Basque Country

Document Type

Conference Paper

Rights

Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence

Disciplines

Electrical and electronic engineering

Publication Details

2018 ITU Kaleidoscope: Machine Learning for a 5G Future (ITU K) (Publisher: IEEE)

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.

DOI

https://doi.org/10.23919/ITU-WT.2018.8598032

Recommended Citation

E. Ibarrola, M. Davis, C. Voisin, C. Close and L. Cristobo, "A Machine Learning Management Model for QoE Enhancement in Next-Generation Wireless Ecosystems," 2018 ITU Kaleidoscope: Machine Learning for a 5G Future (ITU K), 2018, pp. 1-8, doi: 10.23919/ITU-WT.2018.8598032.