A Machine Learning Management Model for QoE Enhancement in Next-Generation Wireless Ecosystems

• Published in: 2018 ITU Kaleidoscope: Machine Learning for a 5G Future (ITU K) pp. 1 - 8
• Main Authors: Ibarrola, Eva, Davis, Mark, Voisin, Camille, Close, Ciara, Cristobo, Leire
• Format: Conference Proceeding
• Language: English
• Published: ITU 01-11-2018
• Subjects: 5G mobile communication; Big data; Ecosystems; Machine learning; Next generation networking; QoBiz; QoE; QoS; Quality of experience; Quality of service; Wi-Fi; Wireless communication
• DOI: 10.23919/ITU-WT.2018.8598032

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.