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Electrical and electronic engineering
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.
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.