Network operators are interested in continuously monitoring the satisfaction of their customers to minimise the churn rate: however, collecting user feedbacks through surveys is a cumbersome task. In this work we explore the possibility of predicting the long-term user satisfaction relative to network coverage and video streaming starting from user-side network measurements only. We leverage country-wide datasets to engineer features which are then used to train several machine learning models. The obtained results suggest that, although some correlation is visible and could be exploited by the classifiers, long-term user satisfaction prediction from network measurements is a very challenging task: we therefore point out possible action points to be implemented to improve the prediction results.
We analyze a city-wide dataset of 4G mobile network traffic obtained directly from user-side logs, allowing fine-grained analyses of different application services over time and space. We group applications in classes and analyze their traffic patterns: the analysis reveals great heterogeneity in the usage of different applications and in their space/time correlations, with important implications for future networking services such as network slicing and resource allocations.
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