  Network operators need to continuosly upgrade their infrastructures in order
to keep their customer satisfaction levels high. Crowdsourcing-based approaches
are generally adopted, where customers are directly asked to answer surveys
about their user experience. Since the number of collaborative users is
generally low, network operators rely on Machine Learning models to predict the
satisfaction levels/QoE of the users rather than directly measuring it through
surveys. Finally, combining the true/predicted user satisfaction levels with
information on each user mobility (e.g, which network sites each user has
visited and for how long), an operator may reveal critical areas in the
networks and drive/prioritize investments properly. In this work, we propose an
empirical framework tailored to assess the quality of the detection of
under-performing cells starting from subjective user experience grades. The
framework allows to simulate diverse networking scenarios, where a network
characterized by a small set of under-performing cells is visited by
heterogeneous users moving through it according to realistic mobility models.
The framework simulates both the processes of satisfaction surveys delivery and
users satisfaction prediction, considering different delivery strategies and
evaluating prediction algorithms characterized by different prediction
performance. We use the simulation framework to test empirically the
performance of under-performing sites detection in general scenarios
characterized by different users density and mobility models to obtain insights
which are generalizable and that provide interesting guidelines for network
operators.
