  Measuring customer experience on mobile data is of utmost importance for
global mobile operators. The reference signal received power (RSRP) is one of
the important indicators for current mobile network management, evaluation and
monitoring. Radio data gathered through the minimization of drive test (MDT), a
3GPP standard technique, is commonly used for radio network analysis.
Collecting MDT data in different geographical areas is inefficient and
constrained by the terrain conditions and user presence, hence is not an
adequate technique for dynamic radio environments. In this paper, we study a
generative model for RSRP prediction, exploiting MDT data and a digital twin
(DT), and propose a data-driven, two-tier neural network (NN) model. In the
first tier, environmental information related to user equipment (UE), base
stations (BS) and network key performance indicators (KPI) are extracted
through a variational autoencoder (VAE). The second tier is designed as a
likelihood model. Here, the environmental features and real MDT data features
are adopted, formulating an integrated training process. On validation, our
proposed model that uses real-world data demonstrates an accuracy improvement
of about 20% or more compared with the empirical model and about 10% when
compared with a fully connected prediction network.
