GUMBLE: Uncertainty-Aware Conditional Mobile Data Generation using
Bayesian Learning
Abstract
In the context of mobile and Internet of Things (IoT) networks, data
naturally originates at the edge, making crowdsourcing a convenient and
inherent approach to data collection. However, crowdsourcing presents
challenges related to privacy, sampling bias, statistical sufficiency,
and the need for time-consuming post-processing. To this end, generating
synthetic data using Deep Learning techniques emerges as a promising
solution to overcome such limitations. In this study, we propose an
innovative framework that transcends applications and data types,
enabling the conditional generation of crowdsourced datasets with
location information in mobile and IoT networks. A crucial aspect of our
methodology is its ability to assess uncertainty in newly generated
samples and produce calibrated predictions through approximate Bayesian
methods. Without loss of generality, we ascertain the validity of our
method on the task of Minimization of Drive Test (MDT) data generation,
presenting for the first time a comparison of synthetically generated
data with an original large-scale MDT set collected from a Mobile
Network Operator’s network infrastructure. By offering a versatile
solution to data generation, our framework contributes to overcoming
challenges associated with crowdsourced data, opening up possibilities
for advanced analytics and experimentation in mobile and IoT networks.