  Domain-wide recognized by their high value in human presence and activity
studies, cellular network datasets (i.e., Charging Data Records, named CdRs),
however, present accessibility, usability, and privacy issues, restricting
their exploitation and research reproducibility.This paper tackles such
challenges by modeling Cdrs that fulfill real-world data attributes. Our
designed framework, named Zen follows a four-fold methodology related to (i)
the LTSM-based modeling of users' traffic behavior, (ii) the realistic and
flexible emulation of spatiotemporal mobility behavior, (iii) the structure of
lifelike cellular network infrastructure and social interactions, and (iv) the
combination of the three previous modules into realistic Cdrs traces with an
individual basis, realistically. Results show that Zen's first and third models
accurately capture individual and global distributions of a fully anonymized
real-world Cdrs dataset, while the second model is consistent with the
literature's revealed features in human mobility. Finally, we validate Zen Cdrs
ability of reproducing daily cellular behaviors of the urban population and its
usefulness in practical networking applications such as dynamic population
tracing, Radio Access Network's power savings, and anomaly detection as
compared to real-world CdRs.
