  Mobility modeling in 5G and beyond 5G must address typical features such as
time-varying correlation between mobility patterns of different nodes, and
their variation ranging from macro-mobility (kilometer range) to micro-mobility
(sub-meter range). Current models have strong limitations in doing so: the
widely used reference-based models, such as the Reference Point Group Mobility
(RPGM), lack flexibility and accuracy, while the more sophisticated rule-based
(i.e. behavioral) models are complex to set-up and tune. This paper introduces
a new rule-based Modular Mobility Model, named Mo3, that provides accuracy and
flexibility on par with behavioral models, while preserving the intuitiveness
of the reference-based approach, and is based on five rules: 1) Individual
Mobility, 2) Correlated Mobility, 3) Collision Avoidance, 4) Obstacle Avoidance
and 5) Upper Bounds Enforcement. Mo3 avoids introducing acceleration vectors to
define rules, as behavioral models do, and this significantly reduces
complexity. Rules are mapped one-to-one onto five modules, that can be
independently enabled or replaced. Comparison of time-correlation features
obtained with Mo3 vs. reference-based models, and in particular RPGM, in pure
micro-mobility and mixed macro-mobility / micro-mobility scenarios, shows that
Mo3 and RPGM generate mobility patterns with similar topological properties
(intra-group and inter-group distances), but that Mo3 preserves a spatial
correlation that is lost in RPGM - at no price in terms of complexity - making
it suitable for adoption in 5G and beyond 5G.

Networking and Internet Architecture