  Future vehicular communication networks call for new solutions to support
their capacity demands, by leveraging the potential of the millimeter-wave
(mm-wave) spectrum. Mobility, in particular, poses severe challenges in their
design, and as such shall be accounted for. A key question in mm-wave vehicular
networks is how to optimize the trade-off between directive Data Transmission
(DT) and directional Beam Training (BT), which enables it. In this paper,
learning tools are investigated to optimize this trade-off. In the proposed
scenario, a Base Station (BS) uses BT to establish a mm-wave directive link
towards a Mobile User (MU) moving along a road. To control the BT/DT trade-off,
a Partially Observable (PO) Markov Decision Process (MDP) is formulated, where
the system state corresponds to the position of the MU within the road link.
The goal is to maximize the number of bits delivered by the BS to the MU over
the communication session, under a power constraint. The resulting optimal
policies reveal that adaptive BT/DT procedures significantly outperform
common-sense heuristic schemes, and that specific mobility features, such as
user position estimates, can be effectively used to enhance the overall system
performance and optimize the available system resources.
