  This paper investigates highly mobile vehicular networks from users'
perspectives in highway transportation. Particularly, a centralized
software-defined architecture is introduced in which centralized resources can
be assigned, programmed, and controlled using the anchor nodes (ANs) of the
edge servers. Unlike the legacy networks, where a typical user is served from
only one access point (AP), in the proposed system model, a vehicle user is
served from multiple APs simultaneously. While this increases the reliability
and the spectral efficiency of the assisted users, it also necessitates an
accurate power allocation in all transmission time slots. As such, a joint user
association and power allocation problem is formulated to achieve enhanced
reliability and weighted user sum rate. However, the formulated problem is a
complex combinatorial problem, remarkably hard to solve. Therefore,
fine-grained machine learning algorithms are used to efficiently optimize joint
user associations and power allocations of the APs in a highly mobile vehicular
network. Furthermore, a distributed single-agent reinforcement learning
algorithm, namely SARL-MARL, is proposed which obtains nearly identical
genie-aided optimal solutions within a nominal number of training episodes than
the baseline solution. Simulation results validate that our solution
outperforms existing schemes and can attain genie-aided optimal performances.
