  Flying and ground-based cars require various services such as autonomous
driving, remote pilot, infotainment, and remote diagnosis. Each service
requires specific Quality of Service (QoS) and network features. Therefore,
network slicing can be a solution to fulfill the requirements of various
services. Some services, such as infotainment, may have similar requirements to
serve flying and ground-based cars. Therefore, some slices can serve both kinds
of cars. However, when network slice resource sharing is too aggressive, slices
can not meet QoS requirements, where resource under-provisioning causes the
violation of QoS, and resource over-provisioning causes resource
under-utilization. We propose two closed loops for managing RAN slice resources
for cars to address these challenges. First, we present an auction mechanism
for allocating Resource Block (RB) to the tenants who provide services to the
cars using slices. Second, we design one closed loop that maps slices and
services of tenants to virtual Open Distributed Units (vO-DUs) and assigns RB
to vO-DUs for management purposes. Third, we design another closed loop for
intra-slices RB scheduling to serve cars. Fourth, we present a reward function
that interconnects these two closed loops to satisfy the time-varying demands
of cars at each slice while meeting QoS requirements in terms of delay.
Finally, we design distributed deep reinforcement learning approach to maximize
the formulated reward function. The simulation results show that our approach
satisfies more than 90% vO-DUs resource constraints and network slice
requirements.
