  In this paper, we investigate a radio access network (RAN) slicing problem
for Internet of vehicles (IoV) services with different quality of service (QoS)
requirements, in which multiple logically-isolated slices are constructed on a
common roadside network infrastructure. A dynamic RAN slicing framework is
presented to dynamically allocate radio spectrum and computing resource, and
distribute computation workloads for the slices. To obtain an optimal RAN
slicing policy for accommodating the spatial-temporal dynamics of vehicle
traffic density, we first formulate a constrained RAN slicing problem with the
objective to minimize long-term system cost. This problem cannot be directly
solved by traditional reinforcement learning (RL) algorithms due to complicated
coupled constraints among decisions. Therefore, we decouple the problem into a
resource allocation subproblem and a workload distribution subproblem, and
propose a two-layer constrained RL algorithm, named Resource Allocation and
Workload diStribution (RAWS) to solve them. Specifically, an outer layer first
makes the resource allocation decision via an RL algorithm, and then an inner
layer makes the workload distribution decision via an optimization subroutine.
Extensive trace-driven simulations show that the RAWS effectively reduces the
system cost while satisfying QoS requirements with a high probability, as
compared with benchmarks.
