  While network slicing has become a prevalent approach to service
differentiation, radio access network (RAN) slicing remains challenging due to
the need of substantial adaptivity and flexibility to cope with the highly
dynamic network environment in RANs. In this paper, we develop a slicing-based
resource management framework for a two-tier RAN to support multiple services
with different quality of service (QoS) requirements. The developed framework
focuses on base station (BS) service coverage (SC) and interference management
for multiple slices, each of which corresponds to a service. New designs are
introduced in the spatial, temporal, and slice dimensions to cope with
spatiotemporal variations in data traffic, balance adaptivity and overhead of
resource management, and enhance flexibility in service differentiation. Based
on the proposed framework, an energy efficiency maximization problem is
formulated, and an artificial intelligence (AI)-assisted approach is proposed
to solve the problem. Specifically, a deep unsupervised learning-assisted
algorithm is proposed for searching the optimal SC of the BSs, and an
optimization-based analytical solution is found for managing interference among
BSs. Simulation results under different data traffic distributions demonstrate
that our proposed slicing-based resource management framework, empowered by the
AI-assisted approach, outperforms the benchmark frameworks and achieves a
close-to-optimal performance in energy efficiency.
