  With the cellular networks becoming increasingly agile, a major challenge
lies in how to support diverse services for mobile users (MUs) over a common
physical network infrastructure. Network slicing is a promising solution to
tailor the network to match such service requests. This paper considers a
system with radio access network (RAN)-only slicing, where the physical
infrastructure is split into slices providing computation and communication
functionalities. A limited number of channels are auctioned across scheduling
slots to MUs of multiple service providers (SPs) (i.e., the tenants). Each SP
behaves selfishly to maximize the expected long-term payoff from the
competition with other SPs for the orchestration of channels, which provides
its MUs with the opportunities to access the computation and communication
slices. This problem is modelled as a stochastic game, in which the decision
makings of a SP depend on the global network dynamics as well as the joint
control policy of all SPs. To approximate the Nash equilibrium solutions, we
first construct an abstract stochastic game with the local conjectures of
channel auction among the SPs. We then linearly decompose the per-SP Markov
decision process to simplify the decision makings at a SP and derive an online
scheme based on deep reinforcement learning to approach the optimal abstract
control policies. Numerical experiments show significant performance gains from
our scheme.
