  Network slicing is a proposing technology to support diverse services from
mobile users (MUs) over a common physical network infrastructure. In this
paper, we consider radio access network (RAN)-only slicing, where the physical
RAN is tailored to accommodate both computation and communication
functionalities. Multiple service providers (SPs, i.e., multiple tenants)
compete with each other to bid for a limited number of channels across the
scheduling slots, aiming to provide their subscribed MUs the opportunities to
access the RAN slices. An eavesdropper overhears data transmissions from the
MUs. We model the interactions among the non-cooperative SPs as a stochastic
game, in which the objective of a SP is to optimize its own expected long-term
payoff performance. To approximate the Nash equilibrium solutions, we first
construct an abstract stochastic game using the channel auction outcomes. Then
we linearly decompose the per-SP Markov decision process to simplify the
decision-makings and derive a deep reinforcement learning based scheme to
approach the optimal abstract control policies. TensorFlow-based experiments
verify that the proposed scheme outperforms the three baselines and yields the
best performance in average utility per MU per scheduling slot.
