  This paper studies the problem of non-cooperative radio resource scheduling
in a vehicle-to-vehicle communication network. The technical challenges lie in
high vehicle mobility and data traffic variations. Over the discrete scheduling
slots, each vehicle user equipment (VUE)-pair competes with other VUE-pairs in
the coverage of a road side unit (RSU) for the limited frequency to transmit
queued packets. The frequency allocation at the beginning of each slot by the
RSU is regulated following a sealed second-price auction. Each VUE-pair aims to
optimize the expected long-term performance. Such interactions among VUE-pairs
are modelled as a stochastic game with a semi-continuous global network state
space. By defining a partitioned control policy, we transform the stochastic
game into an equivalent game with a global queue state space of finite size. We
adopt an oblivious equilibrium (OE) to approximate the Markov perfect
equilibrium (MPE), which characterizes the optimal solution to the equivalent
game. The OE solution is theoretically proven to be with an asymptotic Markov
equilibrium property. Due to the lack of a priori knowledge of network
dynamics, we derive an online algorithm to learn the OE policies. Numerical
simulations validate the theoretical analysis and show the effectiveness of the
proposed online learning algorithm.
