Abstract

This paper addresses the problem of wireless resource scheduling in a vehicle-to-vehicle (V2V) communication network. The technical challenges lie in the fast changing network dynamics, namely, the channel quality and the data traffic variations. For a road segment covered by a road side unit (RSU), especially in a dense urban area, the vehicle density tends to be stable. The incoming service requests from the vehicle user equipment (VUE)-pairs compete with each other for the limited frequency resource in order to deliver data packets. Such competitions are regulated by the RSU via a sealed second-price auction at the beginning of scheduling slots. Each incumbent service request aims at maximizing the expected long-term payoff from bidding the frequency resource for packet transmissions. Markov perfect equilibrium (MPE) can be utilized to characterize the optimal competitive behaviors of the service requests. When the number of incumbent VUE-pairs becomes large, solving the MPE becomes infeasible. We adopt an oblivious equilibrium to approximate the MPE, which is theoretically proven to be error-bounded. The decision making process at each service request is hence transformed into a single-agent Markov decision process, for which we propose an on-line auction based learning scheme. Through simulation experiments, we show the potential performance gains from our proposed scheme, in terms of per-service request average utility.