Abstract : Smart vehicles require constantly running heavy vehicular computations with their limited computation/energy resources. 5G vehicular networks have potential to resolve the issue, by letting the vehicular tasks offloaded to 5G mobile edge computing (MEC) servers. To better support vehicular computation offloading, this paper proposes a road-side 5G infrastructure consisting of multiple millimeter-wave (mmWave) small-cell base stations (BSs) and a cellular mid-band based macro-cell BS where each BS is equipped with an MEC server. Then, the vehicles with mmWave/mid-band dual interfaces can decide which BS to choose for offloading. We propose a decentralized offloading decision mechanism where each vehicle tries to minimize the time-energy joint cost with three choices: local computing, offloading to a small-cell MEC, offloading to a macro-cell MEC. In particular, we model the problem as an ordinal potential game, derive its potential function to ensure the existence of and finite-time convergence to a Nash equilibrium (NE), analyze its Price-of-Anarchy, and develop an iterative offloading decision update algorithm. In doing so, we also consider slicing the global game into multiple non-overlapping smaller games and running them in parallel, to investigate the best slicing strategy. Our extensive simulations show the game’s real-time convergence to an NE, reveal the NE’s near-optimal performance, and present the efficacy of the proposed game slicing.
Index terms : 5G, computation offloading, game slicing, millimeter wave, mobile edge computing, potential game, vehicular network