Computation Offloading Based on Game Theory in MEC-Assisted V2X Networks
Haipeng Wang, Zhipeng Lin, Kun Guo, Tiejun Lv · 2021
Mobile edge computing (MEC) is a promising technique to meet the demand of network resources in vehicle-to-everything (V2X) networks. By offloading computation-intensive and delay-sensitive tasks to nearby devices with idle network resources, the technique can make up for the shortage of computing resources in terminal devices. In this paper, we design a MEC-assisted V2X network where vehicles can offload tasks to a nearby vehicle via a vehicle-to-vehicle (V2V) link or a nearby roadside unit (RSU) via a vehicle-to-infrastructure (V2I) link, and vehicles can directly offload tasks to the network via vehicle-to-network (V2N) links. We consider the mutual interference among vehicles when they offload tasks to other vehicles in the same link. Since offloading decision-making can impact the delay and energy consumption of the network, we construct an offloading decision-making problem, and describe the problem as a game. We prove that the game can achieve the Nash equilibrium (NE), and can always converge after the finite improvement property (FIP). A computing offloading (CO) algorithm, which can reduce the delay and the energy consumption, is proposed to achieve the NE. Based on the proposed CO algorithm, this paper also presents an offloading-allocation (OA) algorithm. Extensive simulation results show that the proposed OA algorithm reduces the number of iterations and increases the convergence rate.