Mode Selection and Resource Allocation for MEC-Assisted V2X Networks Under Limited Energy and Bandwidth Constraints

Mobile edge computing (MEC)-assisted vehicle-to-everything (V2X) communication has been proposed as it can reduce the computation overhead of vehicles by offloading partial tasks. However, the performance improvement of such systems is still challenging due to the limited spectrum resources and computation capabilities. To this end, we study a mode selection and resource allocation (RA) problem in MEC-assisted V2X networks with limited energy and bandwidth constraints. Our goal is to minimize the delay of vehicle-to-infrastructure (V2I) links under the constraints of the maximum transmission bandwidth, the minimum data rate, the maximum transmit power, and the mode selection factors. To solve the mixed-integer nonlinear programming problem, an alternative optimization method is employed to decompose it into two subproblems: a radio RA subproblem and a task offloading subproblem. Then, the former subproblem is converted into a convex problem via the variable substitution approach, while the latter one is converted into a convex problem via variable relaxation and successive convex approximation. Finally, an iteration-based RA algorithm is proposed. Simulation results show that the proposed algorithm reduces latency by 77.9% compared to the RA algorithm without MEC and by 68.9% compared to the RA algorithm without mode selection.