Cost-Effective Task Offloading in NOMA-Enabled Vehicular Mobile Edge Computing

Nonorthogonal multiple access (NOMA) and mobile edge computing (MEC) are two key emerging technologies for vehicular networks, where NOMA allows multiple vehicular user equipments (VUEs) to share the same wireless resources, and thus to enhance the spectrum utilization and system capacity, and MEC permits VUEs to offload their complex applications to MEC servers, and thus to provide support for computationally intensive intelligent applications. In this article, a NOMA-based vehicle edge computing (VEC) network model is proposed, and the cost minimization problem is constructed. Under the premise of ensuring the delay tolerance of all VUEs, the total system cost is minimized through the joint optimization of offloading decision-making, VUE clustering, subchannel and computation resource allocation, and transmission power control. Since the proposed problem is a mixed-integer nonlinear programming problem, which is difficult to solve, we decouple it into two subproblems and propose two heuristic algorithms to solve the task offloading and the MEC resource assignment problem, respectively, and finally, we obtain the closed-form solutions for cloud-related optimization problems through simple analysis. Simulation results show that the proposed joint algorithm is superior to other baseline algorithms in terms of system cost minimization.

Automated summary

This article proposes a NOMA-based vehicle edge computing network model to minimize system cost through joint optimization of offloading decision-making, VUE clustering, resource allocation, and power control. Two heuristic algorithms are used to solve the task offloading and resource assignment problems, and closed-form solutions for cloud-related optimization problems are obtained.