Economical Profit Maximization in MEC Enabled Vehicular Networks

Abstract

Mobile edge computing enabled vehicular networking has appeared as a promising solution to the emerging resource hungry vehicular applications. In this paper, we study the computation offloading in a cognitive vehicular network that reuses the TV white space (TVWS) bands. We propose to maximize the average economical profit of the service provider by jointly considering communication and computation resource allocation, while guaranteeing network stability and the QoS of TVWS primary users. Based on Lyapunov optimization, we design an per-frame algorithm to tackle the joint optimization problem, where we first derive the closed-form solution for computation resource allocation, and then develop a continuous relaxation and Lagrangian dual decomposition based iterative algorithm for radio resource allocation. Simulation results demonstrate that the proposed algorithm can flexibly balance the profit-delay tradeoff, and can improve the economical profit of the service provider significantly as compared with the existing schemes.