Joint Resource Allocation and Computation Offloading With Time-Varying Fading Channel in Vehicular Edge Computing

Abstract

Vehicular edge computing (VEC) is considered as a novel paradigm to enhance the safety of automated vehicles and intelligent transportation systems (ITS). The computation offloading strategies are the key point of VEC, and the effect of time-varying channels cannot be ignored during the task transmission period. This paper investigates the utility maximization problem with task delay requirement constraints, in which the influence of time-varying channel on the task offloading strategies during the task offloading period is considered. The time-varying fading channel leads to the time-varying spectrum efficiency (SE), so the previous offloading strategies are questionable when the additional uncertain allocated bandwidth is taken into account. To deal with it, we first propose a linearization based Branch and Bound (LBB) algorithm to solve the fixed SE problem without considering the time-varying channel characteristics. Considering the complexity of the LBB algorithm, a closest rounding integer (CRI) algorithm is proposed to solve the fixed SE problem. Then, based on the resource allocation strategies of the fixed SE problem, we propose the LBB based computation offloading (LBBCO) algorithm and the CRI based computation offloading (CRICO) algorithm to solve the original problem for both the static tasks and dynamic tasks. The proposed LBBCO/CRICO algorithms are also applicable to multi-vehicle and multi-task scenarios. Furthermore, we analyze the effect of small-scale fading on the proposed offloading strategies. The simulation results show that the average utilities of LBBCO and CRICO algorithms have a small gap by 3.93% and 6.13% only to the upper bound, respectively. Meanwhile, the proposed LBBCO and CRICO algorithms can outperform the previous state-of-the-art solution by 4.52% and 2.38%, respectively.