Task offloading for vehicular edge computing with imperfect CSI: A deep reinforcement approach

Abstract

This article examines a multi-user mobile edge computing (MEC) system for the Internet of Vehicle (IoV), where one edge point (EP) nearby the vehicles can help assist in processing the compute-intensive tasks. For the MEC networks, the majority of existing works concentrate on the minimization of system cost of task offloading under the perfect channel estimation, which however fails to consider the practical limitation of imperfect channel estimation (CSI) because of vehicles’ high-mobility. Therefore, the goal of our study is to reduce the delay as well as energy consumption (EC) of computation and communication with imperfect CSI, which are the two significant performance metrics of MEC network. With this aim, we first express the system cost as a form of the linear combination of the delay and EC, and then formulate the optimization problem for the system cost. Moreover, a novel deep approach is proposed, which is integrated by deep reinforcement learning (DRL) with the Lagrange multiplier to jointly minimize the system cost. In particular, the DRL algorithm is employed to obtain the capable offloading strategy, while the Lagrange multiplier is used to obtain the bandwidth allocation. The simulated results are finally presented to show that the devised approach outperforms the traditional ones.