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.

本文研究了一种用于车联网 (IoV) 的多用户移动边缘计算 (MEC) 系统，其中车辆附近的一个边缘点 (EP) 可以帮助处理计算密集型任务。对于 MEC 网络，现有的大部分工作集中在完美信道估计下任务卸载的系统成本最小化，但是由于车辆的高移动性而没有考虑不完美信道估计（CSI）的实际限制。因此，我们研究的目标是在不完善的 CSI 情况下减少计算和通信的延迟和能耗 (EC)，这是 MEC 网络的两个重要性能指标。为此，我们首先将系统成本表示为延迟和 EC 的线性组合形式，然后制定系统成本的优化问题。此外，提出了一种新颖的深度方法，将深度强化学习 (DRL) 与拉格朗日乘数相结合，共同最小化系统成本。特别是，DRL算法用于获得有能力的卸载策略，而拉格朗日乘数用于获得带宽分配。最后给出了模拟结果，以表明所设计的方法优于传统方法。