No abstract

In this paper, we investigate a multiuser mobile edge computing (MEC)-aided smart internet of vehicle (IoV) network, where one edge server can help accomplish the intensive computational tasks from the vehicular users. For the MEC networks, most of existing works mainly focus on minimizing the system latency to guarantee the user's quality of service (QoS) through designing some offloading strategies, which however fail to consider the pricing from the server and hence fail to take into account the the budget constraint from the users. To address this issue, we jointly incorporate the budget constraint into the system design of the MEC based IoV networks, and then propose a joint deep reinforcement learning (DRL) approach combined with the convex optimization algorithm. Specifically, a deep Q-network (DQN) is firstly used to make the offloading decision, and then the Lagrange multiplier method is employed to allocate the computational capability of the server to multiple users. Simulations are finally presented to demonstrate that the proposed schemes outperforms the conventional ones. In particular, the proposed scheme can effective reduce the system latency up to 56% compared to the conventional schemes.

No abstract