Reinforcement Learning for Joint Optimization of Communication and Computation in Vehicular Networks

Abstract

Ultra reliability and low latency communications (URLLC) are considered as one of the most important use cases for computation tasks in the Internet of Vehicles (IoV) edge computing networks. To meet the task requirements in IoV networks, communication and computing resources are allocated in an effective and efficient way. Thus, this paper proposes a multi-objective reinforcement learning strategy, called intelligent communication and computation resource allocation (ICCRA), which combines communication and computing resource allocation to reduce the total system cost consisting of latency and reliability. Specifically, this strategy can be decomposed into three algorithms. The algorithm called joint computation offloading and collaboration is a general framework of the strategy, it first uses the K-nearest neighbor method to select offloading layers for computation tasks, such as cloud computing layer, mobile edge computing layer, and local computing layer. Then, when selecting the local computing layer to perform the task, the algorithm called collaborative vehicle selection is used to find the target vehicle to execute collaborative computing. The allocation of communication and computing resources is defined as two independent objectives, the algorithm called multi-objective resource allocation uses the reinforcement learning to achieve the optimal solution, at the mobile edge computing layer. Simulation results show that compared with local computing, edge computing, and random computing strategies, the proposed strategy reduces the total system cost effectively. For instance, compared with edge computing strategy, the total system cost of the proposed strategy can be reduced about 50% on average with the different number of vehicle tasks.