Joint Computing and Caching in 5G-Envisioned Internet of Vehicles: A Deep Reinforcement Learning-Based Traffic Control System

Recent developments of edge computing and content caching in wireless networks enable the Intelligent Transportation System (ITS) to provide high-quality services for vehicles. However, a variety of vehicular applications and time-varying network status make it challenging for ITS to allocate resources efficiently. Artificial intelligence algorithms, owning the cognitive capability for diverse and time-varying features of Internet of Connected Vehicles (IoCVs), enable an intent-based networking for ITS to tackle the above-mentioned challenges. In this paper, we develop an intent-based traffic control system by investigating Deep Reinforcement Learning (DRL) for 5G-envisioned IoCVs, which can dynamically orchestrate edge computing and content caching to improve the profits of Mobile Network Operator (MNO). By jointly analyzing MNO's revenue and users' quality of experience, we define a profit function to calculate the MNO's profits. After that, we formulate a joint optimization problem to maximize MNO's profits, and develop an intelligent traffic control scheme by investigating DRL, which can improve system profits of the MNO and allocate network resources effectively. Experimental results based on real traffic data demonstrate our designed system is efficient and well-performed.

I. Introduction

With the emergence of the Fifth Generation (5G) wireless networks, various devices in communication networks are able to connect and exchange information more efficiently than ever before [1]. As an important application of Internet of Things (IoTs), the Internet of Connected Vehicles (IoCVs) gains tremendous capability to construct an efficient transportation system to serve vehicles. Intent-based networking along with the IoCV enables the development of Intelligent Transportation System (ITS), which plays an important role to improve the quality of lives for inhabitants in smart cities. With the increasing scale of IoCVs and diverse requirements of vehicular applications, Mobile Network Operators (MNOs) are urgent to design systems with high Quality-of-Service (QoS) and Quality of Experience (QoE), which can provide satisfactory services for their users.