When Deep Reinforcement Learning Meets 5G-Enabled Vehicular Networks: A Distributed Offloading Framework for Traffic Big Data

The emerging 5G-enabled vehicular networks can satisfy various requirements of vehicles by traffic offloading. However, limited cellular spectrum and energy supplies restrict the development of 5G-enabled applications in vehicular networks. In this article, we construct an intelligent offloading framework for 5G-enabled vehicular networks, by jointly utilizing licensed cellular spectrum and unlicensed channels. A cost minimization problem is formulated by considering the latency constraint of users and is further decomposed into two subproblems due to its complexity. For the first subproblem, a two-sided matching algorithm is proposed to schedule the unlicensed spectrum. Then, a deep-reinforcement-learning-based method is investigated for the second one, where the system state is simplified to realize distributed traffic offloading. Real-world traces of taxies are leveraged to illustrate the effectiveness of our solution.

I. Introduction

With the recent advancements in 5G-enabled wireless communication technologies, electronic devices are expected to be connected to provide smart services for humans and facilitate our daily lives. The Internet of vehicles (IoV) is an emerging paradigm, which enables vehicles to perform various applications and provide comfortable traveling experiences for drivers and passengers [1], [2]. However, these applications and services generally have strict processing deadline constraints and limited energy supplies. Meanwhile, the explosive growth of vehicular data and complicated interactions between vehicles and servers pose great challenges for mobile network operators to guarantee the quality of service (QoS) of users and decrease the energy cost of network communications [3].