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Applying DQN solutions in fog-based vehicular networks: Scheduling, caching, and collision control
Vehicular Communications ( IF 6.7 ) Pub Date : 2021-08-24 , DOI: 10.1016/j.vehcom.2021.100397
Seongjin Park 1 , Younghwan Yoo 2 , Chang-Woo Pyo 3
Affiliation  

In the near future, vehicular networks are expected to provide and consume a variety of services for autonomous driving, connected car, and Internet of Things (IoT). For practical service scenarios, it is necessary to consider the characteristics of the dynamic environment and Quality of Services (QoS) in a vehicular network. The goal of this paper is to maximize service delivery ratio while meeting QoS factors. We present three issues to be addressed by a road side unit (RSU) acting as a fog server. The first issue is the scheduling of services with different effective time. The second is the RSU cache replacement strategy considering limited storage space. The third is the QoS-based message collision control for channels that multiple vehicles share. This paper solves these three issues by leveraging Deep Q Network (DQN), one of deep reinforcement learning techniques. To this end, the three problems are defined as Markov Decision Process (MDP) problems and the effectiveness of the proposed method is demonstrated through experiments. Experimental results substantiate that the proposed method based on DQN can find a policy that is adaptive to situations through learning for each defined problem.



中文翻译:

在基于雾的车辆网络中应用 DQN 解决方案:调度、缓存和碰撞控制

在不久的将来,车载网络有望为自动驾驶、联网汽车和物联网 (IoT) 提供和消费各种服务。对于实际的服务场景,需要考虑车联网中动态环境和服务质量(QoS)的特点。本文的目标是在满足 QoS 因素的同时最大化服务交付率。我们提出了作为雾服务器的路侧单元 (RSU) 需要解决的三个问题。第一个问题是不同有效时间的服务的调度。二是考虑有限存储空间的RSU缓存替换策略。三是对多辆车共享的通道进行基于QoS的消息冲突控制。本文利用Deep Q Network (DQN)解决了这三个问题,深度强化学习技术之一。为此,将这三个问题定义为马尔可夫决策过程(MDP)问题,并通过实验证明了所提出方法的有效性。实验结果证实,所提出的基于 DQN 的方法可以通过学习每个定义的问题来找到适应情况的策略。

更新日期:2021-08-24
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