论文标题

信息年龄的知识内容缓存和连接车辆的分配

Age-of-Information Aware Contents Caching and Distribution for Connected Vehicles

论文作者

Park, Soohyun, Park, Chanyoung, Jung, Soyi, Choi, Minseok, Kim, Joongheon

论文摘要

为了支持快速,准确的自动驾驶服务,通过与连接的车辆网络中的周围基础设施进行通信,可以收集和利用道路环境信息,这很难通过车辆传感器本身获得。因此,我们考虑一种利用基础设施,例如路边单元(RSU)和宏基站(MBS)(MBS)等基础设施的情况。由于道路环境的迅速变化,这一概念代表了道路内容的新鲜感,即信息时代(AOI)很重要。根据AOI值,在连接的车辆系统中,必须提前将适当的内容保留在RSU中,在内容过期之前对其进行更新,然后将内容发送到想要使用它的车辆。但是,对于最小AOI而言,内容传输过于频繁会导致对网络资源的不加区分使用。此外,传输控制,内容AOI和服务延迟没有不利地考虑对用户服务的影响。因此,重要的是要找到适当的妥协。由于这些原因,本文的目的将降低通过拟议系统传递内容的系统成本,同时最大程度地减少MBS,RSUS和UVS中呈现的内容AOI。能够分别使用马尔可夫决策过程(MDP)和Lyapunov优化框架来进行传输过程,即内容缓存和服务,即内容缓存和服务,这些框架通过数据密集型绩效评估进行了验证。

To support rapid and accurate autonomous driving services, road environment information, which is difficult to obtain through vehicle sensors themselves, is collected and utilized through communication with surrounding infrastructure in connected vehicle networks. For this reason, we consider a scenario that utilizes infrastructure such as road side units (RSUs) and macro base station (MBS) in situations where caching of road environment information is required. Due to the rapidly changed road environment, a concept which represents a freshness of the road content, age of information (AoI), is important. Based on the AoI value, in the connected vehicle system, it is essential to keep appropriate content in the RSUs in advance, update it before the content is expired, and send the content to the vehicles which want to use it. However, too frequent content transmission for the minimum AoI leads to indiscriminate use of network resources. Furthermore, a transmission control, that content AoI and service delay are not properly considered adversely, affects user service. Therefore, it is important to find an appropriate compromise. For these reasons, the objective of this paper is about to reduce the system cost used for content delivery through the proposed system while minimizing the content AoI presented in MBS, RSUs and UVs. The transmission process, which is able to be divided into two states, i.e., content caching and service, is approached using Markov decision process (MDP) and Lyapunov optimization framework, respectively, which guarantee optimal solutions, as verified via data-intensive performance evaluation.

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