内容新鲜度保障的车联网多智能体缓存分发策略

崔亚平; 石宏吉; 吴大鹏; 何鹏; 王汝言

doi:10.11959/j.issn.1000-436x.2025013

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内容新鲜度保障的车联网多智能体缓存分发策略

学术论文 | 更新时间：2025-02-13

- 内容新鲜度保障的车联网多智能体缓存分发策略
- Multi-agent caching distribution strategy for content freshness guarantee in IoV
- 通信学报 2025年46卷第1期页码：52-66
- 作者机构：
  
  1.重庆邮电大学通信与信息工程学院，重庆 400065
  2.先进网络与智能互联技术重庆市高校重点实验室，重庆 400065
  3.泛在感知与互联重庆市重点实验室，重庆 400065
- 作者简介：
  
  [ "崔亚平（1986- ），男，河南新乡人，博士，重庆邮电大学副教授，主要研究方向为智能反射面、移动边缘计算和车联网的网络功能虚拟化等。" ]
  [ "石宏吉（1998- ），男，四川邻水人，重庆邮电大学硕士生，主要研究方向为车载网络和边缘缓存等。" ]
  [ "吴大鹏（1979- ），男，黑龙江大庆人，博士，重庆邮电大学教授、博士生导师，主要研究方向为普适网络、IP QoS架构、网络可靠性以及通信系统的性能评估等。" ]
  [ "何鹏（1990- ），男，重庆人，博士，重庆邮电大学副教授，主要研究方向为移动边缘计算、分子通信。" ]
  [ "王汝言（1969- ），男，湖北浠水人，博士，重庆邮电大学教授、博士生导师，主要研究方向为泛在网络、多媒体信息处理等。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61801065;62271096;61871062;U20A20157;62061007);重庆市教委科学技术研究基金资助项目(KJQN202000603;KJQN202300621);重庆市自然科学基金资助项目(CSTB2022NSCQ-MSX0468;cstc2020jcyjzdxmX0024;cstc2021jcyjmsxmX0892;CSTB2023NSCQ-LZX0134);重庆市高校创新研究群体基金资助项目;The University Innovation Research Group of Chongqing(CXQT20017);重邮信通青创团队支持计划基金资助项目;The Youth Innovation Group Support Program of ICE Discipline of CQUPT(SCIE-QN-2022-04);四川省重点研发计划基金资助项目(2024YFHZ0093)
- DOI：10.11959/j.issn.1000-436x.2025013
  中图分类号： TN927
- 收稿：2024-09-25，
  
  修回：2024-12-10，
  
  纸质出版：2025-01-25
- 稿件说明：
移动端阅览
崔亚平,石宏吉,吴大鹏等.内容新鲜度保障的车联网多智能体缓存分发策略[J].通信学报,2025,46(01):52-66.

CUI Yaping,SHI Hongji,WU Dapeng,et al.Multi-agent caching distribution strategy for content freshness guarantee in IoV[J].Journal on Communications,2025,46(01):52-66.
崔亚平,石宏吉,吴大鹏等.内容新鲜度保障的车联网多智能体缓存分发策略[J].通信学报,2025,46(01):52-66. DOI： 10.11959/j.issn.1000-436x.2025013.

CUI Yaping,SHI Hongji,WU Dapeng,et al.Multi-agent caching distribution strategy for content freshness guarantee in IoV[J].Journal on Communications,2025,46(01):52-66. DOI： 10.11959/j.issn.1000-436x.2025013.

摘要

车辆需要频繁动态变化内容支持车联网（IoV）时延敏感型应用，这会增加宏基站（MBS）负载，降低内容新鲜度。利用边缘缓存将最新内容提前缓存在小基站（SBS）能有效降低车辆时延和提高内容新鲜度。对影响时延和内容信息年龄（AoI）进行深入分析，提出一种内容新鲜度保障的多智能体强化学习（MARL）算法，通过优化缓存分发决策保障车辆获得高新鲜度内容。仿真结果表明，所提算法不仅收敛速度更快，而且在降低车辆时延和提升内容新鲜度方面表现出更好效果。

Abstract

Vehicles need to dynamically changing content to support latency-sensitive applications in Internet of vehicles (IoV)

thereby increasing the load on the macro base station (MBS) and reducing the freshness of content. Utilizing edge caching to cache the latest content in small base station (SBS) can effectively reduce the latency and improve the content freshness. An in-depth analysis was conducted on latency and content's age of information (AoI). A content freshness assurance multi-agent reinforcement learning (MARL) algorithm was proposed

which optimized cache distribution decisions to guarantee high freshness. Simulation results show that the proposed algorithm not only converges faster but also demonstrates better performance in reducing latency and enhancing content freshness.

关键词

Keywords

references

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