北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2024, Vol. 47 ›› Issue (4): 124-129.

• 论文 • 上一篇    下一篇

面向车联网的动态网络切片资源部署算法

李晓辉1,2, 周媛媛2, 吕思婷1, 苏家楠2   

  1. 1. 西安电子科技大学 通信工程学院 2. 西安电子科技大学 广州研究院
  • 收稿日期:2023-04-12 修回日期:2023-10-09 出版日期:2024-08-28 发布日期:2024-08-26
  • 通讯作者: 周媛媛 E-mail:21181214004@stu.xidian.edu.cn

Dynamic Network Slicing Resource Deployment Algorithm in Vehicular Networks

LI Xiaohui1,2, ZHOU Yuanyuan2, LYU Siting1, SU Jianan2   


  • Received:2023-04-12 Revised:2023-10-09 Online:2024-08-28 Published:2024-08-26
  • Contact: Yuan-Yuan ZHOU E-mail:21181214004@stu.xidian.edu.cn

摘要: 考虑到车联网中车辆快速移动造成拓扑复杂的问题,提出了一种基于深度强化学习的动态网络切片资源部署算法。在车辆到基础设施通信场景下,针对不断变化的车辆拓扑和业务请求,将切片资源部署问题建模为可观测的马尔可夫决策模型,利用联合控制器实时监测网络状态,根据执行切片资源分配比例的动作奖励值来实时更新参数,引入优先经验回放策略来加快收敛速度,为每个业务请求提供充足的通信资源来交互车辆速度和位置信息。仿真实验结果表明,对比其他算法,所提算法在端到端吞吐量、端到端时延、切片丢包率和车辆业务请求接受率方面都展现了更好的性能。

关键词: 网络切片 , 车辆到基础设施通信 , 深度强化学习 , 马尔可夫决策过程

Abstract: Considering the problem of complex topology in the rapid movement of vehicles in the network of vehicles, a dynamic network slicing resource deployment algorithm based on deep reinforcement learning is proposed. In the communication scenario of vehicle to infrastructure, for the changing vehicle topology and business requests, the slicing resource deployment problem is modeling as an observed Markov decision model, and the joint controller is used to monitor the network status in real time. The parameters are updated in real time according to the value of the actions in the distribution ratio of slicing resources, and a prioritized experience replay strategy is introduced to accelerate convergence speed, providing sufficient communication resources for each service request to interact with vehicle speed and location information. Simulation experiment results indicate that, compared to other algorithms, the proposed algorithm demonstrates better performance in end-to-end throughput, end-to-end latency, slice packet loss rate, and vehicle service request acceptance rate.

Key words: network slicing ,  vehicle to infrastructure communication ,  deep reinforcement learning ,  Markov decision process

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