异构车联网中基于Markov决策的最优网络选择

董彪; 唐亮; 杨洪生; 卜智勇

Chinese

您当前的位置：

首页 >

文章列表页 >

异构车联网中基于Markov决策的最优网络选择

更新时间：2026-09-04

- 异构车联网中基于Markov决策的最优网络选择
- An MDP-Based Optimal Network Selection Algorithm for Heterogeneous IoV
- 微电子学与计算机 2017年34卷第11期页码：68-74
- 作者机构：
  
  1. 中国科学院机构上海微系统与信息技术研究所宽带无线移动通信实验室,上海,200050
  2. 中国科学院大学机构,北京,101408
- 作者简介：
- 基金信息：
- DOI：
  中图分类号：
- 纸质出版：2017
- 稿件说明：
移动端阅览
董彪, 唐亮, 杨洪生, 卜智勇. 异构车联网中基于Markov决策的最优网络选择[J]. 微电子学与计算机, 2017, 34(11): 68-74.

DONG Biao, TANG Liang, YANG Hong-sheng, et al. An MDP-Based Optimal Network Selection Algorithm for Heterogeneous IoV[J]. 2017, 34(11): 68-74.
董彪, 唐亮, 杨洪生, 卜智勇. 异构车联网中基于Markov决策的最优网络选择[J]. 微电子学与计算机, 2017, 34(11): 68-74. DOI：

DONG Biao, TANG Liang, YANG Hong-sheng, et al. An MDP-Based Optimal Network Selection Algorithm for Heterogeneous IoV[J]. 2017, 34(11): 68-74. DOI：

摘要

在基于V2V和V2X的异构车联网系统中

车辆需要在各网络之间选择最优网络

针对网络选择技术普遍不能满足网络和车辆状态的随机无记忆性以及网络选择后状态的转移性

提出了基于VMDP的最优决策算法.该算法建立了VMDP状态空间以及状态转移概率矩阵

并且利用VIA求得最优网络选择策略.与其他算法进行性能对比

仿真结果表明该算法不仅能有效地实现车辆的最优网络选择

而且在通信链路质量和切换次数等性能上优于对比算法10%~30%

有效地提高了车辆通信性能.

Abstract

In the V2V and V2X-based heterogeneous vehicular networking system

the vehicles need to select the optimal network

between each network. For network selection techniques generally cant meet the random and memoryless states of the networks and vehicles

and cant predict the states transition

network optimal decision algorithm based on VMDP is proposed. The algorithm established the VMDP state space and state transition probability matrix

and used VIA to get the optimal network selection strategy. Through the performance comparison with other algorithms

the simulation results show that the algorithm can not only effectively achieve the vehicle optimal network selection

but also achieve an improvement of 10%~30% on the communication link quality and vehicular switching times. The proposed algorithm can effectively improve the performance of vehicle communication.

关键词

Keywords

references

Supraja S, Kousalya P. A comparative study by AHP and TOPSIS for the selection of all round excellence award[C]// ICEEOT 2016 - 2016 IEEE International Conference on Electrical, Electronics, and Optimization Techniques. IEEE, 2016: 314-319.

Sun C, Stevens-Navarro E, Wong V W S. A constrained MDP-Based vertical handoff decision algorithm for 4G wireless networks[C]// IEEE International Conference on Communications. IEEE, 2008: 2169-2174.

Yang K, Gondal I, Qiu B, et al. Combined SINR based vertical handoff algorithm for next generation heterogeneous wireless networks[C]// Global Telecommunications Conference, 2007. GLOBECOM ′07. IEEE, 2007: 4483-4487.

Zhu J, Xu L, Yang L, et al. An optimal vertical handoff decision algorithm for multiple services with different priorities in heterogeneous wireless networks[J]. Wireless Personal Communications, 2015, 83(1): 1-23. DOI: 1

Chandavarkar B R, Reddy G R M. Analysis of sigmoidal utility function based handover in heterogeneous wireless networks[C]// International Conference on Microwave, Optical and Communication Engineering. IEEE, 2015: 118-121.

Wang L, Binet D. MADM-based network selection in heterogeneous wireless networks: A simulation study[C]// International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace % Electronic Systems Technology, 2009. Wireless Vitae. IEEE, 2009: 559-564.

Stevens-Navarro E, Lin Y X, Wong W S. An MDP-Based vertical handoff decision algorithm for heterogeneous wireless networks[J]. IEEE Transactions on Vehicular Technology, 2008, 57(4): 1243-1254. DOI: 1243

Shen W, Zeng Q A. Cost-function-based network selection strategy in integrated wireless and mobile networks[J]. IEEE Transactions on Vehicular Technology, 2008, 57(6): 3778-3788. DOI: 3778

Karedal J, Czink N, Paier A, et al. Path loss modeling for vehicle-to-vehicle communications[J]. IEEE Transactions on Vehicular Technology, 2011, 60(1): 323-328. DOI: 323

Puternam M L. Markov Decision Processes-Discrete Stochastic Dynamic Programming[J]. Wiley series in probability and mathematical statistics, 1994, 37(3): 353-353. DOI: 353

Kaiwartya O, Abdullah A H, Yue Cao, et al. Internet of vehicles: motivation, layered architecture network codel challenges and future aspects[J]. IEEE Access, 2016(4): 5356-5373. DOI: 5356

Chandrasekharamenon N P, Ancharev B. Connectivity analysis of one-dimensional vehicular ad hoc networks in fading channels[J]. EURASIP Journal on Wireless Communications and Networking, 2012(1): 1. DOI: 1

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于MSS-YOLO-MPHands融合架构的装配手部动作识别研究

V型沟槽SiC中子探测器的蒙特卡洛分析

基于监控指标关联性的微服务异常检测

基于改进RT-DETR的航拍小目标检测算法

基于多尺度融合与小目标增强的航拍车辆检测算法