太赫兹科学与电子信息学报2024，Vol.22Issue(11)：1199-1208,10.DOI:10.11805/TKYDA2023049
基于5G的列车云边端协同计算设计与优化
5G-based design and optimization of cloud-edge-train collaborative computing
摘要
Abstract
Urban rail transit plays a significant role in alleviating urban traffic congestion,and the coordinated control of multiple urban rail vehicles has been a research hotspot in recent years.The multi-vehicle coordinated computing task is limited by communication,leading to issues such as poor resource allocation balance,slow system response to environmental changes,and limited cooperative operation capabilities.The integration of 5G communication and Mobile Edge Computing(MEC)can effectively improve the real-time and accuracy of task processing,enhancing the overall system performance.This paper designs an autonomous coordinated computing architecture for urban rail vehicle operation control systems based on 5G and MEC.According to the characteristics of multi-vehicle coordinated control tasks,the problem of edge server selection in multi-vehicle coordinated computing offloading is modeled as a Multi-Armed Bandit(MAB)learning model,and a solution based on the Upper Confidence Bound(UCB)algorithm is proposed to minimize the overall energy consumption and latency of the urban rail vehicle multi-vehicle coordinated control system.Simulation results show that the proposed algorithm model has significant performance advantages in terms of average reward,best selection probability,average execution latency,and weighted total cost.关键词
多车协作/移动边缘(MEC)计算/5G网络/任务卸载/多臂匪徒(MAB)学习/置信区间上限(UCB)算法Key words
multi-train collaboration/Mobile Edge Computing(MEC)/5G network/task offloading/Multi-Armed Bandits(MAB)learning/Upper Confidence Bound(UCB)algorithm分类
信息技术与安全科学引用本文复制引用
徐建喜,魏思雨,李宗平..基于5G的列车云边端协同计算设计与优化[J].太赫兹科学与电子信息学报,2024,22(11):1199-1208,10.基金项目
国家自然科学基金资助项目(61973026) （61973026）
