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:

城市轨道交通对于缓解城市交通拥堵具有重要作用，城轨列车多车协同控制是近年来的研究热点。多车协同计算任务受通信的限制，存在资源分配平衡差、系统对环境变化响应慢、协同运行能力有限等问题。5G通信与移动边缘计算(MEC)的结合可有效改进任务处理的实时性与准确性，提高系统整体性能。本文设计了一种基于5G与MEC的城轨列车运行控制系统自主协同计算架构，根据多车协同控制任务的特征，将多车协同计算卸载中的边缘服务器选择问题建模为多臂匪徒(MAB)学习模型，并提出一种基于置信区间上限(UCB)算法的求解方案，使城轨列车多车协同控制系统的整体能耗和时延最小。仿真结果表明，本文所提出的算法模型在平均奖励、最佳选择概率、平均执行时延、加权总成本等方面具有显著的性能优势。

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.