移动边缘计算环境下的通信资源调度算法  

Communication Resource Scheduling Algorithm in Mobile Edge Computing Environment

作　　者：李煜 王翔 LI Yu;WANG Xiang(Inner Mongolia Power Group Mengdian Information and Communication Industry Co.,Ltd.,Hohhot 010000,China;Inner Mongolia Power(Group)Co.,Ltd.Communication Branch,Hohhot 010000,China)

机构地区：[1]内蒙古电力集团蒙电信息通信产业有限责任公司,内蒙古呼和浩特010000 [2]内蒙古电力(集团)有限责任公司通信分公司,内蒙古呼和浩特010000

出　　处：《数字通信世界》2025年第2期55-57,共3页Digital Communication World

摘　　要：移动边缘计算(MEC)是一种新兴的计算模式,旨在将计算和存储资源部署在靠近用户设备的网络边缘,以减少延迟和提高服务质量,然而,MEC环境下的通信资源调度面临着诸多挑战。本文针对MEC环境下的通信资源调度问题,提出了一种基于深度强化学习的调度算法,该算法通过对网络状态进行实时感知和学习,自适应地调整资源分配策略,以最大化系统的长期收益。仿真结果表明,与传统的调度算法相比,所提算法能够显著提高系统的吞吐量和用户体验质量,同时降低时延和能耗。本文的研究成果对于优化MEC环境下的通信资源管理具有一定的参考意义。Mobile edge computing(MEC)is a new computing model,which aims to deploy computing and storage resources near the network edge of user equipment to reduce latency and improve quality of service.However,communication resource scheduling in MEC environment faces many challenges.The article proposes a scheduling algorithm based on deep reinforcement learning for communication resource scheduling in MEC environment.This algorithm adaptively adjusts resource allocation strategies by perceiving and learning the network state in real-time,in order to maximize the long-term benefits of the system.The simulation results show that compared with traditional scheduling algorithms,the proposed algorithm can significantly improve the system's throughput and user experience quality,while reducing latency and energy consumption.The research results of the article have important guiding significance for optimizing communication resource management in MEC environment.

关 键 词：移动边缘计算 资源调度 深度强化学习 吞吐量 时延 

分 类 号：TN929.5[电子电信—通信与信息系统]