基于Q-Learning的MEC多用户多信道的任务卸载研究  

Research on MEC Multi-User Multi-Channel Task Offloading Based on Q-Learning

作　　者：任晶秋[1] 王子贤 REN Jingqiu;WANG Zixian(School of Electrical and Information Engineering,Northeast Petroleum University,Daqing 163318,China)

机构地区：[1]东北石油大学电气信息工程学院,黑龙江大庆163318

出　　处：《吉林大学学报(信息科学版)》2025年第1期1-7,共7页Journal of Jilin University(Information Science Edition)

基　　金：国家自然科学基金资助项目(62271447)。

摘　　要：为降低MEC(Mobile Edge Computing)系统的总开销,将所有设备的延迟和能量消耗的加权总和设定为优化目标,解决了多用户多信道移动边缘计算系统中的任务卸载问题。该方案能使多个用户设备通过无线信道将计算负荷重的任务卸载到MEC服务器上。并考虑到多个智能设备间在剩余能量方面的差异,引入能量因子用于衡量智能设备在能耗和时延之间的偏重。同时利用基于Q-learning算法的强化学习方案共同优化卸载决策、计算资源的分配以及无线信道的选择。仿真结果表明,该算法能有效降低任务处理的时延和能耗,容纳更多用户。In order to reduce the total overhead of the MEC(Mobile Edge Computing) system,the weighted sum of latency and energy consumption of all devices are considered as the optimization objective,and the problem of task offloading is solved in a multi-user multi-channel mobile edge computing system.Specifically,multiple user devices are able to offload computationally-heavy tasks to the MEC server over a wireless channel.Considering the difference in residual energy among multiple smart devices,an energy factor is introduced to measure the bias of smart devices between energy consumption and latency.A reinforcement learning scheme based on the Q-learning algorithm is applied to co-optimize the offloading decision,the allocation of computational resources,and the selection of wireless channels.Simulation results show that the algorithm can effectively reduce the delay and energy consumption of task processing and accommodate more users.

关 键 词：移动边缘计算 计算卸载 信道选择 能量因子 

分 类 号：TN929.5[电子电信—通信与信息系统]