基于无线供能和节点间合作的二进制计算卸载方案  

Binary Computing Offloading Scheme Based on Wireless Power Supply and Node Cooperation

作　　者：秦娥[1] 许方耀 徐也淳 池凯凯[1] 毛科技[1] 李卫锋 何文秀 QIN E;XU Fangyao;XU Yechun;CHI Kaikai;MAO Keji;LI Weifeng;HE Wenxiu(College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou Zhejiang 310023,China;School of Public Administration,Zhejiang University of Technology,Hangzhou Zhejiang 310023,China;Watone Cloud Data Co.,Ltd.,Hangzhou Zhejiang 310030,China;Zhijiang College,Zhejiang University of Technology,Shaoxing Zhejiang 312030,China)

机构地区：[1]浙江工业大学计算机科学与技术学院,浙江杭州310023 [2]浙江工业大学公共管理学院,浙江杭州310023 [3]浙江华通云数据科技有限公司,浙江杭州310030 [4]浙江工业大学之江学院,浙江绍兴312030

出　　处：《传感技术学报》2024年第6期1014-1024,共11页Chinese Journal of Sensors and Actuators

基　　金：浙江省基础公益研究计划项目(LGG22F020014);国家自然科学基金项目(62072410)。

摘　　要：针对性研究了无线供能下的移动边缘计算,其中计算节点能够自主执行计算任务或将其卸载给空闲节点或边缘服务器,以最大化节点的总计算速率(Sum Computing Rate,SCR)。首先,将SCR最大化问题建模为一个非凸问题,考虑到能量因果和任务因果等约束。接着,提出了一个基于深度强化学习的解决方案,采用深度神经网络输出近似最优的二进制卸载决策。最后,设计了高效算法来解决在给定卸载决策下的子问题。该方案具备在线学习能力,具有快速收敛和低计算复杂度的特点,实现了近似最大SCR。Mobile edge computing under wireless power supply is focused on,where computing nodes can independently perform computing tasks or unload them to idle nodes or edge computing server to maximize the sum computing rate(SCR)of nodes.Firstly,the SCR maximi-zation problem is modeled as a non-convex problem,taking into account the constraints such as energy causality and task causality.Next,a solution based on deep reinforcement learning is proposed,which uses deep neural networks to output approximately optimal binary off-loading decisions.Finally,an efficient algorithm is designed to solve the subproblem under a given offloading decision.This scheme has the ability of online learning,fast convergence,and low computational complexity,achieving the approximate maximum SCR.

关 键 词：移动边缘计算 无线供能 节点间协作 二进制卸载 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]