[1]李陶深,巩 健,曾续玲,等.无线供电MEC系统的计算能效最大化策略[J].郑州大学学报(工学版),2025,46(01):133-142.[doi:10.13705/j.issn.1671-6833.2024.01.014]
 LI Taoshen,GONG Jian,ZENG Xuling,et al.Computing Energy Efficiency Maximization Strategy of Wireless Powered Mobile Edge Computing Systems[J].Journal of Zhengzhou University (Engineering Science),2025,46(01):133-142.[doi:10.13705/j.issn.1671-6833.2024.01.014]
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无线供电MEC系统的计算能效最大化策略()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
46
期数:
2025年01期
页码:
133-142
栏目:
出版日期:
2024-12-23

文章信息/Info

Title:
Computing Energy Efficiency Maximization Strategy of Wireless Powered Mobile Edge Computing Systems
文章编号:
1671-6833(2025)01-0133-10
作者:
李陶深12 巩 健2 曾续玲2 吕 品2
1.南宁学院 信息工程学院,广西 南宁 530299;2.广西大学 计算机与电子信息学院,广西 南宁 530004
Author(s):
LI Taoshen12 GONG Jian2 ZENG Xuling2 LYU Pin2
1.College of Information Engineering, Nanning University, Nanning 530299, China; 2.School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China
关键词:
无线供电移动边缘计算系统 非正交多址接入 计算能效 能量收集 资源分配 计算卸载
Keywords:
wireless powered mobile edge computing(MEC) system non-orthogonal multiple access(NOMA) computing energy efficiency energy harvesting resource allocation computing offload
分类号:
TP393TP391TP273
DOI:
10.13705/j.issn.1671-6833.2024.01.014
文献标志码:
A
摘要:
为了解决无线供电移动边缘计算(MEC)系统的计算能效优化问题,提出一种基于非正交多址接入的无线供电MEC系统的资源分配策略。该策略将非线性能量收集模型应用到移动设备上,通过联合优化MEC服务器和移动设备的计算频率、执行时间、基站发射功率、设备发射功率、卸载时间和能量收集时间,比较充分地利用移动设备和MEC服务器的可用计算资源,提高设备的吞吐量和计算位数,进而最大限度地提升系统计算能效。将该联合优化问题转化为非凸分式规划问题,设计一种基于Dinkelbach的迭代算法来获得最优的资源分配方案。仿真实验表明:该资源分配策略所获得的系统计算能效更高,具有更好的性能增益。
Abstract:
In order to solve the computing energy efficiency optimization problem of the wireless powered mobile edge computing(MEC) system, a computing resource allocation strategy of wireless powered MEC system based on non-orthogonal multiple access(NOMA) was proposed, which applied a nonlinear energy harvesting model to mobile devices. By jointly optimizing the calculation frequency, execution time, base station transmission power, equipment transmission power, offloading time and energy collection time of MEC server and mobile equipment, this strategy could fully utilize the available computing resources of mobile devices and MEC servers, improve device throughput and computing bits, and thus maximize system computing energy efficiency. Then the joint optimization problem was transformed into a non-convex fractional programming problem, and an iterative algorithm based on Dinkelbach was designed to obtain the optimal resource allocation scheme. The comparative simulation results showed that the resource allocation strategy could achieve higher computing energy efficiency and better performance gains.

参考文献/References:

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更新日期/Last Update: 2024-12-31