Joint task processing/offloading mode selection and resource-allocation for backscatter-aided and wireless-powered MEC

计算机科学 资源配置 无线 移动边缘计算 凸优化 能源消耗 坐标下降 模式(计算机接口) 任务(项目管理) 频道(广播) 实时计算 分布式计算 服务器 计算机网络 正多边形 算法 电信 管理 经济 生态学 几何学 数学 生物 操作系统
作者
KunLi Shi,Fei Wang
出处
期刊:Computer Networks [Elsevier BV]
卷期号:224: 109584-109584 被引量:4
标识
DOI:10.1016/j.comnet.2023.109584
摘要

We propose to develop the joint task processing/offloading mode selection and system resource-allocation schemes for a backscatter-aided and wireless-powered mobile edge computing (MEC), where multiple edge users (EUs), a hybrid access point (H-AP) working as a dedicated energy source and an information receiver, and several carrier emitters (CEs) coexist. We aim to maximize the residual energy of all energy harvesting based EUs while ensuring that EUs can complete their tasks, by jointly optimizing EUs’ task processing mode (local computing mode or edge computing mode), task offloading mode, and system resource-allocation. For the task offloading mode, each EU can select the bistatic backscatter (BB) communication mode or the harvest-then-transmit (HTT) mode to directly offload its task to the MEC server co-located with the H-AP. Also, each EU can select the ambient backscatter (AB)+BB communication mode or the AB+HTT communication mode to backscatter its data to nearby EUs through AB communication and then offload data to the MEC server with the help of nearby EUs through HTT or BB communication. First, we formulate a residual energy maximization problem for the considered system. Since the formulated optimization problem is non-convex with multiple coupled variables, we adopt the block coordinate descent (BCD) method to decompose it into several subproblems. Then, we utilize the alternating directions method of multipliers (ADMM) to transform the non-convex subproblems into convex subproblems, and propose the distributed joint task processing/offloading mode selection and system resource-allocation schemes. Finally, we verify and evaluate the performance of our proposed scheme through numerical analyses, which show that our proposed scheme outperforms the other benchmark schemes in terms of the residual energy and the task completion rate of EUs by jointly selecting the task processing mode and the task offloading mode for each EU.

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