Movable-Antenna-Enhanced Wireless-Powered Mobile-Edge Computing Systems

被引:0
|
作者
Chen, Pengcheng [1 ]
Yang, Yuxuan [3 ]
Lyu, Bin [1 ,2 ]
Yang, Zhen [1 ]
Jamalipour, Abbas [3 ]
机构
[1] Nanjing Univ Posts & Telecommun, Key Lab Minist Educ Broadband Wireless Commun & Se, Nanjing 210003, Peoples R China
[2] Nanjing Univ Posts & Telecommun, Jiangsu Engn Res Ctr Commun & NetworkTechnol, Nanjing 210003, Peoples R China
[3] Univ Sydney, Sch Elect & Informat Engn, Sydney, NSW 2006, Australia
来源
IEEE INTERNET OF THINGS JOURNAL | 2024年 / 11卷 / 21期
基金
中国国家自然科学基金;
关键词
Task analysis; Wireless communication; Servers; Radio frequency; Antennas; Resource management; Optimization; Movable antennas (MAs); MA positioning configurations; particle swarm optimization with variable local search (PSO-VLS); wireless-powered mobile-edge computing (WP-MEC); COMPUTATION RATE MAXIMIZATION; RESOURCE-ALLOCATION;
D O I
10.1109/JIOT.2024.3437201
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this article, we propose a movable antenna (MA)-enhanced scheme for wireless-powered mobile-edge computing (WP-MEC) system, where the hybrid access point (HAP) equipped with multiple MAs first emits wireless energy to charge wireless devices (WDs), and then receives the offloaded tasks from the WDs for edge computing. The MAs deployed at the HAP enhance the spatial Degrees of Freedom (DoFs) by flexibly adjusting the positions of MAs within an available region, thereby improving the efficiency of both downlink wireless energy transfer (WPT) and uplink task offloading. To balance the performance enhancement against the implementation intricacy, we further propose three types of MA positioning configurations, i.e., dynamic MA positioning, semidynamic MA positioning, and static MA positioning. In addition, the nonlinear power conversion of energy harvesting (EH) circuits at the WDs and the finite computing capability at the edge server are taken into account. Our objective is to maximize the sum computational rate (SCR) by jointly optimizing the time allocation, positions of MAs, energy beamforming matrix, receive combing vectors, and offloading strategies of WDs. To solve the nonconvex problems, efficient alternating optimization (AO) frameworks are proposed. Moreover, we propose a hybrid algorithm of particle swarm optimization with variable local search (PSO-VLS) to solve the subproblem of MA positioning. Numerical results validate the superiority of exploiting MAs over the fixed-position antennas (FPAs) for enhancing the SCR performance of WP-MEC systems.
引用
收藏
页码:35505 / 35518
页数:14
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