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

• Published in: IEEE internet of things journal Vol. 11; no. 21; pp. 35505 - 35518
• Main Authors: Chen, Pengcheng, Yang, Yuxuan, Lyu, Bin, Yang, Zhen, Jamalipour, Abbas
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.11.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Antennas; Beamforming; Computation offloading; Configuration management; Edge computing; Energy conversion; Energy harvesting; MA positioning configurations; Mobile computing; Movable antennas (MAs); Optimization; Particle swarm optimization; particle swarm optimization with variable local search (PSO-VLS); Performance enhancement; Radio frequency; Resource management; Servers; Task analysis; Wireless communication; wireless-powered mobile-edge computing (WP-MEC)
• DOI: 10.1109/JIOT.2024.3437201
• ISSN: 2327-4662

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.