Joint Offloading and Energy Harvesting Design in Multiple Time Blocks for FDMA Based Wireless Powered MEC

• Main Authors: Zhiyan Yu, Gaochao Xu, Yang Li, Peng Liu, Long Li
• Format: Article
• Language: English
• Published: MDPI AG 2021-03-01
• Series: Future Internet
• Subjects: mobile edge computing; Wireless Power Transmission (WPT); task offloading; joint optimization; resource allocation
• Online Access: https://www.mdpi.com/1999-5903/13/3/70

The combination of mobile edge computing (MEC) and wireless power transfer (WPT) is recognized as a promising technology to solve the problem of limited battery capacities and insufficient computation capabilities of mobile devices. This technology can transfer energy to users by radio frequency (RF) in wireless powered mobile edge computing. The user converts the harvested energy, stores it in the battery, and utilizes the harvested energy to execute corresponding local computing and offloading tasks. This paper adopts the Frequency Division Multiple Access (FDMA) technique to achieve task offloading from multiple mobile devices to the MEC server simultaneously. Our objective is to study multiuser dynamic joint optimization of computation and wireless resource allocation under multiple time blocks to solve the problem of maximizing residual energy. To this end, we formalize it as a nonconvex problem that jointly optimizes the number of offloaded bits, energy harvesting time, and transmission bandwidth. We adopt convex optimization technology, combine with Karush–Kuhn–Tucker (KKT) conditions, and finally transform the problem into a univariate constrained convex optimization problem. Furthermore, to solve the problem, we propose a combined method of Bisection method and sequential unconstrained minimization based on Reformulation-Linearization Technique (RLT). Numerical results demonstrate that the performance of our joint optimization method outperforms other benchmark schemes for the residual energy maximization problem. Besides, the algorithm can maximize the residual energy, reduce the computation complexity, and improve computation efficiency.