Joint Offloading and Computing Optimization in Wireless Powered Mobile-Edge Computing Systems

TL;DR

This paper introduces a unified wireless powered MEC system that optimizes energy transfer, computation offloading, and resource allocation to enhance performance and reduce energy consumption in IoT devices.

Contribution

It proposes a novel joint optimization framework for MEC and WPT in multiuser systems, including an optimal resource allocation scheme under latency constraints.

Findings

Optimized energy beamforming reduces total energy consumption.

Joint offloading and CPU frequency control improve system efficiency.

Numerical results outperform benchmark schemes.

Abstract

Mobile-edge computing (MEC) and wireless power transfer (WPT) have been recognized as promising techniques in the Internet of Things (IoT) era to provide massive low-power wireless devices with enhanced computation capability and sustainable energy supply. In this paper, we propose a unified MEC-WPT design by considering a wireless powered multiuser MEC system, where a multi-antenna access point (AP) (integrated with an MEC server) broadcasts wireless power to charge multiple users and each user node relies on the harvested energy to execute computation tasks. With MEC, these users can execute their respective tasks locally by themselves or offload all or part of them to the AP based on a time division multiple access (TDMA) protocol. Building on the proposed model, we develop an innovative framework to improve the MEC performance, by jointly optimizing the energy transmit beamformer at the AP, the central processing unit (CPU) frequencies and the numbers of offloaded bits at the users, as well as the time allocation among users. Under this framework, we address a practical scenario where latency-limited computation is required. In this case, we develop an optimal resource allocation scheme that minimizes the AP's total energy consumption subject to the users' individual computation latency constraints. Leveraging the state-of-the-art optimization techniques, we derive the optimal solution in a semi-closed form. Numerical results demonstrate the merits of the proposed design over alternative benchmark schemes.