Intelligent-Reflecting-Surface-Aided Mobile Edge Computing With Binary Offloading: Energy Minimization for IoT Devices

Mobile edge computing (MEC) is envisioned as a promising technique to support computation-intensive and time-critical applications in future Internet of Things (IoT) era. However, the uplink transmission performance will be highly impacted by the hostile wireless channel, the low bandwidth, and the low transmission power of IoT devices. Recently, intelligent reflecting surface (IRS) has drawn much attention because of its capability to control the wireless environments so as to enhance the spectrum and energy efficiencies of wireless communications. In this article, we consider an IRS-aided multidevice MEC system where each IoT device follows the binary offloading policy, i.e., a task has to be computed as a whole either locally or remotely at the edge server. We aim to minimize the total energy consumption of devices by jointly optimizing the binary offloading modes, the CPU frequencies, the offloading powers, the offloading times, and the IRS phase shifts for all devices. Two algorithms, which are greedy based and penalty based, are proposed to solve the challenging nonconvex and discontinuous problem. It is found that the penalty-based method has only linear complexity with respect to the number of devices, but it performs close to the greedy-based method with cubic complexity with respect to the number of devices. Furthermore, binary offloading via IRS indeed saves more energy compared to the case without IRS.

I. Introduction

In traditional mobile cloud computing systems, the data of mobile devices would be sent to the cloud server in the core network for further computing [1], [2]. However, this scheme cannot fit the future Internet of Things (IoT) era due to the explosively increasing amount of data generated by a massive number of IoT wireless devices and the time-critical requirements of new applications, such as industrial monitoring, disaster early warning, and healthcare [3]–[5]. Recently, a new computing paradigm called mobile edge computing (MEC) has emerged and drawn a lot of attention from both academia and industry [6]–[8]. It pushes the computing capability from the core network to the network edge. In this context, IoT devices can offload their intensive computation to the nearby edge server, which is co-deployed with the access point (AP). By offloading computation to the network edge instead of the cloud, IoT devices can be served with ultralow latency and core link congestion can be mitigated significantly [9].