Energy Optimization for Intelligent Reflecting Surface Assisted Mobile Edge Computing

Abstract

Mobile edge computing (MEC) is envisioned as a key enabler to support massive Internet of Things (IoT) devices with time-critical and computation-intensive computation tasks. However, the uplink transmission brings a huge burden to IoT devices with finite battery lifetime. The emerging intelligent reflecting surface (IRS) would be a promising technology to enhance the system performance in this case due to its capability to smartly control the wireless environments so as to enhance the energy and spectrum efficiencies of wireless communications. In this paper, we consider an IRS-assisted multi-device MEC system where each device follows the binary offloading policy. Since energy consumption is a vital concern for IoT devices, an energy minimization problem is formulated to minimize the total energy consumption of devices by jointly optimizing the binary offloading modes, CPU frequencies, offloading powers, offloading times and IRS phase shifts for all devices. A greedy-based algorithm is proposed to solve the challenging discontinuous problem. Simulation results demonstrate that the employment of IRS significantly reduce the energy consumption compared to the case without IRS.