Energy Minimization for Intelligent Reflecting Surface-Assisted Mobile Edge Computing

Abstract

Intelligent reflecting surface (IRS) has been increasingly considered in mobile edge computing (MEC), assisting smart terminals (STs) in offloading computationally-intense tasks to base stations (BSs). This paper presents a new IRS-assisted MEC framework, which jointly optimizes the local CPU frequencies of the STs, the receive beamformers of the BS, the ST offloading schedules, and the IRS phase configuration, to minimize the energy consumption of the STs. To this end, we reveal that the optimal CPU frequency is time-invariant for each ST. Under flat-fading channels, the IRS phases and the receive beamformers of the BS can be then decoupled from the offloading schedules. Based on this structure, we develop an alternating optimization to solve the IRS phase configuration and the receive beamformers, and then exploit the Lagrange duality method to solve the offloading schedules. We prove that the overall algorithm is guaranteed to compute a stationary point solution for the problem of interest with a low complexity. Under frequency-selective channels, we also develop a new alternating optimization algorithm to minimize the energy consumption, where manifold optimization is leveraged to effectively solve the IRS phase shifts. Numerical results show that the proposed algorithms are superior to existing techniques in terms of energy efficiency under both flat-fading and frequency-selective channels.