Power Minimization of Intelligent Reflecting Surface-Aided Uplink IoT Networks
Jiao Wu, Byonghyo Shim
Abstract
Employing intelligent reflecting surfaces (IRSs) is emerging as a green alternative to massive antenna systems for improving signal quality and suppressing interference. Specifically, IRS is a planar surface consisting of a large number of low-cost and passive elements each being able to reflect the incident signal independently with an adjustable phase shift, thus the three-dimension (3D) passive beamforming can be collaboratively achieved without the need of any transmit radio-frequency (RF) chains. In this paper, we study the uplink power control of an IRS-aided Internet of Things (IoT) network under the quality of service (QoS) constraints at each user. Our goal is to minimize the total user power by jointly optimizing the phase shifts of IRS reflecting elements and the receiving beamforming at the BS, subject to each user's individual signal-to-interference-plus-noise ratio (SINR) constraint which characterizes its QoS. To solve the formulated non-convex optimization problem, we develop an efficient scheme, called the Riemannian manifold-based alternating optimization (RM-AO). Simulation results demonstrate that the proposed RM-AO algorithm saves the uplink transmit power significantly.