Capacity Improvement for Intelligent Reflecting Surface-Assisted Wireless Systems With a Small Portion of Active Elements

Abstract

In this study, the use of intelligent reflecting surfaces (IRSs) to improve the capacity of a multi-user multiple-input-single-output downlink system is investigated. Unlike existing IRS-assisted wireless systems equipped with entirely passive IRS elements or purely active IRS elements, we propose a new IRS architecture in which each IRS element can operate either in active mode or in passive mode. To reduce power consumption, only a small portion of the IRS elements operate in active mode. We seek to maximize the capacity of the proposed IRS-assisted wireless system by jointly optimizing the phase shifts of all IRS elements, the transmit beamforming of the base station, and the IRS active-passive mode vector, which leads to a non-convex problem. In this study, the non-convex problem is addressed by using a probability learning-based algorithm from the cross-entropy optimization framework with an explicit expression for learning the targeted sampling distribution’s tilting parameters. The simulation results reveal that a significant capacity improvement over traditional IRS-assisted wireless systems with entirely passive IRS elements can be achieved using the proposed scheme, which employs only a small portion of active IRS elements.