Joint Optimization of IRS-Assisted Multiuser MIMO Systems With Low-Resolution DACs

Intelligent reflecting surface (IRS) technology is highly anticipated in future smart communication systems as it can improve spectral efficiency (SE) by adjusting signal phase shifts. This article investigates the SE and energy efficiency (EE) of IRS-assisted multiuser multiple-input multiple-output (MIMO) systems, where base station (BS) antennas are equipped with low-resolution digital-to-analog converters (DACs). In the pursuit of maximizing achievable SE, we undertake the complex task of jointly optimizing the transmission beamforming vector and the phase shift matrix of the IRS, which is known to be NP-hard. To tackle this nonconvexity in joint optimization, we employ an alternating optimization algorithm and apply successive approximation (SCA) and semidefinite relaxation (SDR) methods with the assistance of the convex optimization toolbox. To evaluate the system's achievable EE, we construct a realistic power consumption model and derive the theoretical expression for achievable EE. Numerical simulations clearly demonstrate that the alternating optimization scheme significantly enhances SE, while the utilization of low-resolution DACs effectively improves EE.