Intelligent Reflecting Surface Aided Multigroup Multicast MISO Communication Systems

Abstract

Intelligent reflecting surface (IRS) has recently been envisioned to offer\nunprecedented massive multiple-input multiple-output (MIMO)-like gains by\ndeploying large-scale and low-cost passive reflection elements. By adjusting\nthe reflection coefficients, the IRS can change the phase shifts on the\nimpinging electromagnetic waves so that it can smartly reconfigure the signal\npropagation environment and enhance the power of the desired received signal or\nsuppress the interference signal. In this paper, we consider downlink\nmultigroup multicast communication systems assisted by an IRS. We aim for\nmaximizing the sum rate of all the multicasting groups by the joint\noptimization of the precoding matrix at the base station (BS) and the\nreflection coefficients at the IRS under both the power and unit-modulus\nconstraint. To tackle this non-convex problem, we propose two efficient\nalgorithms under the majorization--minimization (MM) algorithm framework.\nSpecifically, a concave lower bound surrogate objective function of each user's\nrate has been derived firstly, based on which two sets of variables can be\nupdated alternately by solving two corresponding second-order cone programming\n(SOCP) problems. Then, in order to reduce the computational complexity, we\nderive another concave lower bound function of each group's rate for each set\nof variables at every iteration, and obtain the closed-form solutions under\nthese loose surrogate objective functions. Finally, the simulation results\ndemonstrate the benefits in terms of the spectral and energy efficiency of the\nintroduced IRS and the effectiveness in terms of the convergence and complexity\nof our proposed algorithms.\n