Intelligent Reflecting Surface Aided Multigroup Multicast MISO Communication Systems

Research output: Contribution to journalArticle

DOI

Authors

  • Gui Zhou
  • Cunhua Pan
  • Hong Ren
  • Kezhi Wang
  • Arumugam Nallanathan

External departments

  • Queen Mary University of London

Details

Original languageEnglish
Pages (from-to)3236-3251
Number of pages16
JournalIEEE Transactions on Signal Processing
Volume68
Early online date23 Apr 2020
DOIs
Publication statusPublished - 6 Jun 2020
Publication type

Research output: Contribution to journalArticle

Abstract

Intelligent reflecting surface (IRS) has recently been envisioned to offer unprecedented massive multiple-input multiple-output (MIMO)-like gains by deploying large-scale and low-cost passive reflection elements. By adjusting the reflection coefficients, the IRS can change the phase shifts on the impinging electromagnetic waves so that it can smartly reconfigure the signal propagation environment and enhance the power of the desired received signal or suppress the interference signal. In this paper, we consider downlink multigroup multicast communication systems assisted by an IRS. We aim for maximizing the sum rate of all the multicasting groups by the joint optimization of the precoding matrix at the base station (BS) and the reflection coefficients at the IRS under both the power and unit-modulus constraint. To tackle this non-convex problem, we propose two efficient algorithms. Specifically, a concave lower bound surrogate objective function has been derived firstly, based on which two sets of variables can be updated alternately by solving two corresponding second-order cone programming (SOCP) problems.Then, in order to reduce the computational complexity, we further adopt the majorization—minimization (MM) method for each set of variables at every iteration, and obtain the closed form solutions under loose surrogate objective functions. Finally, the simulation results demonstrate the benefits of the introduced IRS and the effectiveness of our proposed algorithms.

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