Intelligent Reflecting Surface Aided Multigroup Multicast MISO Communication Systems

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Intelligent Reflecting Surface Aided Multigroup Multicast MISO Communication Systems. / Zhou, Gui; Pan, Cunhua; Ren, Hong; Wang, Kezhi; Nallanathan, Arumugam.

In: IEEE Transactions on Signal Processing, Vol. 68, 06.06.2020, p. 3236-3251.

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Zhou, Gui ; Pan, Cunhua ; Ren, Hong ; Wang, Kezhi ; Nallanathan, Arumugam. / Intelligent Reflecting Surface Aided Multigroup Multicast MISO Communication Systems. In: IEEE Transactions on Signal Processing. 2020 ; Vol. 68. pp. 3236-3251.

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@article{adb80bf5f2cb42d9bb8ec77575e846d6,
title = "Intelligent Reflecting Surface Aided Multigroup Multicast MISO Communication Systems",
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.",
keywords = "Intelligent reflecting surface (IRS), large intelligent surface (LIS), multigroup, multicast, alternating optimization, majorization—minimization (MM)",
author = "Gui Zhou and Cunhua Pan and Hong Ren and Kezhi Wang and Arumugam Nallanathan",
year = "2020",
month = jun,
day = "6",
doi = "10.1109/TSP.2020.2990098",
language = "English",
volume = "68",
pages = "3236--3251",
journal = "IEEE Transactions on Signal Processing",
issn = "1053-587X",
publisher = "IEEE",

}

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TY - JOUR

T1 - Intelligent Reflecting Surface Aided Multigroup Multicast MISO Communication Systems

AU - Zhou, Gui

AU - Pan, Cunhua

AU - Ren, Hong

AU - Wang, Kezhi

AU - Nallanathan, Arumugam

PY - 2020/6/6

Y1 - 2020/6/6

N2 - 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.

AB - 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.

KW - Intelligent reflecting surface (IRS)

KW - large intelligent surface (LIS)

KW - multigroup

KW - multicast

KW - alternating optimization

KW - majorization—minimization (MM)

U2 - 10.1109/TSP.2020.2990098

DO - 10.1109/TSP.2020.2990098

M3 - Article

VL - 68

SP - 3236

EP - 3251

JO - IEEE Transactions on Signal Processing

JF - IEEE Transactions on Signal Processing

SN - 1053-587X

ER -