Intelligent Reflecting Surface Aided Multigroup Multicast MISO Communication Systems
Intelligent Reflecting Surface Aided Multigroup Multicast MISO Communication Systems
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DOI：
10.1109/tsp.2020.2990098
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发表时间：
2020-01-01
影响因子：
5.4
通讯作者：
Nallanathan, Arumugam
中科院分区：
文献类型：
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作者：
Zhou, Gui;Pan, Cunhua;Nallanathan, Arumugam
智能反射表面（IRS）最近被设想通过部署大规模和低成本的无源反射元件来提供前所未有的大规模多输入多输出（MIMO）样增益。通过调整反射系数，IRS可以改变入射电磁波上的相移，使得它可以智能地重新配置信号传播环境，并增强所需接收信号的功率或抑制干扰信号。在本文中，我们考虑下行链路多组多播通信系统的IRS辅助。我们的目标是最大化的总和速率的所有多播组的联合优化的预编码矩阵在基站（BS）和反射系数在IRS下的功率和单位模约束。为了解决这个非凸问题，我们提出了两个有效的算法下的优化和最小化（MM）算法框架。具体地说，首先推导出每个用户速率的凹下界代理目标函数，基于该凹下界代理目标函数，通过求解两个相应的二阶锥规划（SOCP）问题，可以交替更新两组变量.然后，为了降低计算复杂度，我们推导出每一组变量在每次迭代时的速率的另一个凹下界函数，并获得这些松散替代目标函数下的封闭形式解。最后，仿真结果表明，在频谱和能量效率的引入IRS和我们提出的算法的收敛性和复杂性的有效性方面的好处。
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 under the majorization& x2013;minimization (MM) algorithm framework. Specifically, a concave lower bound surrogate objective function of each user& x0027;s rate 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 derive another concave lower bound function of each group& x0027;s rate for each set of variables at every iteration, and obtain the closed-form solutions under these loose surrogate objective functions. Finally, the simulation results demonstrate the benefits in terms of the spectral and energy efficiency of the introduced IRS and the effectiveness in terms of the convergence and complexity of our proposed algorithms.