Joint Symbol-Level Precoding and Reflecting Designs for IRS-Enhanced MU-MISO Systems
Joint Symbol-Level Precoding and Reflecting Designs for IRS-Enhanced MU-MISO Systems
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DOI：
10.1109/twc.2020.3028371
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发表时间：
2019-12
影响因子：
10.4
通讯作者：
Rang Liu;Ming Li;Qian Liu;A. L. Swindlehurst
中科院分区：
文献类型：
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作者：
Rang Liu;Ming Li;Qian Liu;A. L. Swindlehurst
智能反射表面(IRS)已经成为一种革命性的解决方案，通过以经济高效和硬件高效的方式改变传播环境来增强无线通信。此外，符号级预编码(SLP)由于其在将多用户干扰(MUI)转化为有用信号能量方面的优势而引起了人们的极大关注。因此，研究在符号级预编码系统中使用IRS以通过操纵多用户信道来更有效地利用MUI是很有意义的。在本文中，我们主要研究IRS增强多用户多输入单输出(MU-MISO)系统中的联合符号级预编码和反射设计。同时考虑了功率最小化和服务质量(Qos)平衡问题。为了解决联合优化问题，我们提出了一种高效的迭代算法，将其分解为单独的符号级预编码问题和块级反射设计问题。采用了一种高效的基于梯度投影的算法来设计符号级预编码，并采用了一种基于黎曼共轭梯度的算法来解决反射设计问题。仿真结果证明了IRS算法在性能上的显著提高，并说明了所提算法的有效性。
Intelligent reflecting surfaces (IRSs) have emerged as a revolutionary solution to enhance wireless communications by changing propagation environment in a cost-effective and hardware-efficient fashion. In addition, symbol-level precoding (SLP) has attracted considerable attention recently due to its advantages in converting multiuser interference (MUI) into useful signal energy. Therefore, it is of interest to investigate the employment of IRS in symbol-level precoding systems to exploit MUI in a more effective way by manipulating the multiuser channels. In this article, we focus on joint symbol-level precoding and reflecting designs in IRS-enhanced multiuser multiple-input single-output (MU-MISO) systems. Both power minimization and quality-of-service (QoS) balancing problems are considered. In order to solve the joint optimization problems, we develop an efficient iterative algorithm to decompose them into separate symbol-level precoding and block-level reflecting design problems. An efficient gradient-projection-based algorithm is utilized to design the symbol-level precoding and a Riemannian conjugate gradient (RCG)-based algorithm is employed to solve the reflecting design problem. Simulation results demonstrate the significant performance improvement introduced by the IRS and illustrate the effectiveness of our proposed algorithms.