双RIS辅助的MISO系统吞吐量最大化研究

谢文武; 张沁可; 梁锡涛; 刘晨宇; 余超; 王骥

doi:10.11999/JEIT240612

双RIS辅助的MISO系统吞吐量最大化研究

doi: 10.11999/JEIT240612

谢文武¹,
张沁可¹,
梁锡涛^{1, 2},
刘晨宇¹,
余超^1, ,,
王骥²

1.
湖南理工学院信息科学与工程学院岳阳 414006
2.
华中师范大学物理科学与技术学院武汉 430079

基金项目: 国家自然科学基金 (62472169)，湖南省自然科学基金(2023JJ50045, 2024JJ7218, 2024JJ7219)，湖南省教育厅项目(22B0676, 23C0217)，湖南省大学生创新创业项目(S202410543061)

详细信息

作者简介:
谢文武：男，副教授，研究方向为5G&6G基带算法、NFC等

张沁可：女，硕士生，研究方向为RIS、凸优化

梁锡涛：男，硕士生，研究方向为5G/6G无线通信

刘晨宇：男，硕士生，研究方向为RIS、凸优化

余超：男，讲师，研究方向为NOMA、凸优化

王骥：男，副教授，研究方向为5G/6G无线通信

通讯作者:
余超　12012017@hnist.edu.cn

中图分类号: TN911
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出版历程
- 收稿日期: 2024-07-17
- 修回日期: 2024-11-15
- 网络出版日期: 2024-11-29

Throughput Maximization for Double RIS-Assisted MISO Systems

XIE Wenwu¹,
ZHANG Qinke¹,
LIANG Xitao^{1, 2},
LIU Chenyu¹,
YU Chao^{1
, ,},
WANG Ji²

1.
School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang 414006, China
2.
College of Physical Science and Technology, Central China Normal University, Wuhan 430079, China

Funds: The National Natural Science Foundation of China (62472169), Hunan Provincial Natural Science Foundation (2023JJ50045, 2024JJ7218, 2024JJ7219), The Project of Education Bureau of Hunan Province (22B0676, 23C0217), Hunan Provincial College Students Innovation and Entrepreneurship-Project: (S202410543061)

摘要

摘要: 近年来，有源可重构智能表面(ARIS)技术获得了学术界的广泛关注。然而，ARIS在多RIS辅助无线通信系统中的应用还缺乏相关研究。针对此问题，该文提出基于双RIS辅助的无线通信系统模型。模型假设基站(BS)和用户之间的直连链路受阻，仅通过RIS形成的反射链路进行通信。在此基础上，根据ARIS与被动RIS(PRIS)的不同组合情况，提出4种RIS组合模型。模型的目标是优化基站波束赋形、RIS的相移矩阵和功率分配因子，以最大化系统通信容量。由于该优化问题为非凸问题，该文采用了交替优化算法(AO)与连续凸逼近(SCA)对问题进行处理。仿真结果表明，无论基站发射功率高或低，TAAR组合模型的性能均显著优于传统单ARIS配置。
- 双RIS /
- MISO /
- 交替优化算法 /
- 凸逼近
Abstract: In recent years, Active Reconfigurable Intelligent Surface (ARIS) technology has received extensive attention from academia. However, there is still a lack of research on the application of ARIS in multi-RIS assisted wireless communication systems. A dual RIS assisted wireless communication system is proposed in this paper The model assumes that the direct link between the Base Station (BS) and the user is blocked and only communicates through the reflection link formed by the RIS. On this basis, according to the different combinations of ARIS and Passive RIS (PRIS), four RIS combination models are proposed. The model aims to maximize the communication capacity of system by optimizing the BS beamforming, RIS phase matrix and power allocation factor. To tackle this non-convex optimization problem, the paper proposes an Alternating Optimization (AO) algorithm and employs Successive Convex Approximation (SCA). Simulation results illustrated that under the condition of total power constraints, the performance of the Transmitter-ARIS ARIS-Receive combination model significantly superior to the traditional signal ARIS model, regardless of whether the transmit power of the BS is high or low.
- Double Reconfiguration Intelligent Surface (RIS) /
- Multiple-Input Single-Output (MISO) /
- Alternating Optimization (AO) algorithm /
- Convex approximation

HTML全文

图 1 双RIS辅助单用户场景下无线通信系统模型

下载: 全尺寸图片幻灯片

图 2 不同组合模型下不同放大功率$a_{\max }^2$的系统容量

下载: 全尺寸图片幻灯片

图 3 不同发射功率$P$的系统容量

下载: 全尺寸图片幻灯片

1 基于AO算法的信道对齐流程

(1) 初始化参数${{\theta}} _1^{(0)}$, ${{\theta}} _2^{(0)}$, ${{\mathbf{\omega }}^{(0)}}$迭代数$n = 0$，收敛门限$\varepsilon $
(2) 利用初始参数计算$ \gamma _{{\text{TPPR}}}^n $的值
(3) 迭代开始：
(4) 　固定${{\theta} _2}$, ${\mathbf{\omega }}$，求解式(15)得到${{\theta} _1}$
(5) 　固定${{\theta} _1}$, ${\mathbf{\omega }}$，求解式(16)得到${{\theta} _2}$
(6) 　固定${{\theta} _1}$,${{\theta} _2}$，求解式(17)得到${\mathbf{\omega }}$
(7) 　使用${{\theta} _1}$,${{\theta} _2}$, ${\mathbf{\omega }}$，计算$\gamma _{{\text{TPPR}}}^{n + 1}$的值
(8) 　　判断$\left\| {\gamma _{{\text{TPPR}}}^{n + 1} - \gamma _{{\text{TPPR}}}^n} \right\| \le \varepsilon $或$n \ge 100$是否成立，若不满　　　　足条件，$n = n + 1$返回步骤4
(9) 结束循环
(10) 得到最优解${{\theta} _1}$, ${{\theta} _2}$, ${\mathbf{\omega }}$

下载: 导出CSV

2 基于SCA的AO算法流程

(1) 初始化参数${\theta} _1^{(0)}$, ${\theta} _2^{(0)}$, ${{\mathbf{\omega }}^{(0)}}$, ${\tau _0}$, ${\kappa _0}$，迭代数$n = 0$，收敛　门限$\varepsilon $
(2) 计算$\gamma _{{\text{TAPR}}}^n$的值
(3) 迭代开始:
(4) 　固定${{\theta} _2}$, ${\mathbf{\omega }}$，求解问题(P2.1)，将解进行高斯随机化后得到　　　 ${\theta} _1^{n + 1}$
(5) 　固定${{\theta} _1}$, ${\mathbf{\omega }}$，求解问题(P2.2)，将解进行高斯随机化后得到　　　 ${\theta} _2^{n + 1}$
(6) 　固定${{\theta} _1}$, ${{\theta} _2}$，求解问题(P2.3)得到${{\mathbf{\omega }}^{n + 1}}$
(7) 　　使用${\theta} _1^{n + 1}$, ${\theta} _2^{n + 1}$,${{\mathbf{\omega }}^{n + 1}}$，计算$\gamma _{{\text{TAPR}}}^{n + 1}$的值
(8) 　　　判断收敛条件$\left\| {\gamma _{{\text{TAPR}}}^{n + 1} - \gamma _{{\text{TAPR}}}^n} \right\| \le \varepsilon $或$n \ge 100$是否成　　　　　立，若满足条件，迭代结束，否则，$n = n + 1$返回步　　　　　骤4
(9) 得到最优解${{\theta} _1}$, ${{\theta} _2}$, ${\mathbf{\omega }}$

下载: 导出CSV

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