耦合相移下有源同时反射和透射智能反射面辅助的多用户安全通信

郝万明; 曾齐; 王芳; 杨守义

doi:10.11999/JEIT240149

耦合相移下有源同时反射和透射智能反射面辅助的多用户安全通信

doi: 10.11999/JEIT240149

郝万明^{1, 2, ,},
曾齐¹,
王芳¹,
杨守义¹

1.
郑州大学电气与信息工程学院郑州 450001
2.
东南大学移动通信全国重点实验室南京 210096

基金项目: 国家自然科学基金(62101499)，东南大学移动通信全国重点实验室开放研究基金(2024D12)

详细信息

作者简介:
郝万明：男，副教授，研究方向为毫米波通信，太赫兹通信，大规模MIMO技术，物理层安全技术，智能超表面技术等

曾齐：男，硕士生，研究方向为物理层安全技术，智能反射面技术等

王芳：女，副教授，研究方向为宽带无线通信，认知无线电技术等

杨守义：男，教授，研究方向为无线移动通信、移动云计算等

通讯作者:
郝万明　iewmhao@zzu.edu.cn

中图分类号: TN92
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- PDF下载量: 38
- 被引次数: 0
出版历程
- 收稿日期: 2024-03-07
- 修回日期: 2024-05-14
- 网络出版日期: 2024-05-24
- 刊出日期: 2024-09-26

Active Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surface Assisted Multi-user Security Communication with Coupled Phase Shift

HAO Wanming^{1, 2
, ,},
ZENG Qi¹,
WANG Fang¹,
YANG Shouyi¹

1.
School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China
2.
National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China

Funds: The National Natural Science Foundation of China (62101499), The Open Research Fund of National Mobile Communications Research Laboratory, Southeast University (2024D12)

摘要

摘要: 无源智能反射面在增强无线通信系统和提高物理层安全方面极具潜力，但是其存在严重的“双衰落”和半区域覆盖的缺点。为此，该文研究了一种有源同时反射和透射智能反射面(STAR-RIS)，并在考虑反射和透射相移互耦合条件下，建立一个联合优化基站波束和有源STAR-RIS波束的安全能效最大化问题。为求解所形成的非凸优化问题，利用连续凸近似、罚函数法、半正定松弛、交替优化技术将原问题转化为凸问题，并提出一种基于惩罚对偶分解算法。仿真结果验证了该文所提算法的有效性。
- 智能反射面 /
- 物理层安全 /
- 安全能效最大化 /
- 耦合相移
Abstract: Passive intelligent reflecting surfaces hold great potential in enhancing wireless communication systems and improving physical layer security, but they suffer from significant drawbacks such as “double fading” and partial coverage. Therefore, an active Simultaneously Transmitting And Reflecting Reconfigurable Intelligent Surface (STAR-RIS) is conducted in this paper. Considering the coupling between reflection and transmission phase shifts, a joint optimization problem for maximizing the security energy efficiency of base station beams and active STAR-RIS beams is formulated. To solve the resulting non-convex optimization problem, continuous convex approximation, penalty function method, semi-definite relaxation, and alternating optimization techniques are employed to transform the original problem into a convex one. Additionally, a penalty dual decomposition algorithm is proposed. Simulation results validate the effectiveness of the algorithm proposed in this paper.
- Intelligent reflecting surface /
- Physical layer security /
- Security-energy efficiency maximization /
- Coupled phase-shift

HTML全文

图 1 系统模型

下载: 全尺寸图片幻灯片

图 2 系统能效随迭代次数变化

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图 3 系统能效与总功率的关系

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图 4 系统能效与功率分配系数的关系

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图 5 系统能效与STAR-RIS单元数的关系

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图 6 系统能效与基站到STAR-RIS距离的关系

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1 基于PDD的能效最大化算法

初始化优化变量，收敛精度$\varepsilon = {10^{ - 3}}$
重复
重复
求解问题式(17)，获得${{\boldsymbol{w}}_k}$
求解问题式(18)，获得${\boldsymbol{A}}$
求解问题式(21)，获得$\{ {{\boldsymbol{\theta}} _{\text{R}}},{{\boldsymbol{\theta}} _{\text{T}}}\} $
求解式(25)、式(26)和式(28)，获得$ \{ {\mathop{{\boldsymbol{\theta}} }\limits^{\smile} }_{\text{R}} ,{\mathop{{\boldsymbol{\theta}} }\limits^{\smile} } _{\text{T}}\} $
直到收敛
判断如果$ \varDelta \le \omega $，更新${{\boldsymbol{\lambda}} _i} = {{\boldsymbol{\lambda}} _i} + \dfrac{1}{\rho }({{\mathop {{\boldsymbol{\theta}} }\limits^{\smile}} _i} - {{ {{\boldsymbol{\theta}} }}_i}),\forall i$
否则设置$\rho = {\text{c}}\rho $
更新$\omega = 0.9\varDelta $
直到$\varDelta \le \varepsilon $，结束循环

下载: 导出CSV

参考文献(27)

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