智能反射面辅助通感一体化系统安全资源分配算法

朱政宇; 杨晨一; 李铮; 郝万明; 杨婧; 孙钢灿

doi:10.11999/JEIT240083

智能反射面辅助通感一体化系统安全资源分配算法

doi: 10.11999/JEIT240083

朱政宇^{1, 2},
杨晨一¹,
李铮¹,
郝万明¹,
杨婧¹,
孙钢灿^1, ,

1.
郑州大学电气与信息工程学院郑州 450001
2.
东南大学移动通信国家重点实验室南京 210018

基金项目: 国家重点研发计划(2022YFD2001200)，国家自然科学基金(61922072)，中国博士后科学基金(2023T160596)，河南省自然科学基金优青项目(232300421097)，河南省高校科技创新人才支持计划(23HASTIT019)，河南省博士后经费资助(202001015)，东南大学移动通信国家重点实验室开放课题(2023D11)

详细信息

作者简介:
朱政宇：男，副教授，研究方向为无线通信和信号处理、5G/6G、智能反射面辅助通信等

杨晨一：女，硕士生，研究方向为智能反射面辅助、通感一体化

李铮：男，博士生，研究方向为无线通信和信号处理

郝万明：男，副教授，研究方向为毫米波/太赫兹智能超表面、通感一体化

杨婧：女，讲师，研究方向为通信感知一体化

孙钢灿：男，教授，研究方向为通信信号处理、通信信号关键参数盲估计等

通讯作者:
孙钢灿　iegcsun@zzu.edu.cn

中图分类号: TN915.0
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- 被引次数: 0
出版历程
- 收稿日期: 2024-02-04
- 修回日期: 2024-05-04
- 网络出版日期: 2024-05-17

Resource Allocation Algorithm for Intelligent Reflecting Surface-assisted Secure Integrated Sensing And Communications System

1.
School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
2.
National Mobile Communications Research Laboratory, Southeast University, Nanjing 210018, China

Funds: The National Key R&D Program of China (2022YFD2001200), The National Natural Science Foundation of China (61922072), China Postdoctoral Science Foundation (2023T160596), The Natural Science Foundation of Henan Province (232300421097), The Program for Science & Technology Innovation Talents in Universities of Henan Province (23HASTIT019), Henan Postdoctoral Foundation (202001015), The Open Research Fund of National Mobile Communications Research Laboratory, Southeast University (2023D11)

摘要

摘要: 为了解决6G通感一体化系统(ISAC)中信息传输安全以及频谱紧张的问题，该文提出一种智能反射面(IRS)辅助ISAC系统安全资源分配算法。首先，在IRS-ISAC系统中，用户受到窃听者的恶意攻击时，通过干扰机发射的干扰信号和IRS智能地调节反射相移，重新配置传输环境，以提高系统的物理层安全。其次，考虑在基站和干扰机的最大发射功率约束，IRS反射相移约束以及雷达的信干噪比约束下，建立一个联合优化基站发射波束成形、干扰机预编码和IRS相移的系统保密率最大化优化问题。然后，利用交替优化和半正定松弛(SDR)算法等方法对原非凸优化问题进行转换，求出一个能够得到确定解的凸优化问题。最后提出一种基于交替迭代的安全资源分配算法。仿真结果验证了所提算法的安全性和有效性以及IRS-ISAC系统的优越性。
- 通感一体化 /
- 智能反射面 /
- 物理层安全 /
- 交替优化
Abstract: In order to solve the problems of information security, and spectrum limitation in Integrated Sensing And Communications (ISAC) systems, a secure resource allocation scheme in Intelligent Reflecting Surface (IRS)-assisted ISAC systems is investigated in this paper. To start with, in this IRS-ISAC system, where the user is being maliciously attacked by eavesdroppers, the security of the system is ensured by incorporating a jammer and deploying an IRS that utilizes its intelligent regulation of the wireless environment. Then, a secrecy rate maximization problem that subjects to the maximum transmit power constraints of the base station and the jammer, the IRS reflecting phase shift constraints, and the radar’s signal-to-noise ratio constraints is formulated by jointly designing the transmit beamforming of base station, jammer precoding vectors, and IRS phase shifts. Next, utilizing techniques such as alternating optimization and Semi-Definite Relaxation (SDR) algorithm, the original non-convex optimization problem is reformulated into a convex optimization problem, capable of determining a definitive solution. Finally, simulation results verify the security and effectiveness of the proposed algorithm and the superiority of the IRS-ISAC system.
- Integrated Sensing And Communication(ISAC) /
- Intelligent Reflecting Surface(IRS) /
- Physical layer security /
- Alternating optimization

HTML全文

图 1 系统模型

下载: 全尺寸图片幻灯片

图 2 保密率随迭代次数变化曲线

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图 3 系统保密率与基站最大发射功率的关系

下载: 全尺寸图片幻灯片

图 4 系统保密率与IRS的反射元素数量的关系

下载: 全尺寸图片幻灯片

图 5 雷达SINR与基站发射功率的关系

下载: 全尺寸图片幻灯片

1 求解式(10)的交替优化算法

输入：$ {P_{\text{B}}} $, $ {P_{\text{J}}} $, ${\varGamma _{\text{t}}}$, $ {{\boldsymbol{H}}_{{\text{I, }}m}} $, $ {{\boldsymbol{G}}_{{\text{I, }}m}} $, ${{\boldsymbol{h}}}_{{\text{B, }}m}^{H} $, ${{\boldsymbol{g}}}_{{\text{J, }}m}^{H} $, $\varepsilon $, $L$
输出：$ {{\boldsymbol{w}}} $, $ {{\boldsymbol{v}}} $, $ {{\boldsymbol{\theta}} } $
(1) 初始化$ {{{\boldsymbol{w}}}^{(0)}} $, $ {{{\boldsymbol{v}}}^{(0)}} $和$ {{{\boldsymbol{\theta}} }^{(0)}} $；
(2) 设置迭代次数$ r = 1 $, $ {{\boldsymbol{W}}^{(0)}} = {{\boldsymbol{w}}}{{{\boldsymbol{w}}}^{{\mathrm{H}}} } $, $ {{\boldsymbol{F}}^{(0)}} = {{\boldsymbol{v}}}{{{\boldsymbol{v}}}^{{\mathrm{H}}} } $；
(3) 重复
(4) 　在给定$ {{{\boldsymbol{\theta}} }^{(r - 1)}} $, $ {{\boldsymbol{W}}^{(r - 1)}} $和$ {{\boldsymbol{F}}^{(r - 1)}} $时，求解式(11)；根据　　　式(18)和式(19)分别找到最优的$ {t}_{\text{s}}^{(r)} $和$ t_{{\text{e, }}k}^{(r)} $；
(5) 　在给定$ {t}_{\text{s}}^{(r)} $和$ t_{{\text{e, }}k}^{(r)} $时，通过求解式(20)，找到最优的$ {{\boldsymbol{W}}}^{(r)} $ 　　　和$ {{\boldsymbol{F}}^{(r)}} $，通过特征值分解得出$ {{{\boldsymbol{w}}}^{(r)}} $和$ {{{\boldsymbol{v}}}^{(r)}} $；
(6) 　在给定$ {{{\boldsymbol{w}}}^{(r)}} $和$ {{{\boldsymbol{v}}}^{(r)}} $时，方法同上，通过求解式(21)，找到　　　最优的$ {{{\boldsymbol{\theta }}}^{(r)}} $；
(7) 更新$r{\text{ = }}r{\text{ + 1}} $
(8) 直到问题式(10)的目标中的目标值下降$ \le \varepsilon $或者$r = L$。

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