Article(id=1251893505136738722, tenantId=1146029695717560320, journalId=1251234473337991274, issueId=1251893504037831074, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1003-3114.2025.05.004, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1745683200000, receivedDateStr=2025-04-27, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1776404270681, onlineDateStr=2026-04-17, pubDate=1758124800000, pubDateStr=2025-09-18, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1776404270681, onlineIssueDateStr=2026-04-17, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1776404270680, creator=13701087609, updateTime=1776404270680, updator=13701087609, issue=Issue{id=1251893504037831074, tenantId=1146029695717560320, journalId=1251234473337991274, year='2025', volume='51', issue='5', pageStart='877', pageEnd='1134', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1776404270419, creator=13701087609, updateTime=1776404832543, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1251895861849043019, tenantId=1146029695717560320, journalId=1251234473337991274, issueId=1251893504037831074, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1251895861849043020, tenantId=1146029695717560320, journalId=1251234473337991274, issueId=1251893504037831074, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=911, endPage=918, ext={EN=ArticleExt(id=1251893507024175530, articleId=1251893505136738722, tenantId=1146029695717560320, journalId=1251234473337991274, language=EN, title=Joint Robust Beamforming Design for IRS-assisted Physical Layer Key Generation Under Hardware Impairments, columnId=1251893506944483753, journalTitle=Radio Communications Technology, columnName=Special Topic: 6G and IoT Technologies, runingTitle=null, highlight=null, articleAbstract=

This paper investigates an Intelligent Reflecting Surface (IRS)-assisted Physical-layer Key Generation (PKG) system under residual Transceiver Hardware Impairments(THI). A closed-form expression for the Key Generation Rate(KGR) is derived, and a KGR maximization problem is formulated under the base station transmit power constraint and the unit-modulus constraint on the IRS phase shifts. To solve this problem, a robust optimization algorithm is proposed, which integrates Alternating Optimization(AO), Successive Convex Approximation (SCA), Semi-Definite Relaxation(SDR), and penalty methods to iteratively optimize the transmit beamforming and IRS phase shifts. Numerical simulation results demonstrate that the proposed robust algorithm can effectively mitigate the impact of hardware impairments and improve the KGR.

, correspAuthors=Shaochuan YANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Yi WANG, Shaochuan YANG, Fei ZHAO, Baofeng JI, Zheng CHU, Chunguo LI), CN=ArticleExt(id=1251893543707558576, articleId=1251893505136738722, tenantId=1146029695717560320, journalId=1251234473337991274, language=CN, title=硬件损伤情况下IRS辅助密钥生成系统鲁棒波束赋形设计, columnId=1251893507133227435, journalTitle=无线电通信技术, columnName=专题:6G与物联网技术, runingTitle=null, highlight=null, articleAbstract=

研究了存在残余收发信机硬件损伤(Transceiver Hardware Impairments,THI)情况下的智能反射面(Intelligent Reflecting Surface,IRS)辅助的物理层密钥生成(Physical-layer Key Generation,PKG)系统,推导了密钥生成速率(Key Generation Rate,KGR)的闭合表达式,并在基站发射功率约束和IRS相位偏移单位模约束下,构建了KGR最大化问题。为求解该问题,提出了一种鲁棒优化算法,该算法结合了交替优化(Alternating Optimization,AO)、逐次凸逼近(Successive Convex Approximation,SCA)、半定松弛(Semi-Definite Relaxation,SDR)和惩罚方法,迭代优化发射波束成形和IRS相移。仿真结果表明,所提鲁棒算法能够有效对抗硬件损伤、提升KGR。

, correspAuthors=杨少川, authorNote=null, correspAuthorsNote=
杨少川 男,(1989—),博士,讲师。主要研究方向:IRS辅助无线通信、物理层安全、能效通信等。
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王毅 男,(1984—),博士,副教授。主要研究方向:大规模MIMO、能效通信、无人机辅助通信、IRS辅助无线通信、物理层安全等。

赵飞 男,(1985—),博士,讲师。主要研究方向:IRS辅助无线通信、NOMA、能效通信等。

冀保峰 男,(1985—),博士,教授,博士生导师。主要研究方向:智能系统、移动通信、通感算控一体化。

楚征 男,(1986—),博士,助理教授。主要研究方向:移动通信、通感一体化、IRS辅助无线通信、物理层安全、SWIPT等。

李春国 男,(1983—),博士,教授,博士生导师。主要研究方向:6G蜂窝通信、网络空间安全、人工智能、计算机视觉等。

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王毅 男,(1984—),博士,副教授。主要研究方向:大规模MIMO、能效通信、无人机辅助通信、IRS辅助无线通信、物理层安全等。

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王毅 男,(1984—),博士,副教授。主要研究方向:大规模MIMO、能效通信、无人机辅助通信、IRS辅助无线通信、物理层安全等。

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2.Henan Key Laboratory of General Aviation Technology, Zhengzhou 450046 China
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3.航空航天电子信息技术河南省协同创新中心,河南 郑州 450046, bio={"content":"

赵飞 男,(1985—),博士,讲师。主要研究方向:IRS辅助无线通信、NOMA、能效通信等。

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赵飞 男,(1985—),博士,讲师。主要研究方向:IRS辅助无线通信、NOMA、能效通信等。

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冀保峰 男,(1985—),博士,教授,博士生导师。主要研究方向:智能系统、移动通信、通感算控一体化。

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冀保峰 男,(1985—),博士,教授,博士生导师。主要研究方向:智能系统、移动通信、通感算控一体化。

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楚征 男,(1986—),博士,助理教授。主要研究方向:移动通信、通感一体化、IRS辅助无线通信、物理层安全、SWIPT等。

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楚征 男,(1986—),博士,助理教授。主要研究方向:移动通信、通感一体化、IRS辅助无线通信、物理层安全、SWIPT等。

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李春国 男,(1983—),博士,教授,博士生导师。主要研究方向:6G蜂窝通信、网络空间安全、人工智能、计算机视觉等。

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李春国 男,(1983—),博士,教授,博士生导师。主要研究方向:6G蜂窝通信、网络空间安全、人工智能、计算机视觉等。

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journalId=1251234473337991274, articleId=1251893505136738722, language=EN, label=Fig. 2, caption=Convergence behavior of the proposed robust algorithm, figureFileSmall=8q+IZpxU63gd4mLtA1P/Dw==, figureFileBig=sM/uzhT/QHP4c6e9bTosxg==, tableContent=null), ArticleFig(id=1251895549465670509, tenantId=1146029695717560320, journalId=1251234473337991274, articleId=1251893505136738722, language=CN, label=图2, caption=所提鲁棒算法的收敛性, figureFileSmall=8q+IZpxU63gd4mLtA1P/Dw==, figureFileBig=sM/uzhT/QHP4c6e9bTosxg==, tableContent=null), ArticleFig(id=1251895549545362289, tenantId=1146029695717560320, journalId=1251234473337991274, articleId=1251893505136738722, language=EN, label=Fig. 3, caption=Relationship between KGR and the maximum transmit power of the base station, figureFileSmall=8WFESGS0t1LfUMn0f0legw==, figureFileBig=VupCiPFEwzaVMUPNyXIQ9w==, tableContent=null), ArticleFig(id=1251895549646025589, tenantId=1146029695717560320, journalId=1251234473337991274, articleId=1251893505136738722, 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cost, figureFileSmall=dcvDECT0huI2n9iH6qn9Eg==, figureFileBig=ztGE74x8u1eRv8FPanza0g==, tableContent=null), ArticleFig(id=1251895549964792703, tenantId=1146029695717560320, journalId=1251234473337991274, articleId=1251893505136738722, language=CN, label=图5, caption=硬件成本固定时KGR与硬件损伤程度的关系, figureFileSmall=dcvDECT0huI2n9iH6qn9Eg==, figureFileBig=ztGE74x8u1eRv8FPanza0g==, tableContent=null), ArticleFig(id=1251895551541851011, tenantId=1146029695717560320, journalId=1251234473337991274, articleId=1251893505136738722, language=EN, label=Fig. 6, caption=Relationship between KGR and the Rician factor, figureFileSmall=OKISUd6jwUkFZeRAezj6JA==, figureFileBig=AdEKSmoyDXD1yeyLq58+HA==, tableContent=null), ArticleFig(id=1251895551608959877, tenantId=1146029695717560320, journalId=1251234473337991274, articleId=1251893505136738722, language=CN, label=图6, caption=KGR与莱斯因子的关系, figureFileSmall=OKISUd6jwUkFZeRAezj6JA==, figureFileBig=AdEKSmoyDXD1yeyLq58+HA==, tableContent=null), ArticleFig(id=1251895551697040265, tenantId=1146029695717560320, journalId=1251234473337991274, articleId=1251893505136738722, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
参数初始化:设置初始值{Wt=wtwtHQt=},t=i= 0;
1:外循环t:
2:给定{WtQt},通过求解式(28)更新Wt+1
3:设置=Qt和惩罚因子κ
4:内循环i
5:给定{Wt+1},通过求解式(37)获得
6:更新κ=τκi=i+1;
7:直到ε2,退出内循环;
8:更新Qt+1=Qit=t+1计算
9:直到ε1,停止外循环;
10:利用特征值分解由W*Q*得到w*θ*
), ArticleFig(id=1251895551776732042, tenantId=1146029695717560320, journalId=1251234473337991274, articleId=1251893505136738722, language=CN, label=算法1, caption=

求解式(19)的KGR最大化鲁棒联合优化算法

, figureFileSmall=null, figureFileBig=null, tableContent=
参数初始化:设置初始值{Wt=wtwtHQt=},t=i= 0;
1:外循环t:
2:给定{WtQt},通过求解式(28)更新Wt+1
3:设置=Qt和惩罚因子κ
4:内循环i
5:给定{Wt+1},通过求解式(37)获得
6:更新κ=τκi=i+1;
7:直到ε2,退出内循环;
8:更新Qt+1=Qit=t+1计算
9:直到ε1,停止外循环;
10:利用特征值分解由W*Q*得到w*θ*
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硬件损伤情况下IRS辅助密钥生成系统鲁棒波束赋形设计
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王毅 1, 2, 3 , 杨少川 1, 2, 3, * , 赵飞 1, 2, 3 , 冀保峰 4 , 楚征 5 , 李春国 6
无线电通信技术 | 专题:6G与物联网技术 2025,51(5): 911-918
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无线电通信技术 | 专题:6G与物联网技术 2025, 51(5): 911-918
硬件损伤情况下IRS辅助密钥生成系统鲁棒波束赋形设计
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王毅1, 2, 3, 杨少川1, 2, 3, *, 赵飞1, 2, 3, 冀保峰4, 楚征5, 李春国6
作者信息
  • 1.郑州航空工业管理学院 电子信息学院,河南 郑州 450046
  • 2.河南省通用航空技术重点实验室,河南 郑州 450046
  • 3.航空航天电子信息技术河南省协同创新中心,河南 郑州 450046
  • 4.河南科技大学 信息工程学院,河南 洛阳 471023
  • 5.宁波诺丁汉大学 电器与电子工程学院,浙江 宁波 315100
  • 6.东南大学 信息科学与工程学院,江苏 南京 210096
  • 王毅 男,(1984—),博士,副教授。主要研究方向:大规模MIMO、能效通信、无人机辅助通信、IRS辅助无线通信、物理层安全等。

    赵飞 男,(1985—),博士,讲师。主要研究方向:IRS辅助无线通信、NOMA、能效通信等。

    冀保峰 男,(1985—),博士,教授,博士生导师。主要研究方向:智能系统、移动通信、通感算控一体化。

    楚征 男,(1986—),博士,助理教授。主要研究方向:移动通信、通感一体化、IRS辅助无线通信、物理层安全、SWIPT等。

    李春国 男,(1983—),博士,教授,博士生导师。主要研究方向:6G蜂窝通信、网络空间安全、人工智能、计算机视觉等。

通讯作者:

杨少川 男,(1989—),博士,讲师。主要研究方向:IRS辅助无线通信、物理层安全、能效通信等。
Joint Robust Beamforming Design for IRS-assisted Physical Layer Key Generation Under Hardware Impairments
Yi WANG1, 2, 3, Shaochuan YANG1, 2, 3, *, Fei ZHAO1, 2, 3, Baofeng JI4, Zheng CHU5, Chunguo LI6
Affiliations
  • 1.School of Electronics and Information, Zhengzhou University of Aeronautics, Zhengzhou 450046, China
  • 2.Henan Key Laboratory of General Aviation Technology, Zhengzhou 450046 China
  • 3.Collaborative Innovation Center of Aeronautics and Astronautics Electronic Information Technology Zhengzhou 450046, China
  • 4.College of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China
  • 5.Department of Electrical and Electronic Engineering, University of Nottingham Ningbo China, Ningbo 315100, China
  • 6.School of Information Science and Engineering, Southeast University, Nanjing 210096, China
出版时间: 2025-09-18 doi: 10.3969/j.issn.1003-3114.2025.05.004
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研究了存在残余收发信机硬件损伤(Transceiver Hardware Impairments,THI)情况下的智能反射面(Intelligent Reflecting Surface,IRS)辅助的物理层密钥生成(Physical-layer Key Generation,PKG)系统,推导了密钥生成速率(Key Generation Rate,KGR)的闭合表达式,并在基站发射功率约束和IRS相位偏移单位模约束下,构建了KGR最大化问题。为求解该问题,提出了一种鲁棒优化算法,该算法结合了交替优化(Alternating Optimization,AO)、逐次凸逼近(Successive Convex Approximation,SCA)、半定松弛(Semi-Definite Relaxation,SDR)和惩罚方法,迭代优化发射波束成形和IRS相移。仿真结果表明,所提鲁棒算法能够有效对抗硬件损伤、提升KGR。

硬件损伤  /  密钥生成  /  智能反射面  /  交替优化

This paper investigates an Intelligent Reflecting Surface (IRS)-assisted Physical-layer Key Generation (PKG) system under residual Transceiver Hardware Impairments(THI). A closed-form expression for the Key Generation Rate(KGR) is derived, and a KGR maximization problem is formulated under the base station transmit power constraint and the unit-modulus constraint on the IRS phase shifts. To solve this problem, a robust optimization algorithm is proposed, which integrates Alternating Optimization(AO), Successive Convex Approximation (SCA), Semi-Definite Relaxation(SDR), and penalty methods to iteratively optimize the transmit beamforming and IRS phase shifts. Numerical simulation results demonstrate that the proposed robust algorithm can effectively mitigate the impact of hardware impairments and improve the KGR.

hardware impairments  /  key generation  /  IRS  /  AO
王毅, 杨少川, 赵飞, 冀保峰, 楚征, 李春国. 硬件损伤情况下IRS辅助密钥生成系统鲁棒波束赋形设计. 无线电通信技术, 2025 , 51 (5) : 911 -918 . DOI: 10.3969/j.issn.1003-3114.2025.05.004
Yi WANG, Shaochuan YANG, Fei ZHAO, Baofeng JI, Zheng CHU, Chunguo LI. Joint Robust Beamforming Design for IRS-assisted Physical Layer Key Generation Under Hardware Impairments[J]. Radio Communications Technology, 2025 , 51 (5) : 911 -918 . DOI: 10.3969/j.issn.1003-3114.2025.05.004
现今,无线通信网络已成为人们日常生活密不可分的重要组成部分,其安全问题也受到广泛关注。由于无线信道的开放特性,通信系统极易遭受各种攻击,因此无线空口安全作为保障无线通信系统安全性的关键环节之一,具有重要的研究意义[1]。尤其值得注意的是,未来6G网络将提供全域覆盖、高密度连接、低时延高可靠通信等服务,展现出“泛在链接、多域融合”的特征,而现有的高层加密方案普遍存在计算复杂度高、密钥管理繁琐等问题[2]。作为一种轻量级加密技术,PKG利用无线信道固有的随机性与互易性等特性,在合法用户之间建立对称密钥,可以作为上层加密机制的有效补充[3]。PKG无需进行传统的密钥分发与管理,且借助无线信道的空间多样性,只要窃听者位置与合法用户间距超过半个波长,就无法窃取与密钥相关的任何信息[4]。此外,PKG具有实时更新、动态变化的特点,有望实现“一次一密”的完美加密机制,从而有效抵御量子计算技术所带来的安全挑战。
为了实现“一次一密”的安全通信愿景,KGR必须与通信速率相匹配。然而,KGR受限于无线信道的时变性,特别是在传感器网络、智能家居网络等准静态环境中,由于无线信道在较长时间内保持相对稳定,连续生成的密钥之间存在较强相关性,导致KGR降低并引发安全性问题。值得庆幸的是,IRS为提升密钥生成性能提供了全新的解决思路。IRS由大量近乎无源的反射单元构成,每个单元能够独立调整反射信号的幅度、相位及极化方向等参数,同时具备低成本、易部署、兼容性强等优势,在无线携能通信、隐蔽通信和通感一体化等通信场景中有非常广泛的应用[5-8]。通过随机改变IRS单元的相位偏移,可以有效增加复合信道的熵值,从而提升KGR[9]。进一步,通过对IRS单元的优化设计,不仅可以增强合法通信双方信道观测值的相关性、减少信息泄露,还能显著提升密钥生成性能。现有研究针对不同应用场景提出了多种IRS辅助密钥生成的方法,文献[10]通过调整IRS单元开关状态提升KGR,推导了IRS辅助下的KGR表达式,并通过仿真验证了所提鲁棒算法相较于随机切换策略具有更优的性能表现。文献[11]面向多用户单输入单输出(Single Input Single Output,SISO)网络,结合SCA与SDR技术,提出了一种IRS相位偏移优化方法。文献[12]进一步扩展至多输入单输出(Multiple Input Single Output,MISO)系统,分析了IRS单元间空间相关性对密钥生成性能的不利影响。文献[13]则研究了IRS辅助下的多小区PKG系统,分别采用拉格朗日对偶方法与投影梯度上升算法,联合优化基站波束赋形矩阵与IRS相位偏移,以最大化加权和密钥速率。
在实际通信系统中,由于相位噪声、I/Q失衡、频率偏移及功放非线性增益等因素引起的THI将对通信性能产生严重影响[14]。尽管硬件校准技术在一定程度上能够削弱硬件损伤带来的影响,但仍存在残余噪声。而且此类噪声的功率与有用信号功率成正比,无法通过简单地提高发射功率来抵消。文献[15]首次分析了THI对PKG系统性能的影响,推导出了THI条件下SISO系统的KGR闭合表达式,揭示了硬件损伤噪声会导致KGR下降的现象。文献[16]则研究了IRS辅助MISO密钥生成系统在硬件损伤条件下的性能,提出了一种鲁棒波束赋形算法,通过联合优化基站波束赋形向量与IRS相位偏移向量以提升KGR;但其假设硬件损伤噪声在所有天线上均匀分布,该假设较为理想化,未能充分反映实际硬件环境中硬件损伤噪声功率与天线发射/接收功率成正比的特性。
针对上述问题,本文引入了更加贴近实际的硬件损伤建模方法,假设硬件损伤噪声功率与各天线发射/接收功率成正比,在此基础上设计了更为精细的鲁棒波束赋形算法,以进一步提升硬件损伤条件下的KGR。
本文的主要贡献如下:①推导了在THI条件下IRS辅助PKG系统的KGR闭式表达式,并在此基础上提出了在基站发射功率和IRS单位模相位偏移约束下的KGR最大化问题。②提出了一种AO算法,通过结合SCA、SDR和惩罚方法,迭代优化发射波束成形和IRS相位偏移,解决了高度耦合的非凸优化问题。③通过仿真验证了所提波束成形方案的优越性,结果表明该方案在硬件损伤条件下比传统的非鲁棒方案更具鲁棒性,同时揭示了IRS在提高KGR方面的优势以及硬件损伤对KGR的影响。
本文的研究为IRS辅助的PKG系统在硬件损伤条件下的实际应用提供了理论支持和算法指导,为未来无线通信系统的安全性设计提供了新的思路。
IRS辅助的密钥生成系统如图1所示,该系统包含一个基站、一个IRS、一个合法用户Bob和一个窃听者Eve。基站和IRS分别装配N个天线和M个反射单元,Bob和Eve均为单天线节点。为了保证上下行信道的互易性,假设基站和Bob之间在时分双工(Time Division Duplexing,TDD)模式下利用无线信道生成密钥。一个智能控制器被用于协调基站的波束赋形和IRS的相移以提高KGR。基站与IRS、基站与Bob/Eve、IRS与Bob/Eve间的基带等效信道因子分别表示为:FN×M ; hdN×1gdN×1hrM×1grM×1
本文涉及的IRS辅助密钥生成方案包含以下3个步骤:
①参数配置:Bob发送信道探测信号,基站估计信道的统计信道状态信息并利用后文提出的鲁棒算法设计基站的发送波束赋形向量和IRS的相移向量。
②信道探测:基站和Bob互发公开导频并进行信道估计。假设合法通信双方存在硬件损伤。为了分析方便,将基站和Bob的发送天线与接收天线的残余硬件损伤程度分别用1-和1-表示。其中,v∈{ab},角标ab分别表示基站和Bob。基站先发送下行导频,则Bob和Eve的接收信号分别为[17]:
式中:Θ=diag(,…,,…,),φm∈[0,2π),∀m表示IRS的相移矩阵,wN×1sa(|sa|2=1)分别表示发送波束赋形向量和基站发送的导频信号,分别表示由Bob的接收机和基站发射机的残余硬件损伤产生的干扰噪声。根据文献[18],表示方差与有用信号功率成正比的0均值复高斯随机向量,即CN(0,(1-wwH)),其中{A}表示以矩阵A的对角元素构成的对角矩阵。nv表示Bob和Eve端的高斯白噪声(Adaptive White Gaussian Noise,AWGN),即nvCN(0,),v∈{be}。Bob和Eve利用最小二乘(Least Square,LS)法对复合信道进行估计,获得信道估计值hbhe:
式中:zv=nvconjsa),v∈{be}。随后Bob发送上行导频,基站的接收信号为:
式中:sb,|sb|2=1表示Bob发送的公开导频。同样地,假设CN(0,(1-)),CN(0,(1-hd+FΘhr))。naN×1为基站的接收噪声,且naCN(0,IN)。经过LS信道估计后,基站获得复合信道的估计值ha:
为了得到共享随机源,基站需要将hawH相乘得到:
式中:za=wHnaconjsb)。为了分析方便,假设收发双方的噪声方差相等,即==σ2。基站和Bob获得的复合信道特征信息中含有共享随机项、残余硬件损伤噪声和AWGN,因此可以当作共享随机源提取密钥。
③密钥生成:在信道探测之后,基站和Bob首先将收集到的信道特征信息hahb量化为原始比特序列;再通过信息协商来消除不一致的比特;最后,基站和Bob利用隐私放大技术降低信息协商过程中的信息泄露,并提高密钥的随机性。由于这一步骤与现有的密钥生成方案类似[41113],因此本文只关注前2个步骤。
本文推导硬件损伤存在时KGR的闭合表达式,首先,定义wHh=wHhd+wH,其中H=Fdiag(hr),θ=[,…,]T。则可以分别改写为:
然后,推导出的协方差矩阵为:
式中:E={hd}+(θTIN{vecH)·(vecH))H}(conjθ)⊗IN),等式(a)用到了式vecAC)=(CTIvecA)。考虑到不同设备残留硬件损伤产生的干扰噪声相互独立,可以推导出信道协方差如下:
其中:
根据文献[12],KGR为基站与Bob提取的信道特征信息下的条件互信息:
式中:等式(b)成立是由于当Eve距离合法通信双方超过半个波长时,窃听信道与合法信道不相关[19]。协方差矩阵由下式给出:
将式(11)和式(12)代入式(16)可以得到det()的表达式如下:
式中:Ec=Ea+Eb。将式(11)代入式(15)可推导出KGR的闭合表达式如下:
提出一种联合波束赋形算法,通过优化基站发送波束赋形和IRS相移,以最大化系统的KGR。建立KGR最大化问题如下:
式中:Pt表示基站的最大发送功率,[θ]m表示θ的第m个元素。约束条件C1和C2分别表示发送功率约束与IRS相移单位模值约束。式(19)的目标函数是非凹的,优化变量深度耦合且约束条件C2是非凸的,因此难以直接求解。为此,本文利用AO技术将其解耦为2个子问题,分别交替优化wθ
在任意给定IRS相移向量θ的情况下优化基站发送波束赋形向量w。定义WwwH满足W≻=0和RankW)=1。考虑到wHEw=tr(EW),因此对于给定θ,式(19)可被改写为关于W的优化问题:
式中:fW)≜tr(EaWtr(EWEHW)+σ2tr(EcW)+σ4。式(20)的目标函数仍是非凸的且含有W的高次方。利用SCA方法将目标函数线性化,引入松弛变量αβ并将式(20)改写:
式中:
为分析方便,按照以下形式定义Wαβ)和qW):
利用一阶泰勒展开公式分别推导出Wαβ)和qW)的线性估计值:
式中:{Wtαtβt}表示第t次迭代中的一个可行点。(Wtαtβt)在{Wtαtβt}的梯度由下式给出:
接下来利用SDR将非凸的秩一约束C3直接省略,得到关于W的优化问题如下:
式(28)是一个半正定松弛问题,可以利用优化问题求解器CVX直接求解。最优发送波束赋形向量w*可以通过特征根分解W*得到。
在任意给定发送波束赋形向量w的情况下设计IRS相移向量θ。将Rh改写为Rh=,定义=(θT,1)T,利用bbHa=aaHb结合式(3)和式(7)可以推导出:
式中:=
将式(29)和式(30)与式(16)和式(15)相结合,可以推导出:
式中:。定义满足Q≻=0。与前文类似,将关于Q的优化问题表示为:
式中:
约束条件C8表示Q的所有对角线元素均等于1,对应于IRS相移的单位模值约束。式(33)的目标函数和约束条件C10均是非凸的,导致难以直接求解。利用罚函数法处理秩一约束C10。将式(33)改写为[19]:
式中:‖Q*=表示核范数,其中δi为矩阵Q的第i大的奇异值。‖Q2=δ1为谱范数,κ>0为惩罚因子。对于任意的半正定矩阵Q,不等式‖Q*-‖Q2≥0均成立,当且仅当矩阵Q的秩为1时,等号成立。因此,惩罚项可以保证式(35)的最优解满足秩一约束C10。惩罚项属于凸差形式,可以利用一阶泰勒展开公式推导出‖Q2的凸下界:
式中:λQmax表示矩阵Q的最大特征根对应的特征向量,Qt为第t次迭代中的一个固定值。式(35)可以改写为:
式(37)的目标函数可以利用前文提出的SCA方法线性化,再利用CVX求解出最优解Q*。最优的IRS相移向量θ*可以通过Q*的特征值分解得到。所提KGR最大化算法的详细步骤总结在算法1中。
所提KGR最大化鲁棒算法的计算复杂度主要来自于步骤2和步骤5,涉及2个SDP问题。如果利用内点法求解,相应的计算复杂度分别为ON3.5)和O((M+ 1)3.5)。因此,所提鲁棒算法的计算复杂度可以表示为OTAON3.5+TPMM+ 1)3.5)),其中TAOTPM分别表示AO和罚函数法的迭代次数。
通过Matlab仿真验证所提鲁棒算法的性能,并分析硬件损伤对KGR的影响。在仿真设置中,基站、IRS和Bob的坐标分别为(0,0,20)m、(0,20,20)m和(40,0,1.5)m。参考距离1 m处的路径损耗设定为-30 dB。直射链路(基站至Bob)的路径损耗指数设置为4,IRS相关链路(基站至IRS和IRS至用户)的路径损耗指数设置为2.2。小尺度衰落方面,直射链路建模为瑞利分布,IRS相关链路建模为莱斯分布,莱斯因子为3 dB。除非特别说明,其他仿真参数设置为:N=M=10,Pt=10 dBm,=-90 dBm,=0.95,v∈{ab}。
为评估所提鲁棒算法的性能,采用以下对比方案:①理想情况。不存在硬件损伤的理想情况。②非鲁棒算法。存在硬件损伤,但是设计基站发送波束赋形和IRS相移时不考虑硬件损伤。③鲁棒随机相移。存在硬件损伤,利用所提鲁棒算法设计基站发送波束赋形向量,随机生成IRS相移向量。④鲁棒无IRS。存在硬件损伤,并且没有IRS辅助,利用所提鲁棒算法设计基站发送波束赋形向量。
图2展示了不同基站天线数和IRS单元数配置下所提鲁棒算法的收敛性能。可以看出,所有配置下KGR均随着交替优化迭代次数的增加不断升高,而且能够很快收敛,说明所提鲁棒算法具有良好的收敛特性。同时,基站天线数和IRS单元数量越多,收敛所需的迭代次数就越多,这是由于更多的优化变量导致收敛速度变慢。
图3展示了基站最大发射功率与KGR之间的关系。可以看出,所提鲁棒算法的性能优于非鲁棒方案,表明所提鲁棒算法在抵抗硬件损伤噪声方面的良好性能。同时,与未使用IRS的情形相比,引入IRS的方案表现更佳,这是因为在直射路径之外,IRS增加了额外的反射路径,从而带来了更多优化自由度。随机相移方案的KGR远小于所提鲁棒算法的KGR,说明通过合理设计IRS的反射相移,能够有效提高密钥生成性能,凸显了IRS在增强KGR方面的作用。此外,随着基站发射功率的增加,理想条件与非理想条件下系统性能差异逐渐扩大,原因在于残余硬件损伤引入的干扰噪声功率会随信号功率同步增长,因此在高功率场景下硬件损伤的负面影响更加突出。
图4给出了KGR随IRS反射单元数量变化的趋势。可以看出,在存在硬件损伤时,所提鲁棒算法表现出最优性能。除“鲁棒无IRS”的情形外,其余各方案的KGR随着IRS反射单元数量的增加而上升,这是因为反射单元数量越多,系统可调控的自由度越大,进而增强了对信道的优化能力,提高了整体性能。
采用高质量的收发信机可以降低硬件损伤、提升系统性能,但同时会增加硬件成本。因此,在给定硬件成本的情况下,在收发信机链路中设计最优的硬件损伤组合以实现性能最大化具有重要的现实意义[15]图5展示了硬件成本固定时KGR与硬件损伤程度的关系,假设ab=ηba,当硬件成本固定时,可以认为ηab+ηba=1。可以看出,当ηabηba相等时KGR出现最大值,即当上行链路与下行链路的硬件损伤程度相同时,系统性能最好,当上行链路的硬件损伤程度大于或小于下行链路的硬件损伤程度时,都会造成系统性能下降。
图6展示了KGR与IRS相关信道的莱斯因子之间的关系。可以看出,除了“无IRS”的情形外,其余方案的KGR都会随莱斯因子的增加而下降。这是因为只有随机的非视距分量才能用于生成密钥,而莱斯因子越大,非视距分量的功率越低,导致KGR变小。
本文针对IRS辅助的PKG系统,提出了一种对抗残余硬件损伤的鲁棒波束赋形算法。推导出了KGR的闭合表达式,在基站最大发送功率约束与IRS单位模值约束下构造了KGR最大化问题。利用SCA技术、泰勒展开公式、SDR和罚函数法设计了一种基于AO的鲁棒波束赋形算法用以提升KGR,仿真结果验证了所提鲁棒算法的有效性。未来的研究可以考虑IRS的非理想硬件特性,包括幅值与相位耦合、反射单元互耦和相位噪声等对密钥生成性能的影响以及相应的鲁棒方案设计。
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2025年第51卷第5期
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doi: 10.3969/j.issn.1003-3114.2025.05.004
  • 接收时间:2025-04-27
  • 首发时间:2026-04-17
  • 出版时间:2025-09-18
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  • 收稿日期:2025-04-27
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    1.郑州航空工业管理学院 电子信息学院,河南 郑州 450046
    2.河南省通用航空技术重点实验室,河南 郑州 450046
    3.航空航天电子信息技术河南省协同创新中心,河南 郑州 450046
    4.河南科技大学 信息工程学院,河南 洛阳 471023
    5.宁波诺丁汉大学 电器与电子工程学院,浙江 宁波 315100
    6.东南大学 信息科学与工程学院,江苏 南京 210096

通讯作者:

杨少川 男,(1989—),博士,讲师。主要研究方向:IRS辅助无线通信、物理层安全、能效通信等。
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
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