基于硬件损伤的智能反射面辅助安全通信系统能效优化算法

高俊鹏; 周继华; 赵涛; 徐勇军; 赵瑞莉

doi:10.11999/JEIT210976

基于硬件损伤的智能反射面辅助安全通信系统能效优化算法

doi: 10.11999/JEIT210976

高俊鹏¹,
周继华^{1, 2, 3, ,},
赵涛^{2, 3},
徐勇军^{1, 3},
赵瑞莉¹

1.
重庆邮电大学通信与信息工程学院重庆 400065
2.
航天新通科技有限公司重庆 401332
3.
复杂环境通信重庆市重点实验室重庆 400030

基金项目: 国家自然科学基金(61601071, 62071078)，国家重点研发计划(2019YFC1511300)，重庆市自然科学基金 (cstc2019jcyj-xfkxX0002)

详细信息

作者简介:
高俊鹏：男，1988年生，博士生，研究方向为智能反射面、鲁棒资源分配等

周继华：男，1979年生，研究员，博士生导师，研究方向为智能反射面、无线通信等

赵涛：男，1983年生，研究员，硕士生导师，研究方向为智能反射面、移动网络等

徐勇军：男，1986年生，副教授，硕士生导师，研究方向为异构无线网络、智能反射面、鲁棒资源分配等

赵瑞莉：女，1990年生，博士生，研究方向为智能反射面、空天地一体化网络、空地融合车联网等

通讯作者:
周继华　jhzhou@ict.ac.cn

¹⁾本文优化合法用户的能效，而窃听者位置是在网络中随机分布的，并且潜藏在网路环境中，并不会与基站共享信息，所以基站不能获得窃听者的实际全部信息^[17,20]，此外，假设不考虑窃听者硬件损伤，即窃听者具有高质量的硬件，也是考虑优化合法用户最坏的情况^[20]。因此本文忽略窃听者硬件损伤。
中图分类号: TN929.5
计量
- 文章访问数: 882
- HTML全文浏览量: 513
- PDF下载量: 211
- 被引次数: 0
出版历程
- 收稿日期: 2021-09-14
- 修回日期: 2021-12-21
- 录用日期: 2021-12-28
- 网络出版日期: 2022-01-13
- 刊出日期: 2022-07-25

Energy-efficient Algorithm for Intelligent Reflecting Surface-aided Secure Communication Systems with Hardware Impairments

GAO Junpeng¹,
ZHOU Jihua^{1, 2, 3
, ,},
ZHAO Tao^{2, 3},
XU Yongjun^{1, 3},
ZHAO Ruili¹

1.
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Aerospace New Generation Communications Co. Ltd, Chongqing 401332, China
3.
Chongqing Key Laboratory of Complex Environment Communications, Chongqing 400030, China

Funds: The National Natural Science Foundation of China (61601071, 62071078), The National Key Research and Development Program (2019YFC1511300), The Natural Science Foundation of Chongqing (cstc2019jcyj-xfkxX0002)

摘要

摘要: 为了克服阴影衰落和障碍物阻挡的影响，智能反射面(IRS)已经成为一种提高无线通信系统能量效率(EE)和降低硬件成本的有效技术。然而，传统无线资源分配(RA)算法忽略了系统收发机硬件损伤(HIs)的影响，由于放大器非线性、相位噪声的影响使得接收信号失真，从而使得这类算法的系统性能下降。为解决该问题，通过考虑收发机的硬件损伤和网络窃听者的影响，该文研究基于硬件损伤的IRS辅助安全通信系统能效优化问题。首先，基于基站的最大发射功率约束和用户的最小安全速率约束，建立一个含硬件损伤的能效最大资源优化问题。其次，采用辅助变量替换、半正定松弛以及Dinkelbach等方法，将原非凸问题转化为凸问题进行求解。最后，数值仿真结果表明，该算法与传统资源分配算法相比，合法用户的平均中断概率降低了43.5%，该算法中系统的安全能效提高了8.3%，因此，该算法具有较好的抗硬件损伤性和安全性。
- 安全通信 /
- 智能反射面 /
- 能效最大化 /
- 硬件损伤
Abstract: To mitigate the effects of shadow fading and obstacle blocking, Intelligent Reflecting Surface (IRS) has become an effective technology to improve Energy Efficiency (EE) and reduce hardware cost of wireless communication systems. However, traditional radio Resource Allocation (RA) algorithms have ignored the impact of Hardware Impairments (HIs) of system’s transceivers. Since the distorted received signals are caused by the nonlinearity of amplifiers and the influence of phase noise. so that this type of algorithm can degrade system performance. To deal with this issue, Hardware Impairments of the transceiver and the influence of eavesdroppers is considered, and the problem of energy-saving optimization of hardware impairment in IRS-assisted secure communication systems is investigated. Firstly, an EE-based maximization resource optimization problem is formulated under the maximum transmit power constraint of the base station and the minimum secure rate constraints of users. Secondly, the original non-convex problem is transformed into a convex problem by using the auxiliary variable substitution, semidefinite relaxation and Dinkelbach’s method. Finally, simulation results show that the proposed algorithm is improved 8.3% in terms of security EE and is reduced by 43.5% in terms of the outage probability of legitimate users by comparing it with the traditional RA algorithms without HIs. Therefore, the proposed algorithm has better security and hardware damage resistance.
- Secure communication /
- Intelligent Reflecting Surface(IRS) /
- Energy Efficiency(EE) maximization /
- Hardware Impairments(HIs)
¹⁾本文优化合法用户的能效，而窃听者位置是在网络中随机分布的，并且潜藏在网路环境中，并不会与基站共享信息，所以基站不能获得窃听者的实际全部信息^[17,20]，此外，假设不考虑窃听者硬件损伤，即窃听者具有高质量的硬件，也是考虑优化合法用户最坏的情况^[20]。因此本文忽略窃听者硬件损伤。

HTML全文

¹⁾本文优化合法用户的能效，而窃听者位置是在网络中随机分布的，并且潜藏在网路环境中，并不会与基站共享信息，所以基站不能获得窃听者的实际全部信息^[17,20]，此外，假设不考虑窃听者硬件损伤，即窃听者具有高质量的硬件，也是考虑优化合法用户最坏的情况^[20]。因此本文忽略窃听者硬件损伤。

图 1 系统模型

下载: 全尺寸图片幻灯片

图 2 仿真安全通信场景

下载: 全尺寸图片幻灯片

图 3 系统能效收敛图

下载: 全尺寸图片幻灯片

图 4 不同算法下能效与最大发射功率的关系

下载: 全尺寸图片幻灯片

图 5 能效与最大发射功率在不同HIs因子以及算法下的关系

下载: 全尺寸图片幻灯片

图 6 能效与安全速率阈值在不同算法下的关系

下载: 全尺寸图片幻灯片

图 7 能效与智能反射面单元数量在不同算法下的关系

下载: 全尺寸图片幻灯片

图 8 中断概率与安全速率阈值在不同算法下的关系

下载: 全尺寸图片幻灯片

表 1 基于交替迭代的资源分配算法

初始化系统参数：初始化相移矩阵 ${{\boldsymbol{F}}^{(0)} }$ ,设置初始迭代次数 ${\rm{iter}} = 1$ ，最大迭代次数 ${{\rm{iter}}^{({\rm{max}})} }$ ，初始能效值 ${\lambda ^{(0)}}$ ，初始化松弛辅助变量 $\bar q_l^{(0)}$ , 　 $\bar u_e^{(0)}$ ，收敛精度 $\varepsilon = 1{0^{ - 4}}$ 。
步骤1　　for ${\rm{iter }}= 1,2, \cdots ,{{\rm{iter}}^{({\rm{max}})}}$ do
步骤2　　根据给定相移矩阵F，系统能效值 $\lambda$ 以及松弛辅助变量 $\overline q_l$ , $\overline u_e$ 。求解问题式(15)得到波束向量矩阵 ${\boldsymbol{W}}_l^{({\rm{iter}})}$ ，AN协方差矩阵　　　　　 ${{\boldsymbol{Z}}^{({\rm{iter}})}}$ ，以及松弛辅助变量 $p_l^{({\rm{iter}})},q_l^{({\rm{iter}})},u_e^{({\rm{iter}})},c_{e,l}^{({\rm{iter}})},c_{e,l}^{({\rm{iter}})}$ 。
步骤3　　更新松弛变量 $\overline q _l^{({\rm{iter}})} = q_l^{({\rm{iter}})}$ , $\overline u _e^{({\rm{iter}})} = u_e^{({\rm{iter}})}$ 。
步骤4　　　　对 ${\boldsymbol{W}}_l^{({\rm{iter}})}$ 进行特征值分解 ${\boldsymbol{W}}_l^{({\rm{iter}})} = {\boldsymbol{U\varLambda}} {{\boldsymbol{U}}^{\rm{H}}} \$，以获得次优解$ {\boldsymbol{w}}_l^{({\rm{iter}})} = {\boldsymbol{U}}{{\boldsymbol{\varLambda}} ^{(1/2)}}{\boldsymbol{r}} $,$ {\boldsymbol{r}} \sim {\rm{CN}}(0,{\boldsymbol{I}}) $。
步骤5　　根据波束向量 ${\boldsymbol{W}}_l^{({\rm{iter}})}$ , AN协方差矩阵 ${{\boldsymbol{Z}}^{({\rm{iter}})}}$ ，以及松弛辅助变量 $p_l^{({\rm{iter}})},\bar q_l^{({\rm{iter}})},\bar u_e^{({\rm{iter}})},c_{e,l}^{({\rm{iter}})},{F^{({\rm{iter}})}}$ 。求解系统能效值　　　　　　 ${\lambda ^{({\rm{iter} })} } = \displaystyle\sum\limits_l {\frac{ {p_l^{({\rm{iter} })} - q_l^{({\rm{iter} })} - (u_e^{({\rm{iter} })} - c_{e,l}^{({\rm{iter} })})} }{ {(\mu (B) + {P^c}){\rm{ln}}2} } }$ ，目标函数 $E_2^{({\rm{iter}})}$ 。
步骤6　　　　对 ${{\boldsymbol{F}}^{({\rm{iter}})}}$ 采用步骤4中的方法来求解 ${{\boldsymbol{f}}^{({\rm{iter}})}}$ 。
步骤7　　　　if $\frac{{\|E_1^{({\rm{iter}} + 1)} - E_1^{({\rm{iter}})}\|}}{{\|E_1^{({\rm{iter}})}\|}} \le \varepsilon$ and $\frac{ {\|E_2^{({\rm{iter}} + 1)} - E_2^{({\rm{iter}})}\|} }{ {\|E_2^{({\rm{iter}})}\|} } \le \varepsilon$ and $\frac{ {\|{\lambda ^{({\rm{iter} } + 1)} } - {\lambda ^{({\rm{iter} })} }\|} }{ {\|{\lambda ^{({\rm{iter} })} }\|} } \le\varepsilon$ 。
break
else
iter = iter + 1。
end
步骤8　　　　end
步骤9　　输出所需要的优化变量 ${\lambda ^},{{\boldsymbol{f}}^},{\boldsymbol{w}}_l^,{{\boldsymbol{Z}}^}$ 。

下载: 导出CSV

表 2 系统仿真参数

参数	值	参数	值
N	2	M	2
$\delta _l^2$ ^[8]	–74 dBm	${\delta ^2}$ ^[8]	–74 dBm
$\mu$ ^[15]	$1$	${P^c}$ ^[15]	10 dBm
${\xi _0}$	–30 dBm	${\delta ^2}$ ^[8]	–74 dBm
$\varepsilon$	10^–4	${P^{{\rm{max}}} }$	30.8 dBm

下载: 导出CSV

参考文献(21)

[1]	徐勇军, 刘子腱, 李国权, 等. 基于NOMA的无线携能D2D通信鲁棒能效优化算法[J]. 电子与信息学报, 2021, 43(5): 1289–1297. doi: 10.11999/JEIT200175 XU Yongjun, LIU Zijian, LI Guoquan, et al. Robust energy efficiency optimization algorithm for NOMA-based D2D communication with simultaneous wireless information and power transfer[J]. Journal of Electronics &Information Technology, 2021, 43(5): 1289–1297. doi: 10.11999/JEIT200175
[2]	XU Yongjun, GUI Guan, GACANIN H, et al. A survey on resource allocation for 5G heterogeneous networks: Current research, future trends, and challenges[J]. IEEE Communications Surveys & Tutorials, 2021, 23(2): 668–695. doi: 10.1109/COMST.2021.3059896
[3]	徐勇军, 谷博文, 杨洋, 等. 基于不完美CSI的D2D通信网络鲁棒能效资源分配算法[J]. 电子与信息学报, 2021, 43(8): 2189–2198. doi: 10.11999/JEIT200587 XU Yongjun, GU Bowen, YANG Yang, et al. Robust energy-efficient resource allocation algorithm in D2D communication networks with imperfect CSI[J]. Journal of Electronics &Information Technology, 2021, 43(8): 2189–2198. doi: 10.11999/JEIT200587
[4]	WU Qingqing and ZHANG Rui. Towards smart and reconfigurable environment: Intelligent reflecting surface aided wireless networks[J]. IEEE Communications Magazine, 2020, 58(1): 106–112. doi: 10.1109/MCOM.001.1900107
[5]	WU Qingqing and ZHANG Rui. Beamforming optimization for wireless network aided by intelligent reflecting surface with discrete phase shifts[J]. IEEE Transactions on Communications, 2020, 68(3): 1838–1851. doi: 10.1109/TCOMM.2019.2958916
[6]	WU Qingqing and ZHANG Rui. Intelligent reflecting surface enhanced wireless network via joint active and passive beamforming[J]. IEEE Transactions on Wireless Communications, 2019, 18(11): 5394–5409. doi: 10.1109/TWC.2019.2936025
[7]	WU Qingqing, ZHANG Shuowen, ZHENG Beixiong, et al. Intelligent reflecting surface-aided wireless communications: A tutorial[J]. IEEE Transactions on Communications, 2021, 69(5): 3313–3351. doi: 10.1109/TCOMM.2021.3051897
[8]	ZHOU Fuhui, CHU Zheng, SUN Haijian, et al. Artificial noise aided secure cognitive beamforming for cooperative MISO-NOMA using SWIPT[J]. IEEE Journal on Selected Areas in Communications, 2018, 36(4): 918–931. doi: 10.1109/JSAC.2018.2824622
[9]	GUI Miao, ZHANG Guangchi, and ZHANG Rui. Secure wireless communication via intelligent reflecting surface[J]. IEEE Wireless Communications Letters, 2019, 8(5): 1410–1414. doi: 10.1109/LWC.2019.2919685
[10]	FENG Biqian, WU Yongpeng, and ZHENG Mengfan. Secure transmission strategy for intelligent reflecting surface enhanced wireless system[C]. 11th International Conference On Wireless Communications and Signal Processing, Xi’an, China, 2019: 1–6.
[11]	ZHANG Jiayi, DAI Linglong, ZAHNG Xinlin, et al. Achievable rate of rician large-scale MIMO channels with transceiver hardware impairments[J]. IEEE Transactions on Vehicular Technology, 2016, 65(10): 8800–8806. doi: 10.1109/TVT.2015.2504428
[12]	XING Zhe, WANG Rui, WU Jun, et al. Achievable rate analysis and phase shift optimization on intelligent reflecting surface with hardware impairments[J]. IEEE Transactions on Wireless Communications, 2021, 20(9): 5514–5530. doi: 10.1109/TWC.2021.3068225
[13]	KHANSEFID A, Minn H, Zhan Qi, et al. Waveform parameter design and comparisons for millimeter-wave massive MIMO systems with RF distortions[C]. IEEE Globecom Workshops, Washington, USA, 2016: 1–6.
[14]	ZHANG Jiayi, XUE Xipeng, BJÖRNSON E, et al. Spectral efficiency of multipair massive MIMO two-way relaying with hardware impairments[J]. IEEE Wireless Communications Letters, 2018, 7(1): 14–17. doi: 10.1109/LWC.2017.2750162
[15]	ZHOU Shaoqing, XU Wei, WANG Kezhi, et al. Spectral and energy efficiency of IRS-assisted MISO communication with hardware impairments[J]. IEEE Wireless Communications Letters, 2020, 9(9): 1366–1369. doi: 10.1109/LWC.2020.2990431
[16]	SHEN Hong, XU Wei, GONG Shulei, et al. Beamforming optimization for IRS-aided communications with transceiver hardware impairments[J]. IEEE Transactions on Communications, 2021, 69(2): 1214–1227. doi: 10.1109/TCOMM.2020.3033575
[17]	PAPAZAFEIROPOULOS A, PAN Cunhua, ELBIR A, et al. Asymptotic analysis of max-min weighted SINR for IRS-assisted MISO systems with hardware impairments[J]. IEEE Wireless Communications Letters, To be published.
[18]	YU Xianghao, XU Dongfang, SUN Ying, et al. Robust and secure wireless communications via intelligent reflecting surfaces[J]. IEEE Journal on Selected Areas in Communications, 2020, 38(11): 2637–2652. doi: 10.1109/JSAC.2020.3007043
[19]	XU Yongjun, XIE Hao, and HU R Q. Max-min beamforming design for heterogeneous networks with hardware impairments[J]. IEEE Communications Letters, 2021, 25(4): 1328–1332. doi: 10.1109/LCOMM.2020.3044936
[20]	ZHOU Gui, PAN Cunhua, REN Hong, et al. Secure wireless communication in RIS-aided MISO system with hardware impairments[J]. IEEE Wireless Communications Letters, 2021, 10(6): 1309–1313. doi: 10.1109/LWC.2021.3064992
[21]	DINKELBACH W. On nonlinear fractional programming[J]. Management Science, 1967, 13(7): 492–498. doi: 10.1287/mnsc.13.7.492