智能反射面辅助的多入单出共生无线电鲁棒安全资源分配算法

吴翠先; 周春宇; 徐勇军; 陈前斌

doi:10.11999/JEIT230426

智能反射面辅助的多入单出共生无线电鲁棒安全资源分配算法

doi: 10.11999/JEIT230426

吴翠先^{1, 2},
周春宇^{1, 2},
徐勇军^{1, 3, ,},
陈前斌¹

1.
重庆邮电大学通信与信息工程学院重庆 400065
2.
数智化通信新技术应用研究中心重庆 400065
3.
移动通信技术重庆市重点实验室重庆 400065

基金项目: 国家自然科学基金(62271094)，重庆市自然科学基金创新发展联合基金(CSTB2022NSCQ-LZX0009)，重庆市教委科学技术研究项目(KJZD-K202200601)

详细信息

作者简介:
吴翠先：女，正高级工程师，硕士生导师，研究方向为共生无线电、资源分配等

周春宇：女，硕士生，研究方向为共生无线电、智能反射面

徐勇军：男，副教授，博士生导师，研究方向为共生无线电、智能反射面、鲁棒资源分配等

陈前斌：男，教授，博士生导师，研究方向为共生无线电、智能反射面、资源分配等

通讯作者:
徐勇军　xuyj@cqupt.edu.cn

1¹⁾ 通过基站与RIS的协同控制使得共生接收机传输性能满足设计要求，从而实现共生环境下的优化。优化过程与传递首先从基站侧发起，基站将波束信息传输给接收端，接收端根据接收到的信息计算其当下的实际速率，判定是否满足优化问题的约束条件，并将判定结果反馈给基站侧，基站通过反馈的结果来调整波束。与此同时，FPGA控制器也接收到相应的反馈，通过对RIS的相移进行调整来辅助基站，直至参数稳定下来。
2²⁾ 本系统可以扩展到多用户系统，且本文侧重于克服CSI不确定性带来的影响并解决窃听用户引起的物理层安全问题，不同于文献[6,7]仅仅将RIS用来协助传输，本文中RIS不仅可以作为中继来反射入射信号，也可以传输其自身的信息给接收机，并且实现能量自供给。
3³⁾对于本系统物联网节点而言，获得CSI的代价并不高，且在实际系统中是可行的。因为CSI的获取可以通过以下两种方式获得：(1)网关通过信道估计获取^[17]，常见的方法包括最小均方误差估计、最大似然估计、导频辅助估计、能量检测估计；(2)通过信道的有限反馈来获取^[18]，其间接收端会周期性地评估信道的状态，并将这些信息以有限的比特数的形式发送回发送端，发送端收到反馈信息后，可以根据这些信息进行自适应调整。
中图分类号: TN929.5
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- 被引次数: 0
出版历程
- 收稿日期: 2023-05-17
- 修回日期: 2023-11-08
- 网络出版日期: 2023-11-14
- 刊出日期: 2024-04-24

Robust Secure Resource Allocation Algorithm for Multiple Input Single Output Symbiotic Radio with Reconfigurable Intelligent Surface Assistance

WU Cuixian^{1, 2},
ZHOU Chunyu^{1, 2},
XU Yongjun^{1, 3
, ,},
CHEN Qianbin¹

1.
School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Research Center of New Digital Intelligent Telecommunication Technology Applications, Chongqing 400065, China
3.
Chongqing Key Laboratory of Mobile Communications Technology, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (62271094), The Key Fund of Natural Science Foundation of Chongqing (CSTB2022NSCQ-LZX0009), The Scientific and Technological Research Program of Chongqing Municipal Education Commission (KJZD-K202200601)

摘要

摘要: 针对信道不确定性影响、无线信息泄露和障碍物阻挡导致通信质量下降等问题，该文提出一种基于智能反射面(RIS)辅助的多输入单输出(MISO)共生无线电(SR)鲁棒安全资源分配算法。考虑主用户的安全速率约束、次用户的最小速率约束、RIS最小能量收集约束，基于有界信道不确定性，建立了一个联合主被动波束赋形优化的资源分配问题。利用半正定松弛、S-procedure和变量替换法将含参数摄动的非凸问题转化为确定性的凸优化问题，并提出一种基于半正定松弛的鲁棒资源分配算法。仿真结果表明，与现有算法相比，该文算法具有较好的收敛性和鲁棒性。
- 共生无线电 /
- 不完美信道状态信息 /
- 智能反射面 /
- 物理层安全
Abstract: To resolve the channel uncertainties, wireless information leakage and low communication quality caused by obstacles, a robust secure resource allocation algorithm for a Reconfigurable Intelligent Surface (RIS) aided Multiple Input Single Output (MISO) Symbiotic Radio (SR) network is proposed. Considering the constraint of the secure rate of the primary user, the constraint of the minimum rate of the secondary user and the constraint of the minimum harvested energy of the RIS, a resource allocation problem based on the bounded channel uncertainties is formulated by jointly optimizing the active beamforming vector and the passive beamforming vector. Then, the parameter perturbation included non-convex problem is transformed into a deterministic convex optimization problem via the semidefinite relaxation, S-procedure and the variable substitution methods, and a semidefinite relaxation based robust resource allocation algorithm is proposed. Numerical results verify that the proposed algorithm has better convergence and robustness compared with existing algorithms.
- Symbiotic Radio (SR) /
- Imperfect channel state information /
- Reconfigurable Intelligent Surface (RIS) /
- Physical-layer secrecy

HTML全文

图 1 系统模型

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图 2 迭代收敛图

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图 3 最小发射功率与信道不确定性$ {\delta _{\boldsymbol{v}}} $的关系

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图 4 最小发射功率与最小保空速率门限值$ R_{\text{p}}^{{\text{min}}} $的关系

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图 5 最小发射功率与信道不确定性$ {\delta _{\boldsymbol{F}}} $的关系

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图 6 最小发射功率与最小保密速率门限值$ R_{\text{p}}^{{\text{min}}} $的关系

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图 7 实际保密速率$ R_{\text{p}} $与信道不确定性$ {\delta _{\boldsymbol{F}}} $的关系

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算法1　基于半正定松弛的鲁棒资源分配算法
初始化系统参数：$ N,{\text{ }}L,{\text{ }}Q,{\text{ }}R_{\text{s}}^{{\text{min}}},{\text{ }}R_{\text{p}}^{{\text{min}}},{\text{ }}\rho ,{\text{ }}\eta ,{\text{ }}{\sigma ^2},{\text{ }}{\delta _{\boldsymbol{F}}},{\text{ }}{\delta _{\boldsymbol{H}}} $, 　${\delta _{\boldsymbol{G}}},{\text{ }}{\delta _{\boldsymbol{h}}},{\text{ }}{\delta _{\boldsymbol{v}}},{\text{ }}e,{\text{ }}{{\boldsymbol{\varPsi}} ^{(0)}},{{\boldsymbol{W}}^{(0)}} $；设置收敛精度$ \varepsilon \ge {\text{0}} $，最大迭代　次数$ {K_{\max }} $，初始化$ k = 0 $；
(1) While $ {\text{\|Tr(}}{{\boldsymbol{W}}^{(k + 1)}}{{) - {\mathrm{Tr}}(}}{{\boldsymbol{W}}^{(k)}}{\text{)\|}} \ge \varepsilon $或$ k \le {K_{\max }} $ do
(2) 给定$ {{\boldsymbol{\varPsi}} ^{(k)}} $，通过求解式(36)获得$ {{\boldsymbol{W}}^{(k + 1)}} $；
(3) 给定$ {{\boldsymbol{W}}^{(k + 1)}} $，通过求解式(38)获得$ {\tilde{\boldsymbol{ \varPsi }}^{(k + 1)}} $；
(4) $ {{\boldsymbol{W}}^{(k + 1)}} = {e^{(k + 1)}}{{\boldsymbol{W}}^{(k + 1)}} $, $ {{\boldsymbol{\varPsi}} ^{(k + 1)}} = \frac{{\text{1}}}{{{e^{(k + 1)}}}}{\tilde {\boldsymbol{\varPsi}} ^{(k + 1)}} $；
(5) 计算$ {\text{Tr(}}{{\boldsymbol{W}}^{(k + 1)}}) $；
(6) 设置迭代次数$ k = k + 1 $；
(7) End While

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