面向窃听用户的RIS-MISO系统鲁棒资源分配算法

徐勇军; 徐然; 周继华; 万杨亮; 黄崇文; 刘伯红

doi:10.11999/JEIT211537

面向窃听用户的RIS-MISO系统鲁棒资源分配算法

doi: 10.11999/JEIT211537

徐勇军^{1, 2},
徐然¹,
周继华^3, ,,
万杨亮⁴,
黄崇文⁵,
刘伯红⁶

1.
重庆邮电大学通信与信息工程学院重庆 400065
2.
重庆金美通信有限责任公司重庆 400030
3.
航天新通科技有限公司重庆 401332
4.
95696部队重庆 400030
5.
浙江大学信息与电子工程学院杭州 310027
6.
重庆邮电大学计算机科学与技术学院重庆 400065

基金项目: 国家自然科学基金(61601071, 62071078)，重庆市自然科学基金(cstc2019jcyj-xfkxX0002)

详细信息

作者简介:
徐勇军：男，1986年生，副教授，硕士生导师，研究方向为智能反射面、鲁棒资源分配等

徐然：男，1998年生，硕士生，研究方向为智能反射面、鲁棒资源分配等

周继华：男，1979年生，教授，博士生导师，研究方向为无线网络、资源分配等

万杨亮：男，1980年生，工程师，研究方向为无线网络、资源分配等

黄崇文：男，1986年生，教授，博士生导师，研究方向为智能超表面/全息智能MIMO通信等

刘伯红：男，1973年生，副教授，硕士生导师，研究方向为云计算、无线网络等

通讯作者:
周继华　jhzhou@ict.ac.cn

中图分类号: TN929.5
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- 被引次数: 6
出版历程
- 收稿日期: 2021-12-20
- 修回日期: 2022-06-02
- 录用日期: 2022-06-07
- 网络出版日期: 2022-06-12
- 刊出日期: 2022-07-25

Robust Resource Allocation Algorithm for RIS-Assisted MISO Systems with Eavesdroppers

1.
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Chongqing Jinmei Communication Co. LTD., Chongqing 400030, China
3.
Aerospace New Generation Communications Co., Ltd., Chongqing 401332, China
4.
95696 Troops, Chongqing 400030, China
5.
College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China
6.
School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (61601071, 62071078), The Natural Science Foundation of Chongqing (cstc2019jcyj-xfkxX0002)

摘要

摘要: 针对信道不确定性影响、用户信息泄露和能效提升等问题，该文提出一种基于不完美信道状态信息的可重构智能反射面(RIS)多输入单输出系统鲁棒资源分配算法。首先，考虑能量收集最小接收功率约束、合法用户最小保密速率约束、基站最大发射功率约束及RIS相移约束，基于有界信道不确定性，建立一个联合优化基站主动波束、能量波束、RIS相移矩阵的多变量耦合非线性资源分配问题。然后，利用Dinkelbach，S-procedure和交替优化方法，将原非凸问题转换成确定性凸优化问题，并提出一种基于连续凸近似的交替优化算法。仿真结果表明，与传统非鲁棒算法对比，所提算法具有较低的中断概率。
- 可重构智能反射面 /
- 不完美信道状态信息 /
- 能效最大化
Abstract: To resolve the problems of the effect of channel uncertainties, information leakage of users and energy-efficient improvement, a robust resource allocation algorithm for Reconfigurable Intelligent Surface (RIS)-assisted multiple-input single-output systems with eavesdroppers and imperfect channel state information is proposed. Firstly, considering the constraints of the minimum received power of energy-harvesting devices, the minimum secure rate of each legitimate user, the maximum transmit power of the base station and the phase-shift matrix of RIS, a multi-variable coupling and nonlinear resource optimization problem is formulated by jointly optimizing the information beamforming, energy beamforming and the phase shifts under bounded channel uncertainties. Then, the original non-convex problem is transformed into a deterministic convex one by using Dinkelbach’s method, S-Procedure and the alternating optimization method. An alternating optimization algorithm based on successive convex approximation is proposed to solve the problem. Simulation results show that the proposed algorithm has lower outage probabilities by comparing it with the traditional non-robust algorithms.
- Reconfigurable Intelligent Surface (RIS) /
- Imperfect channel state information /
- Energy-efficient maximization

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图 1 系统模型

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图 2 仿真场景

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图 3 系统能效收敛图

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图 4 系统能效与基站发射功率阈值之间的关系

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图 5 能量接收机收集的功率与接收机位置的关系

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图 6 系统总能效与基站发射天线数之间的关系

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图 7 系统总能效与用户保密速率门限之间的关系

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图 8 保密中断概率与窃听信道的最大估计误差之间的关系

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表 1 基于连续凸近似的交替优化算法

(1)初始化参数：相移向量 ${{\boldsymbol{q}}^{(0)}}$ ,波束矩阵 $\{ {\boldsymbol{W}}_k^{(0)}\} ,{\boldsymbol{V}}_r^{(0)}$ ,初始迭代次数 $i = 1$ ,最大迭代次数 ${i_{\max }}$ ,初始能效 ${\eta ^{(0)}} = 0$ ,收敛精度 $\varepsilon$ ;
(2)重复
(a)通过给定的相移 ${{\boldsymbol{q}}^{(i - 1)}}$ 和 ${\eta ^{(i - 1)}}$ 求解问题式(32)获得 $\{ {\boldsymbol{W}}_k^{(i)}\} ,{\boldsymbol{V}}_r^{(i)}$ ；
(b)更新 ${\boldsymbol{W}}_k^{(i + 1)}{\text{ = }}{\boldsymbol{W}}_k^{(i)}$ ， ${\boldsymbol{V}}_r^{(i + 1)}{\text{ = }}{\boldsymbol{V}}_r^{(i)}$ ，根据 ${{\boldsymbol{Q}}^{(i - 1)}}$ 求解问题式(45)获得 ${{\boldsymbol{Q}}^{(i)}}$ ，由 ${{\boldsymbol{Q}}^{(i)}} = {{\boldsymbol{\bar q}}^{(i)}}\;{({{\boldsymbol{\bar q}}^{(i)}})^{\text{H}}}$ 和 ${{\boldsymbol{q}}^{(i)}}\; = {[{{\boldsymbol{\bar q}}^{(i)}}]_{(\;1:N)}}$ 获得 ${{\boldsymbol{q}}^{(i)}}$ ；
(c)更新 ${{\boldsymbol{Q}}^{(i + 1)}}{\text{ = }}{{\boldsymbol{Q}}^{(i)}}$ ， ${\eta ^{(i + 1)}} = {\eta ^{(i)}}$ ， $i = i + 1$ ；　(3)直到 $\left\| {\dfrac{ { {\eta ^{(i + 1)} } - {\eta ^{(i)} } } }{ { {\eta ^{(i)} } } }} \right\| \le \varepsilon$ ，获得 $\{ {\boldsymbol{W}}_k^\} ,{\boldsymbol{V}}_r^,{{\boldsymbol{q}}^*}$ 。

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