基于硬件损伤的认知反向散射通信网络鲁棒安全资源分配算法

徐勇军; 姜思巧; 张海波; 王正强; 周继华

doi:10.11999/JEIT230117

基于硬件损伤的认知反向散射通信网络鲁棒安全资源分配算法

doi: 10.11999/JEIT230117

徐勇军^{1, 2, ,},
姜思巧^{1, 2},
张海波^{1, 2},
王正强^{1, 2},
周继华^{1, 3}

1.
重庆邮电大学通信与信息工程学院重庆 400065
2.
移动通信技术重庆市重点实验室重庆 400065
3.
航天新通科技有限公司重庆 401332

基金项目: 国家自然科学基金(62271094)，重庆市教委科学技术研究项目(KJZD-K202200601)，中国博士后科学基金(2022MD723725)，四川省区域创新合作项目(2022YFQ0017)

详细信息

作者简介:
徐勇军：男，副教授，博士生导师，研究方向为反向散射通信、鲁棒资源分配

姜思巧：女，硕士生，研究方向为反向散射、鲁棒资源分配

张海波：男，副教授，硕士生导师，研究方向为无线网络、资源分配等

王正强：男，副教授，研究方向为无线网络、资源分配

周继华：男，研究员，博士生导师，研究方向为无线网络、资源分配等

通讯作者:
徐勇军　xuyj@cqupt.edu.cn

中图分类号: TN929
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- 被引次数: 0
出版历程
- 收稿日期: 2023-03-01
- 修回日期: 2023-05-11
- 网络出版日期: 2023-05-18
- 刊出日期: 2024-02-10

Robust Secure Resource Allocation Algorithm for Cognitive Backscatter Communication with Hardware Impairment

XU Yongjun^{1, 2
, ,},
JIANG Siqiao^{1, 2},
ZHANG Haibo^{1, 2},
WANG Zhengqiang^{1, 2},
ZHOU Jihua^{1, 3}

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

Funds: The National Natural Science Foundation of China (62271094), The Scientific and Technological Research Program of Chongqing Municipal Education Commission (KJZD-K202200601), The China Postdoctoral Science Foundation (2022MD723725), The Sichuan Regional Innovation Cooperation Project (2022YFQ0017)

摘要

摘要: 为了提高反向散射通信网络频谱效率、传输鲁棒性及信息安全性，该文提出一种基于硬件损伤的认知反向散射通信网络鲁棒安全资源分配算法。首先，考虑认知反向散射用户的最小安全速率、传输时间、能量收集和反射系数等约束，基于有界信道不确定性和频谱感知误差模型，建立一个多变量耦合的吞吐量最大化非凸资源分配问题。其次，利用最坏准则、连续凸近似和交替优化方法，将原问题转换为凸优化问题，并提出一种基于迭代的鲁棒资源分配算法。仿真结果表明，与现有算法对比，所提算法具有较好的鲁棒性。
- 认知反向散射通信 /
- 鲁棒资源分配 /
- 硬件损伤 /
- 安全通信 /
- 频谱感知误差
Abstract: To improve spectral efficiency, transmission robustness, and information security of backscatter communication networks, a robust secure resource allocation algorithm is proposed for cognitive backscatter communication networks with hardware impairments. Firstly, considering the constraints of the minimum secure rate of each cognitive backscatter user, transmission time, energy harvesting, and reflection coefficients, a multivariable coupled resource allocation problem with throughput maximization is established under bounded channel uncertainties and spectrum sensing errors. Secondly, the original problem is transformed into a convex problem by using a worst-case approach, successive convex approximation, alternating optimization, and an iteration-based robust resource allocation algorithm is proposed to solve it. Simulation results show that the proposed algorithm has better robustness by comparing it with the existing algorithms.
- Cognitive backscatter communication /
- Robust resource allocation /
- Hardware impairments /
- Secure communication /
- Spectrum sensing errors

HTML全文

图 1 认知反向散射通信网络

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图 2 算法求解流程图

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图 3 算法收敛分析图

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图 4 系统保密吞吐量与$ {\delta _{\text{E}}} $的关系

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图 5 系统吞吐量与${P_0}$的关系

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图 6 系统保密吞吐量与$ r_k^{{\text{B,min}}} $的关系

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图 7 不同算法与$ I_{k,m}^{\max } $的关系

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图 8 信道不确定性对中断概率的影响

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表 1 频谱检测情况

授权频谱状态	授权频谱感知结果	条件感知概率
占用$ \left( O \right) $	占用$ \left( {\tilde O} \right) $	${\rho ^1} = \Pr \{ O\|\tilde O\} = \dfrac{ {\left( {1 - {q^{ {\text{md} } } }} \right){q^{\text{o} } } }}{ { {q^{ {\text{fa} } } }\left( {1 - {q^{\text{o} } } } \right) + \left( {1 - {q^{ {\text{md} } } }} \right){q^{\text{o} } } }}$
空闲$ \left( V \right) $	占用$ \left( {\tilde O} \right) $	${\rho ^2} = \Pr \{ V\|\tilde O\} = \dfrac{ { {q^{ {\text{fa} } } }\left( {1 - {q^{\text{o} } } } \right)} }{ { {q^{ {\text{fa} } } }\left( {1 - {q^{\text{o} } } } \right) + \left( {1 - {q^{ {\text{md} } } }} \right){q^{\text{o} } } }}$
占用$ \left( O \right) $	空闲$ \left( {\tilde V} \right) $	${\rho ^3} = \Pr \{ O\|\tilde V\} = \dfrac{ { {q^{ {\text{md} } } }{q^{\text{o} } } }}{ {\left( {1 - {q^{ {\text{fa} } } }} \right)\left( {1 - {q^{\text{o} } } } \right) + {q^{ {\text{md} } } }{q^{\text{o} } } }}$
空闲$ \left( V \right) $	空闲$ \left( {\tilde V} \right) $	${\rho ^4} = \Pr \{ V\|\tilde V\} = \dfrac{ {\left( {1 - {q^{ {\text{fa} } } }} \right)\left( {1 - {q^{\text{o} } } } \right)} }{ {\left( {1 - {q^{ {\text{fa} } } }} \right)\left( {1 - {q^{\text{o} } } } \right) + {q^{ {\text{md} } } }{q^{\text{o} } } }}$

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算法1 基于迭代的鲁棒资源分配算法
初始化系统参数：$ {P_0} $, $ M $, $ K $, $ f_m^{\text{P}} $, $ {f_{\text{P}}} $, $ {f_k} $, $ {h_k} $, $ {g_k} $, $ {g_{k,m}} $, $ {f_{\text{E}}} $, $ I_{k,m}^{{\text{max}}} $, $ r_k^{{\text{B,min}}} $, $ r_k^{{\text{A,min}}} $, $ \sigma _{\text{U}}^2 $, $ \sigma _{\text{E}}^2 $, $ \kappa _k^{\text{F}} $, $ {\kappa _{\text{R}}} $, ${T_{\rm{A}}}$, ${T_{\rm{B}}}$, $P_k^{\text{C}}$, $p_k^{\text{C}}$, $ \delta _k^{\text{G}} $, $ {\delta _{\text{E}}} $, $ {q^{{\text{fa}}}} $, 　$ {q^{{\text{md}}}} $, $ {q^{\text{o}}} $, $R_{{\text{sum}}}^{\left( 0 \right)}$；定义算法收敛精度$\eta > 0$和外层最大迭代次数${L_{\max }}$；初始化外层迭代次数$l = 0$；
(1) while$\|R_{ {\text{sum} } }^{\left( l \right)} - R_{ {\text{sum} } }^{\left( {l - 1} \right)}\| \ge \eta$或$l \le {L_{\max } }$，do；
(2) 定义迭代次数$l = l + 1$；
(3) 给定${ \tau_k^{{\rm{B}}{\left( {l - 1} \right)} } }$和${\tau_k^{ {\text{A} }{\left( {l - 1} \right)} } }$的值，根据P4-1计算$ \beta _k^{(l)} $和$ p_k^{(l)} $；
(4) 固定$ \beta _k^{(l)} $和$ p_k^{(l)} $，根据P4-2计算${\tau_k^{{\rm{B}}{\left( l \right)} } }$和${\tau_k^{{\text{A}} \left( l \right)} }$；
(5) 更新吞吐量$R_{{\text{sum}}}^{\left( l \right)}$；
(6) end while；
(7) 输出$\tau _k^{\rm{B}}$, $ \tau _k^{\text{A}} $, $ {\beta _k} $, $ {p_k} $

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