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1. 郑州大学河南先进技术研究院,河南 郑州 450003
2. 郑州大学信息工程学院,河南 郑州 450001
3. 郑州大学产业技术研究院,河南 郑州 450001
4. 中国科学院大学计算技术研究所,北京 100049
[ "郝万明(1988- ),男,河南安阳人,博士,郑州大学讲师,主要研究方向为毫米波、NOMA 无线通信、边缘缓存和无线携能通信等。" ]
[ "谢金坤(1993- ),男,河南项城人,郑州大学硕士生,主要研究方向为物联网、反向散射通信、物理层安全等。" ]
[ "孙钢灿(1977- ),男,河南濮阳人,博士,郑州大学副教授,主要研究方向为通信信号处理、通信信号关键参数盲估计、调制方式识别、机器人和智慧物流等。" ]
[ "朱政宇(1988- ),男,河南鹿邑人,博士,郑州大学讲师,主要研究方向为无线通信和信号处理、5G、物联网、机器学习、大规模MIMO、毫米波通信、无人机通信、物理层安全、无线协作网络、凸优化技术和携能传输等。" ]
[ "周一青(1975- ),女,北京人,博士,中国科学院大学研究员,主要研究方向为无线移动通信技术。" ]
网络出版日期:2021-03,
纸质出版日期:2021-03-25
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郝万明, 谢金坤, 孙钢灿, 等. 基于无线供能反向散射通信系统安全性的稳健机会约束优化算法设计[J]. 通信学报, 2021,42(3):100-110.
Wanming HAO, Jinkun XIE, Gangcan SUN, et al. Robust chance-constrained optimization algorithm design for secure wireless powered backscatter communication system[J]. Journal on communications, 2021, 42(3): 100-110.
郝万明, 谢金坤, 孙钢灿, 等. 基于无线供能反向散射通信系统安全性的稳健机会约束优化算法设计[J]. 通信学报, 2021,42(3):100-110. DOI: 10.11959/j.issn.1000-436x.2021048.
Wanming HAO, Jinkun XIE, Gangcan SUN, et al. Robust chance-constrained optimization algorithm design for secure wireless powered backscatter communication system[J]. Journal on communications, 2021, 42(3): 100-110. DOI: 10.11959/j.issn.1000-436x.2021048.
针对无线供能反向散射通信系统中信道不确定性以及安全性问题,提出一种考虑系统安全性的稳健机会约束优化算法。首先,在考虑窃听数据传输速率约束、最小能量收集约束和设备反射率约束条件下,建立一个基于信道不确定性的最大化系统传输速率最小值的优化问题。为求解该问题,应用基于伯恩斯坦型不等式的安全近似方法,将原不确定性问题转换为确定性优化问题。然后,结合不等式的性质,引入相应的辅助变量,将确定性优化问题转换为凸优化问题,并利用标准的凸优化算法进行求解。最后,仿真结果表明了所提算法的有效性。
For the channel uncertainty and security problems in wireless powered backscatter communication (WPBC) system
a robust chance-constrained optimization algorithm was proposed.Firstly
an optimization problem was established to maximize the minimum transmission rate of the system based on channel uncertainty considering eavesdropping data transmission rate constraints
minimum energy collection constraints and device reflectivity constraints.In order to solve the problem
the original uncertainty problem was transformed into a deterministic optimization problem by using the security approximation method based on Bernstein inequality.Then
combining the properties of inequality
the corresponding auxiliary variables were introduced to transform the deterministic optimization problem into a convex optimization problem
and the standard convex optimization algorithm was used to solve the problem.Finally
the simulation results show the effectiveness of the proposed algorithm.
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