Joint Information-Theoretic Secrecy and Covertness for UAV-Assisted Wireless Transmission With Finite Blocklength

被引:6
|
作者
Liu, Pengpeng [1 ]
Li, Zan [1 ]
Si, Jiangbo [1 ]
Al-Dhahir, Naofal [2 ]
Gao, Yang [3 ]
机构
[1] Xidian Univ, State Key Lab Integrated Serv Networks, Xian 710071, Peoples R China
[2] Univ Texas Dallas, Dept Elect & Comp Engn, Richardson, TX 75080 USA
[3] Air Force Engn Univ, Coll Informat & Nav, Xian 710071, Peoples R China
基金
中国博士后科学基金;
关键词
Optimization; Wireless communication; Three-dimensional displays; Jamming; Trajectory; Security; Physical layer security; Unmanned aerial vehicles; physical layer security; covert communication; finite blocklength; 3D trajectory optimization; power optimization; RESOURCE-ALLOCATION; TRAJECTORY DESIGN; POWER; COMMUNICATION; OPTIMIZATION; PROBABILITY; THROUGHPUT; NETWORKS; SYSTEM;
D O I
10.1109/TVT.2023.3254882
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In this paper, we study a novel joint information-theoretic secrecy and covertness model for an unmanned aerial vehicle (UAV)-assisted finite blocklength transmission system, in which an UAV delivers classified information to a ground user (Bob) in the presence of two unauthorized users (Eve and Willie). Specifically, Eve tries to decode the legitimate information while Willie tries to detect if the UAV's transmission is present or not. Firstly, we assume that the perfect location knowledge of both Eve and Willie is known at the UAV. In this scenario, to guarantee security and covertness, the average secrecy rate maximization problem is formulated via joint design of the UAV's transmit power and three dimensional (3D) trajectory under Willie's covertness constraint. Technically speaking, the optimization problem is non-convex and mathematically intractable to tackle owing to the coupling of multiple variables. To address this non-convex problem, an iterative alternating optimization (AO) algorithm is developed with the assistance of the successive convex approximation (SCA) technique. After that, we extend the system model to both Eve's and Willie's imperfect location knowledge case, which is more sophisticated compared to the perfect location knowledge case due to the uncertain location constraints. We still utilize the iterative AO algorithm combined with SCA and S-procedure techniques to cope with the nonconvex problem. Numerical results indicate that our proposed 3D optimization algorithm performs better than the conventional two dimensional (2D) scheme and the benchmark scheme for both perfect and imperfect location knowledge cases.
引用
收藏
页码:10187 / 10199
页数:13
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