Secrecy Capacity Maximization in Wireless Wiretap Channel: A Neurodynamic Optimization Approach

被引：0
|
作者：
Yu, Hongyan [1 ]
Zhang, Baojie [1 ]
Wang, Tong [1 ]
Wang, Jun [1 ]
机构：
[1] Chongqing Three Gorges Univ, Coll Three Gorges Artificial Intelligence, Chongqing 404100, Peoples R China
来源：
2021 13TH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTATIONAL INTELLIGENCE (ICACI) | 2021年
关键词：
Physical-layer security; Neurodynamic optimization; Wiretap channel; Wireless communication; Convex optimization; LINEAR-PROGRAMMING PROBLEMS; PHYSICAL LAYER SECURITY; NEURAL-NETWORK;
D O I：
10.1109/ICACI52617.2021.9435916
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper addresses the secure transmission problem of privacy information over a fading channel with an eavesdropper. A neural network model is proposed for solving the secrecy capacity maximization problems in real time. Unlike conventional power allocation strategies, a neurodynamic secure transmission approach is provided by the relation between KKT (Karush-Kuhn-Tucker) optimality conditions and the equilibrium point of a neural network. The transient behaviour of neural networks are showed, and the effectiveness of the neurodynamic approach is substantiated with a secrecy capacity maximization problem.
引用
收藏
页码：166 / 171
页数：6
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