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

页码：166 / 171

页数：6

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