Paper Info
Title
Game-Theoretic Learning Approaches for Secure D2D Communications Against Full-Duplex Active Eavesdropper.
Abstract
In this paper, we analyze the anti-eavesdropping and anti-jamming performance of D2D communications with a full-duplex active eavesdropper (FAE). We consider the scenario that when the FAE intrudes the D2D underlaying cellular networks, it can passively wiretap confidential messages in D2D communications and actively jam all legitimate links. A hierarchical and heterogeneous power control mechanism with multiple D2D user equipments (DUEs) and one cellular user equipment (CUE) is proposed to combat the intelligent FAE. Moreover, a multi-tier Stackelberg game is formulated to model the complex interaction among them and the existence of Stackelberg equilibrium (SE) is proved. The best response (BR)-based hierarchical power control algorithm with perfect information and a robust learning method with imperfect information are proposed to obtain SE. The numerical results illustrate the convergence of the two proposed hierarchical power control algorithms, which are also compared with the random selection algorithm (RSA).
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2906845
IEEE ACCESS
Keywords
Field
DocType
D2D communications,physical layer security,full-duplex active eavesdropper,Q-learning,stochastic learning automata
Computer science,Computer network,Game theoretic,Duplex (telecommunications)
Journal
Volume
ISSN
Citations 
7
2169-3536
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Yijie Luo131.39
Zhibin Feng200.34
Han Jiang31412.05
Yang Yang4612174.82
Yuzhen Huang500.34
Junnan Yao6242.76