Game-Theoretic Learning Approaches for Secure D2D Communications Against Full-Duplex Active Eavesdropper

Game-Theoretic Learning Approaches for Secure D2D Communications Against Full-Duplex Active Eavesdropper

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 communica...

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Bibliographic Details
Main Authors: Yijie Luo, Zhibin Feng, Han Jiang, Yang Yang, Yuzhen Huang, Junnan Yao
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
D2D communications
physical layer security
full-duplex active eavesdropper
Q-learning
stochastic learning automata
Online Access:https://ieeexplore.ieee.org/document/8674458/

Internet

https://ieeexplore.ieee.org/document/8674458/

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