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).

Keywords

• D2D communications
• physical layer security
• full-duplex active eavesdropper
• Q-learning
• stochastic learning automata