Unmanned aerial vehicles (UAVs) are envisioned to be widely deployed as an integral component in the next generation cellular networks, where spectrum sharing between the aerial and terrestrial communication systems will play an important role. However, there exist significant security and privacy challenges due to the untrusted broadcast features and wireless transmission of the UAV networks. This paper endeavors to resolve the security issues through proposing a novel privacypreserving secure spectrum trading and sharing scheme based on blockchain technology. Specifically, from the operator's perspective, a pricing-based incentive mechanism is firstly introduced, in which a primary mobile network operator (MNO) leases its owned spectrum to a secondary UAV network in exchange for some revenue from the UAV operators. To address the potential security issues, a spectrum blockchain framework is then proposed to illustrate detailed operations of how the blockchain helps to improve the spectrum trading environment. Under this framework, a Stackelberg game is formulated to jointly maximize the profits of the MNO and the UAV operators considering uniform and non-uniform pricing schemes. Security assessment and numerical results confirm the security and efficiency of our schemes for spectrum sharing in UAV-assisted cellular networks.

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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).INDEX TERMS D2D communications, physical layer security, full-duplex active eavesdropper, Q-learning, stochastic learning automata.