Anti-Intelligent UAV Jamming Strategy via Deep Q-Networks

Gao, N. and Qin, Z. and Jing, X. and Ni, Q. and Jin, S. (2020) Anti-Intelligent UAV Jamming Strategy via Deep Q-Networks. IEEE Transactions on Communications, 68 (1). pp. 569-581. ISSN 0090-6778

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Abstract

The downlink communications are vulnerable to intelligent unmanned aerial vehicle (UAV) jamming attack. In this paper, we propose a novel anti-intelligent UAV jamming strategy, in which the ground users can learn the optimal trajectory to elude such jamming. The problem is formulated as a stackelberg dynamic game, where the UAV jammer acts as a leader and the ground users act as followers. First, as the UAV jammer is only aware of the incomplete channel state information (CSI) of the ground users, for the first attempt, we model such leader sub-game as a partially observable Markov decision process (POMDP). Then, we obtain the optimal jamming trajectory via the developed deep recurrent Q-networks (DRQN) in the three-dimension space. Next, for the followers sub-game, we use the Markov decision process (MDP) to model it. Then we obtain the optimal communication trajectory via the developed deep Q-networks (DQN) in the two-dimension space. We prove the existence of the stackelberg equilibrium and derive the closed-form expression for the stackelberg equilibrium in a special case. Moreover, some insightful remarks are obtained and the time complexity of the proposed defense strategy is analyzed. The simulations show that the proposed defense strategy outperforms the benchmark strategies.

Item Type:
Journal Article
Journal or Publication Title:
IEEE Transactions on Communications
Additional Information:
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Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2200/2208
Subjects:
ID Code:
138108
Deposited By:
Deposited On:
25 Oct 2019 09:30
Refereed?:
Yes
Published?:
Published
Last Modified:
24 Sep 2020 04:57