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Anti-Intelligent UAV Jamming Strategy via Deep Q-Networks

Research output: Contribution to journalJournal article

Published

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Anti-Intelligent UAV Jamming Strategy via Deep Q-Networks. / Gao, N.; Qin, Z.; Jing, X.; Ni, Q.; Jin, S.

In: IEEE Transactions on Communications, Vol. 68, No. 1, 01.01.2020, p. 569-581.

Research output: Contribution to journalJournal article

Harvard

Gao, N, Qin, Z, Jing, X, Ni, Q & Jin, S 2020, 'Anti-Intelligent UAV Jamming Strategy via Deep Q-Networks', IEEE Transactions on Communications, vol. 68, no. 1, pp. 569-581. https://doi.org/10.1109/TCOMM.2019.2947918

APA

Gao, N., Qin, Z., Jing, X., Ni, Q., & Jin, S. (2020). Anti-Intelligent UAV Jamming Strategy via Deep Q-Networks. IEEE Transactions on Communications, 68(1), 569-581. https://doi.org/10.1109/TCOMM.2019.2947918

Vancouver

Gao N, Qin Z, Jing X, Ni Q, Jin S. Anti-Intelligent UAV Jamming Strategy via Deep Q-Networks. IEEE Transactions on Communications. 2020 Jan 1;68(1):569-581. https://doi.org/10.1109/TCOMM.2019.2947918

Author

Gao, N. ; Qin, Z. ; Jing, X. ; Ni, Q. ; Jin, S. / Anti-Intelligent UAV Jamming Strategy via Deep Q-Networks. In: IEEE Transactions on Communications. 2020 ; Vol. 68, No. 1. pp. 569-581.

Bibtex

@article{7a433bcc5a354f408906d7e8e761810a,
title = "Anti-Intelligent UAV Jamming Strategy via Deep Q-Networks",
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.",
keywords = "Jamming, Base stations, Trajectory, Games, Unmanned aerial vehicles, Space stations, Security, UAV, jamming, Markov decision process, deep Q-networks",
author = "N. Gao and Z. Qin and X. Jing and Q. Ni and S. Jin",
note = "{\textcopyright}2019 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.",
year = "2020",
month = jan
day = "1",
doi = "10.1109/TCOMM.2019.2947918",
language = "English",
volume = "68",
pages = "569--581",
journal = "IEEE Transactions on Communications",
issn = "0090-6778",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "1",

}

RIS

TY - JOUR

T1 - Anti-Intelligent UAV Jamming Strategy via Deep Q-Networks

AU - Gao, N.

AU - Qin, Z.

AU - Jing, X.

AU - Ni, Q.

AU - Jin, S.

N1 - ©2019 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2020/1/1

Y1 - 2020/1/1

N2 - 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.

AB - 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.

KW - Jamming

KW - Base stations

KW - Trajectory

KW - Games

KW - Unmanned aerial vehicles

KW - Space stations

KW - Security

KW - UAV

KW - jamming

KW - Markov decision process

KW - deep Q-networks

U2 - 10.1109/TCOMM.2019.2947918

DO - 10.1109/TCOMM.2019.2947918

M3 - Journal article

VL - 68

SP - 569

EP - 581

JO - IEEE Transactions on Communications

JF - IEEE Transactions on Communications

SN - 0090-6778

IS - 1

ER -