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Anti-Intelligent UAV Jamming Strategy via Deep Q-Networks

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

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

2019 IEEE International Conference on Communications, ICC 2019 - Proceedings. IEEE, 2019. 8762016 (IEEE International Conference on Communications; Vol. 2019-May).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

Harvard

Gao, N, Qin, Z, Jing, X & Ni, Q 2019, Anti-Intelligent UAV Jamming Strategy via Deep Q-Networks. in 2019 IEEE International Conference on Communications, ICC 2019 - Proceedings., 8762016, IEEE International Conference on Communications, vol. 2019-May, IEEE. https://doi.org/10.1109/ICC.2019.8762016

APA

Gao, N., Qin, Z., Jing, X., & Ni, Q. (2019). Anti-Intelligent UAV Jamming Strategy via Deep Q-Networks. In 2019 IEEE International Conference on Communications, ICC 2019 - Proceedings [8762016] (IEEE International Conference on Communications; Vol. 2019-May). IEEE. https://doi.org/10.1109/ICC.2019.8762016

Vancouver

Gao N, Qin Z, Jing X, Ni Q. Anti-Intelligent UAV Jamming Strategy via Deep Q-Networks. In 2019 IEEE International Conference on Communications, ICC 2019 - Proceedings. IEEE. 2019. 8762016. (IEEE International Conference on Communications). https://doi.org/10.1109/ICC.2019.8762016

Author

Gao, N. ; Qin, Z. ; Jing, X. ; Ni, Q. / Anti-Intelligent UAV Jamming Strategy via Deep Q-Networks. 2019 IEEE International Conference on Communications, ICC 2019 - Proceedings. IEEE, 2019. (IEEE International Conference on Communications).

Bibtex

@inproceedings{d5849fde01dd4266b53b13637c852ea4,
title = "Anti-Intelligent UAV Jamming Strategy via Deep Q-Networks",
abstract = "The downlink communications are vulnerable to intelligent unmanned aerial vehicle (UAV) jamming attack which can learn the optimal attack strategy in complex communication environments. In this paper, we propose an anti-intelligent UAV jamming strategy, in which the mobile users can learn the optimal defense strategy to prevent jamming. Specifically, the UAV jammer acts as a leader and the users act as followers. The problem is formulated as a stackelberg dynamic game, which includes the leader sub-game and the followers sub-game. As the UAV jammer is only aware of the incomplete channel state information (CSI) of the users, we model the leader sub-game as a partially observable Markov decision process (POMDP). The optimal jamming trajectory is obtained via deep recurrent Q-networks (DRQN) in the three-dimension space. For the followers sub-game, we use the Markov decision process (MDP) to model it. Then the optimal communication trajectory can be learned via deep Q-networks (DQN) in the two-dimension space. We prove the existence of the stackelberg equilibrium. The simulations show that the proposed strategy outperforms the benchmark strategies.",
author = "N. Gao and Z. Qin and X. Jing and Q. Ni",
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 = "2019",
month = may
day = "20",
doi = "10.1109/ICC.2019.8762016",
language = "English",
isbn = "9781538680896",
series = "IEEE International Conference on Communications",
publisher = "IEEE",
booktitle = "2019 IEEE International Conference on Communications, ICC 2019 - Proceedings",

}

RIS

TY - GEN

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

AU - Gao, N.

AU - Qin, Z.

AU - Jing, X.

AU - Ni, Q.

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 - 2019/5/20

Y1 - 2019/5/20

N2 - The downlink communications are vulnerable to intelligent unmanned aerial vehicle (UAV) jamming attack which can learn the optimal attack strategy in complex communication environments. In this paper, we propose an anti-intelligent UAV jamming strategy, in which the mobile users can learn the optimal defense strategy to prevent jamming. Specifically, the UAV jammer acts as a leader and the users act as followers. The problem is formulated as a stackelberg dynamic game, which includes the leader sub-game and the followers sub-game. As the UAV jammer is only aware of the incomplete channel state information (CSI) of the users, we model the leader sub-game as a partially observable Markov decision process (POMDP). The optimal jamming trajectory is obtained via deep recurrent Q-networks (DRQN) in the three-dimension space. For the followers sub-game, we use the Markov decision process (MDP) to model it. Then the optimal communication trajectory can be learned via deep Q-networks (DQN) in the two-dimension space. We prove the existence of the stackelberg equilibrium. The simulations show that the proposed strategy outperforms the benchmark strategies.

AB - The downlink communications are vulnerable to intelligent unmanned aerial vehicle (UAV) jamming attack which can learn the optimal attack strategy in complex communication environments. In this paper, we propose an anti-intelligent UAV jamming strategy, in which the mobile users can learn the optimal defense strategy to prevent jamming. Specifically, the UAV jammer acts as a leader and the users act as followers. The problem is formulated as a stackelberg dynamic game, which includes the leader sub-game and the followers sub-game. As the UAV jammer is only aware of the incomplete channel state information (CSI) of the users, we model the leader sub-game as a partially observable Markov decision process (POMDP). The optimal jamming trajectory is obtained via deep recurrent Q-networks (DRQN) in the three-dimension space. For the followers sub-game, we use the Markov decision process (MDP) to model it. Then the optimal communication trajectory can be learned via deep Q-networks (DQN) in the two-dimension space. We prove the existence of the stackelberg equilibrium. The simulations show that the proposed strategy outperforms the benchmark strategies.

U2 - 10.1109/ICC.2019.8762016

DO - 10.1109/ICC.2019.8762016

M3 - Conference contribution/Paper

SN - 9781538680896

T3 - IEEE International Conference on Communications

BT - 2019 IEEE International Conference on Communications, ICC 2019 - Proceedings

PB - IEEE

ER -