DeepAntiJam: Stackelberg Game-Oriented Secure Transmission via Deep Reinforcement Learning

In this letter, we present a novel deep reinforcement learning-assisted anti-jamming transmission scheme (DeepAntiJam) to guarantee reliable and creditable transmission in the presence of one smart jammer and multiple eavesdroppers. Specifically, we formulate secure transmission as a Stackelberg gam...

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Bibliographic Details
Published in:IEEE communications letters 2022-09, Vol.26 (9), p.1984-1988
Main Authors: Lu, Jianzhong, He, Dongxuan, Wang, Zhaocheng
Format: Article
Language:English
Subjects:
Artificial neural networks
Deep learning
Eavesdropping
Game theory
Games
Jammers
Jamming
multiple eavesdroppers
Neural networks
Reinforcement learning
smart jammer
Stackelberg game
Throughput
Transfer learning
Transmitters
Wireless communication
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Summary:In this letter, we present a novel deep reinforcement learning-assisted anti-jamming transmission scheme (DeepAntiJam) to guarantee reliable and creditable transmission in the presence of one smart jammer and multiple eavesdroppers. Specifically, we formulate secure transmission as a Stackelberg game in which the jammer, as the leader, adaptively adjusts its jamming power while the transmitter, as the follower, selects its transmit power and secrecy rate accordingly. Furthermore, the existence and uniqueness of Stackelberg equilibrium are proved. To achieve the Stackelberg equilibrium when the prior knowledge of jammer is unknown, DeepAntiJam is proposed to improve the secrecy throughput, where a solver neural network is utilized to determine the optimal transmission parameter according to the transmission strategy obtained by deep reinforcement learning. Moreover, transfer learning is introduced into the initialization of DeepAntiJam to avoid unnecessary initial random exploration. Simulation results validate that DeepAntiJam can enhance secrecy throughput significantly under the coexistence of smart jammer.
ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2022.3182001