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Anti-Jamming Communication Using Imitation Learning

The communication reliability of wireless communication systems is threatened by malicious jammers. Aiming at the problem of reliable communication under malicious jamming, a large number of schemes have been proposed to mitigate the effects of malicious jamming by avoiding the blocking interference...

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Detalles Bibliográficos
Autores principales: Zhou, Zhanyang, Niu, Yingtao, Wan, Boyu, Zhou, Wenhao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10670521/
https://www.ncbi.nlm.nih.gov/pubmed/37998239
http://dx.doi.org/10.3390/e25111547
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author Zhou, Zhanyang
Niu, Yingtao
Wan, Boyu
Zhou, Wenhao
author_facet Zhou, Zhanyang
Niu, Yingtao
Wan, Boyu
Zhou, Wenhao
author_sort Zhou, Zhanyang
collection PubMed
description The communication reliability of wireless communication systems is threatened by malicious jammers. Aiming at the problem of reliable communication under malicious jamming, a large number of schemes have been proposed to mitigate the effects of malicious jamming by avoiding the blocking interference of jammers. However, the existing anti-jamming schemes, such as fixed strategy, Reinforcement learning (RL), and deep Q network (DQN) have limited use of historical data, and most of them only pay attention to the current state changes and cannot gain experience from historical samples. In view of this, this manuscript proposes anti-jamming communication using imitation learning. Specifically, this manuscript addresses the problem of anti-jamming decisions for wireless communication in scenarios with malicious jamming and proposes an algorithm that consists of three steps: First, the heuristic-based Expert Trajectory Generation Algorithm is proposed as the expert strategy, which enables us to obtain the expert trajectory from historical samples. The trajectory mentioned in this algorithm represents the sequence of actions undertaken by the expert in various situations. Then obtaining a user strategy by imitating the expert strategy using an imitation learning neural network. Finally, adopting a functional user strategy for efficient and sequential anti-jamming decisions. Simulation results indicate that the proposed method outperforms the RL-based anti-jamming method and DQN-based anti-jamming method regarding solving continuous-state spectrum anti-jamming problems without causing “curse of dimensionality” and providing greater robustness against channel fading and noise as well as when the jamming pattern changes.
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spelling pubmed-106705212023-11-16 Anti-Jamming Communication Using Imitation Learning Zhou, Zhanyang Niu, Yingtao Wan, Boyu Zhou, Wenhao Entropy (Basel) Article The communication reliability of wireless communication systems is threatened by malicious jammers. Aiming at the problem of reliable communication under malicious jamming, a large number of schemes have been proposed to mitigate the effects of malicious jamming by avoiding the blocking interference of jammers. However, the existing anti-jamming schemes, such as fixed strategy, Reinforcement learning (RL), and deep Q network (DQN) have limited use of historical data, and most of them only pay attention to the current state changes and cannot gain experience from historical samples. In view of this, this manuscript proposes anti-jamming communication using imitation learning. Specifically, this manuscript addresses the problem of anti-jamming decisions for wireless communication in scenarios with malicious jamming and proposes an algorithm that consists of three steps: First, the heuristic-based Expert Trajectory Generation Algorithm is proposed as the expert strategy, which enables us to obtain the expert trajectory from historical samples. The trajectory mentioned in this algorithm represents the sequence of actions undertaken by the expert in various situations. Then obtaining a user strategy by imitating the expert strategy using an imitation learning neural network. Finally, adopting a functional user strategy for efficient and sequential anti-jamming decisions. Simulation results indicate that the proposed method outperforms the RL-based anti-jamming method and DQN-based anti-jamming method regarding solving continuous-state spectrum anti-jamming problems without causing “curse of dimensionality” and providing greater robustness against channel fading and noise as well as when the jamming pattern changes. MDPI 2023-11-16 /pmc/articles/PMC10670521/ /pubmed/37998239 http://dx.doi.org/10.3390/e25111547 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhou, Zhanyang
Niu, Yingtao
Wan, Boyu
Zhou, Wenhao
Anti-Jamming Communication Using Imitation Learning
title Anti-Jamming Communication Using Imitation Learning
title_full Anti-Jamming Communication Using Imitation Learning
title_fullStr Anti-Jamming Communication Using Imitation Learning
title_full_unstemmed Anti-Jamming Communication Using Imitation Learning
title_short Anti-Jamming Communication Using Imitation Learning
title_sort anti-jamming communication using imitation learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10670521/
https://www.ncbi.nlm.nih.gov/pubmed/37998239
http://dx.doi.org/10.3390/e25111547
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