2019
DOI: 10.1186/s13638-019-1414-4
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An algorithm for jamming strategy using OMP and MAB

Abstract: Reinforcement learning (RL) has the advantage of interaction with an environment over time, which is helpful in cognitive jamming research, especially in an electronic warfare-type scenario, in which the communication parameters and jamming effect are unknown to a jammer. In this paper, an algorithm for a jamming strategy using orthogonal matching pursuit (OMP) and multi-armed bandit (MAB) is proposed. We construct a dictionary in which each atom represents a symbol error rate (SER) curve and can be obtained w… Show more

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Cited by 9 publications
(4 citation statements)
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“…In order to be effective, a jammer must be as proactive as possible and have the shortest learning phase so as not to consume a lot of energy resources. Work aims to reduce the learning phase as shown in [153]. To address this problem, they combine the advantages of an orthogonal matching pursuit system (OMP) and a multiagent system (MAB).…”
Section: Smart Attacks Based On Behavioral Detectionmentioning
confidence: 99%
“…In order to be effective, a jammer must be as proactive as possible and have the shortest learning phase so as not to consume a lot of energy resources. Work aims to reduce the learning phase as shown in [153]. To address this problem, they combine the advantages of an orthogonal matching pursuit system (OMP) and a multiagent system (MAB).…”
Section: Smart Attacks Based On Behavioral Detectionmentioning
confidence: 99%
“…In [22], the authors studied the optimal physical layer jamming patterns based on the multiarm bandit (MAB) framework. Furthermore, the authors in [23] used orthogonal matching pursuit based on MAB to optimize the jamming strategy, and the jamming patterns were enriched. The study in [24] designed an intelligent jamming method based on reinforcement learning to combat the DRL-based user and verified that the proposed RL-based jamming can effectively restrict the performance of DRL-based antijamming method in frequency band selection.…”
Section: Related Workmentioning
confidence: 99%
“…Furthermore, reinforcement learning has numerous applications in communication adversarial scenarios. In [ 20 , 21 , 22 ], the authors extensively studied the utilization of reinforcement learning algorithms to search for optimal jamming strategies at the physical layer and the MAC layer.…”
Section: Introductionmentioning
confidence: 99%