Design and implementation of reinforcement learning‐based intelligent jamming system

Zhang, Shuangyi; Tian, Hui; Chen, Xueqiang; Du, Zhiyong; Huang, Luying; Gong, Yuping; Xu, Yuhua

doi:10.1049/iet-com.2020.0410

Cited by 14 publications

(8 citation statements)

References 18 publications

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“…From the perspective of this article, there is a good background in psychology research, but the data it uses are only obtained from the mental health test questionnaire, and the object of the study is only students of a certain grade in the author's school. Moreover, the literature is not in-depth enough to master the technology of data mining and the experimental tests conducted [9]. Alghamdi et al used the decision tree C4.5 algorithm to establish a mental health assessment model, constructed a decision tree, and predicted mental health by extracting rules.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of Online Intelligent Mental Health Testing Platform

Ren

Hou

Juz-he

2022

Journal of Healthcare Engineering

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In order to solve the problems of high misevaluation rate and low work efficiency in the process of mental health intelligent evaluation, a method of mental health intelligent evaluation system oriented to the decision tree algorithm is proposed. First, the current research status of mental health intelligent evaluation was analyzed and the framework of mental health intelligent evaluation system was constructed. Then, the mental health intelligent evaluation data were collected and the decision tree algorithm was used to analyze and classify the mental health intelligent evaluation data to obtain the mental health intelligent evaluation results. Finally, specific simulation experiments are used to analyze the feasibility and superiority of the mental health intelligent evaluation system. The experimental results show that the recall rate of each system increases with the increasing number of iterations, and the system has the highest recall rate. Also, it is stable after the number of iterations reaches 20, with good recall and adaptive scheduling performance. The recall rate of comparison system 1 and comparison system 2 fluctuates greatly, and the recall rate is lower than that of the system in this paper. It is proved that the method of the mental health intelligent evaluation system of the decision tree algorithm can effectively solve the problem and improve the accuracy of the mental health intelligent evaluation. The efficiency of mental health intelligent evaluation is improved, and the system stability is better, which can meet the actual requirements of current mental health intelligent evaluation.

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Section: Introductionmentioning

confidence: 99%

Design and Implementation of Online Intelligent Mental Health Testing Platform

Ren

Hou

Juz-he

2022

Journal of Healthcare Engineering

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“…Based on the abovementioned studies, various types of methods have made a breakthrough in the field of intelligent jamming [13][14][15][16][17][18][19]. In [13], the authors proposed an intelligent jamming algorithm based on the Q-learning and evaluated the jamming performance of the algorithm in different antijamming strategies.…”

Section: Related Workmentioning

confidence: 99%

“…In [13], the authors proposed an intelligent jamming algorithm based on the Q-learning and evaluated the jamming performance of the algorithm in different antijamming strategies. On this basis, Zhang et al [14] proposed a jamming framework of "offline learning and virtual decision-making" based on the Q-learning and verified the effectiveness of the algorithm in practical communication environment. Furthermore, Rao et al [15] utilized the value variance of effective jamming action to set the confidence interval and eliminate the jamming action with low confidence from the action space, which accelerated the optimal strategy learning process.…”

Section: Related Workmentioning

confidence: 99%

“…On the other hand, the increasing level of intelligence in the field of antijamming and the increasing diversity of antijamming methods have brought huge challenges to jamming [3][4][5][6][7][8][9][10][11][12]. Therefore, to cope with the increasing powerful antijamming technology and overcome the disadvantages of the traditional jamming patterns being single and inflexible, many scholars have carried out researches on communication jamming technology [13][14][15][16][17][18][19]. However, these jamming theories mainly focus on the jamming methods of a single dimensional domain, which can not be extended to other domains.…”

Section: Introductionmentioning

confidence: 99%

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A DRL-Based Intelligent Jamming Approach for Joint Channel and Power Optimization

Wang

Song

et al. 2023

Wireless Communications and Mobile Computing

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The traditional jamming methods mainly focus on the optimization of a single domain, such as frequency domain. However, in the practical wireless communication, the jamming methods in single dimension domain are limited and difficult to cope with multidomain scenarios. To overcome these issues, this paper investigates the problem of joint decision-making for jamming channel and power in a dynamic spectrum environment. Firstly, the Markov decision process (MDP) is used to formulate the jamming channel and power joint selection problem. Then, a deep reinforcement learning- (DRL-) based jamming algorithm is proposed with the function of parallel learning and joint decision-making. Specially, to accelerate the learning speed of the algorithm, the prioritized experience reply (PER) technology is introduced. Finally, a practical jamming testbed is built to evaluate the proposed algorithm. The simulation results and the testbed results demonstrate the effectiveness of the proposed algorithm, which can guarantee the jamming effect and maximize the utilization of jamming resources.

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“…Setting sensing time basing on fading is helpful for communication. However, it may not be enough in the presence of dynamic jammer nodes such as the ones designed in [30]- [33].…”

Section: Related Work a Anti-jammingmentioning

confidence: 99%

Deep Multi-Task Conditional and Sequential Learning for Anti-Jamming

Basomingera¹,

Choi

2021

IEEE Access

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Multi-task learning provides plenty of room for performance improvement to single-task learning, when learned tasks are related and learned with mutual information. In this work, we analyze the efficiency of using a single-task reinforcement learning algorithm to mitigate jamming attacks with frequency hopping strategy. Our findings show that single-task learning implementations do not always guarantee optimal cumulative reward when some jammer's parameters are unknown, notably the jamming time-slot length in this case. Therefore, to maximize packet transmission in the presence of a jammer whose parameters are unknown, we propose deep multi-task conditional and sequential learning (DMCSL), a multi-task learning algorithm that builds a transition policy to optimize conditional and sequential tasks. For the antijamming system, the proposed model learns two tasks: sensing time and transmission channel selection. DMCSL is a composite of the state-of-the-art reinforcement learning algorithms, multi-armed bandit and an extended deep-Q-network. To improve the chance of convergence and optimal cumulative reward of the algorithm, we also propose a continuous action-space update algorithm for sensing time action-space. The simulation results show that DMCSL guarantees better performance than single-task learning by relying on a logarithmically increased action-space sample. Against a random dynamic jamming time-slot, DMCSL achieves about three times better cumulative reward, and against a periodic dynamic jamming time-slot, it improves by 10% the cumulative reward.INDEX TERMS Ad hoc network, deep learning, jamming attack, multi-armed bandit, spectrum sensing.

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Design and implementation of reinforcement learning‐based intelligent jamming system

Cited by 14 publications

References 18 publications

Design and Implementation of Online Intelligent Mental Health Testing Platform

Design and Implementation of Online Intelligent Mental Health Testing Platform

A DRL-Based Intelligent Jamming Approach for Joint Channel and Power Optimization

Deep Multi-Task Conditional and Sequential Learning for Anti-Jamming

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