Cross-Layer Anti-Jamming Scheme: A Hierarchical Learning Approach

Niu, Yingtao

doi:10.1109/access.2018.2847045

Cited by 19 publications

(21 citation statements)

References 23 publications

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“…For 0 ξ → , the learning agent tends to select the action with the maximal Q-value, however, if ξ → ∞ , the learning agent's policy is completely random [27]. the coefficients of the Boltzmann model, which affect the transition from exploring to exploiting [6]. Motivated by classical Q-Learning theory, an anti-jamming Q learning algorithm (AQLA) is put forward to solve the anti-jamming channel selection problem [24].…”

Section: Anti-jamming Q-learning Algorithmmentioning

confidence: 99%

“…Moreover, with the development of artificial intelligence, smart jammers can adaptively launch jamming attacks on users according to the reconnaissance results, which could severely decrease the reliability of transmission [4,5]. It is difficult to counter this jamming by conventional anti-jamming methods, such as frequency-hopping communication schemes, whose anti-jamming strategies are fixed and independent [6].…”

Section: Introductionmentioning

confidence: 99%

“…To address this problem, some artificial intelligence technologies, such as reinforcement learning technology and deep learning technology [6,7], have also been widely applied for wireless communications, due to its dynamic adaptability on anti-jamming [8]. One of the most commonly used method in AI fields is the reinforcement learning method, which has been utilized to analyze the jamming policy [9], and make anti-jamming decision [10].…”

Section: Introductionmentioning

confidence: 99%

“…In terms of power control, a hierarchical learning approach based on reinforcement learning was formulated in [11] to solve the anti-jamming power game. The anti-jamming channel allocation problem is considered as a Markov decision process [6], which is generally solved using a reinforcement learning algorithm, such as Q learning algorithm [12]. In [13], a cross-layer resource allocation approach based on Q learning was proposed to effectively utilize unused spectrum opportunities.…”

Section: Introductionmentioning

confidence: 99%

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Multi-regional Anti-jamming Communication Scheme Based on Transfer Learning and Q Learning

Niu¹

2019

KSII TIIS

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The smart jammer launches jamming attacks which degrade the transmission reliability. In this paper, smart jamming attacks based on the communication probability over different channels is considered, and an anti-jamming Q learning algorithm (AQLA) is developed to obtain anti-jamming knowledge for the local region. To accelerate the learning process across multiple regions, a multi-regional intelligent anti-jamming learning algorithm (MIALA) which utilizes transferred knowledge from neighboring regions is proposed. The MIALA algorithm is evaluated through simulations, and the results show that the it is capable of learning the jamming rules and effectively speed up the learning rate of the whole communication region when the jamming rules are similar in the neighboring regions. . His research interests include learning theory, satellite communication, and communication anti-jamming technology. Yingtao Niu received his M.S. degree from PLA Commanding Communication Academy, China, in 2005, and received his Ph.D. degree from Institute of Communication Engineering, PLA University of Science and Technology Institute, China. He has authored more than 30 journal and conference papers. His main research interests are spread-spectrum communication, cognitive radio theory and techniques, with particular emphasis on algorithms of wireless communication signal processing and decision-making in cognitive radio systems.

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Section: Anti-jamming Q-learning Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-regional Anti-jamming Communication Scheme Based on Transfer Learning and Q Learning

Niu¹

2019

KSII TIIS

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“…As a major threat to reliable communication, the jammer can launch noise-like signals to legitimate receiver to decrease communication quality. In order to cope with malicious jamming attacks, a lot of anti-jamming works have been studied that focused on power control [1][2][3][4][5][6][7][8][9][10], frequency selection [11][12][13][14][15][16][17][18][19][20], and spatial domain [21][22][23][24][25], and many effective anti-jamming technologies were proposed. In this paper, we investigate the anti-jamming hierarchical optimization problem in relay communication system.…”

Section: Introductionmentioning

confidence: 99%

An Anti-Jamming Hierarchical Optimization Approach in Relay Communication System via Stackelberg Game

et al. 2019

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In this paper, we study joint relay selection and the power control optimization problem in an anti-jamming relay communication system. Considering the hierarchical competitive relationship between a user and jammer, we formulate the anti-jamming problem as a Stackelberg game. From the perspective of game, the user selects relay and power strategy firstly which acts as the leader, while the jammer chooses power strategy then that acts as follower. Moreover, we prove the existence of Stackelberg equilibrium. Based on the Q-learning algorithm and multi-armed bandit method, a hierarchical joint optimization algorithm is proposed. Simulation results show the user’s strategy selection probability and the jammer’s regret. We compare the user’s and jammer’s utility under the proposed algorithm with a random selection algorithm to verify the algorithm’s superiority. Moreover, the influence of feedback error and eavesdropping error on utility is analyzed.

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From Adaptive Communication Anti‐Jamming to Intelligent Communication Anti‐Jamming: 50 Years of Evolution

Zhou,

Niu

2024

Advanced Intelligent Systems

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Herein, a comprehensive review of the evolution of intelligent communication anti‐jamming techniques is provided. First, a clear definition of the concept and elaboration on the inherent connotations and capability characteristics of intelligent communication anti‐jamming is provided. Additionally, the initial construction of an intelligent communication anti‐jamming system architecture is outlined. Subsequently, the development of intelligent communication anti‐jamming is delved, tracing its progression from early‐stage adaptive anti‐jamming techniques to the more recent advancements in intelligent anti‐jamming, which are primarily based on game theory and machine learning. Moreover, the latest research findings in this domain are thoroughly examined and the existing challenges and bottlenecks that hinder current research progress are highlighted. Finally, several viable research directions for future studies in the field of intelligent anti‐jamming are proposed. The objective is to facilitate readers in gaining a comprehensive understanding of the concept, characteristics, and system architecture of intelligent communication anti‐jamming. Additionally, it is aimed to provide a comprehensive overview of the developmental process and current state of research, thereby furnishing conceptual and theoretical support for the design of more effective and practical intelligent communication anti‐jamming systems.

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Cross-Layer Anti-Jamming Scheme: A Hierarchical Learning Approach

Cited by 19 publications

References 23 publications

Multi-regional Anti-jamming Communication Scheme Based on Transfer Learning and Q Learning

Multi-regional Anti-jamming Communication Scheme Based on Transfer Learning and Q Learning

An Anti-Jamming Hierarchical Optimization Approach in Relay Communication System via Stackelberg Game

From Adaptive Communication Anti‐Jamming to Intelligent Communication Anti‐Jamming: 50 Years of Evolution

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