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

Feng, Zhibin; Ren, Guochun; Chen, Jin; Chen, Chaohui; Yang, Xiaoqin; Luo, Yijie; Xu, Kun

doi:10.3390/app9163348

Cited by 9 publications

(6 citation statements)

References 46 publications

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“…The solution is found by jamming-free channel selection by stochastic learning. The authors in [21] formulate the anti-jamming process as a Stackelberg game, in which the user acts as a leader, and the disruptor chooses a power strategy and then acts as a follower. As an anti-jamming process, a joint optimization algorithm has been developed using the Q-learning algorithm.…”

Section: Related Workmentioning

confidence: 99%

A Model-Free Cognitive Anti-Jamming Strategy Using Adversarial Learning Algorithm

Sudha

2022

Cybernetics and Information Technologies

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Modern networking systems can benefit from Cognitive Radio (CR) because it mitigates spectrum scarcity. CR is prone to jamming attacks due to shared communication medium that results in a drop of spectrum usage. Existing solutions to jamming attacks are frequently based on Q-learning and deep Q-learning networks. Such solutions have a reputation for slow convergence and learning, particularly when states and action spaces are continuous. This paper introduces a unique reinforcement learning driven anti-jamming scheme that uses adversarial learning mechanism to counter hostile jammers. A mathematical model is employed in the formulation of jamming and anti-jamming strategies based on deep deterministic policy gradients to improve their policies against each other. An open-AI gym-oriented customized environment is used to evaluate proposed solution concerning power-factor and signal-to-noise-ratio. The simulation outcome shows that the proposed anti-jamming solution allows the transmitter to learn more about the jammer and devise the optimal countermeasures than conventional algorithms.

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Section: Related Workmentioning

confidence: 99%

A Model-Free Cognitive Anti-Jamming Strategy Using Adversarial Learning Algorithm

Sudha

2022

Cybernetics and Information Technologies

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“…In Reference 17, based on Reference 16, relay selection and beam former design were considered simultaneously to solve the influence of jamming signals on control message in the vehicle network. The authors in Reference 3 studied single‐user and multi‐relay scenario, with only one RN assisting users to communicate at one time. This reference involved the power optimization of users at all levels and selection optimization of the relay.…”

Section: Related Workmentioning

confidence: 99%

“…The traditional relay selection scheme may not be able to achieve anti‐jamming communication. The most related work to this article is Reference 3. The authors in Reference 3 studied the joint optimization of relay selection and power control against anti‐jamming.…”

Section: Introductionmentioning

confidence: 99%

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Joint relay and channel selection in relay‐aided anti‐jamming system: A reinforcement learning approach

Huang

Xu²,

Chen

et al. 2021

Trans Emerging Tel Tech

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In this article, a joint relay and channel selection problem is investigated in multi‐relay anti‐jamming communication system. Considering the jamming pattern and the relay node (RN) location distribution are unknown, the relay and channel selection problem is formulated as Markov decision processes (MDPs). Different from the existing research on anti‐jamming communication, in this article, the source node (SN) and all RNs are considered as agents who collaboratively learn the environment and make anti‐jamming decisions. A reinforcement‐learning‐based joint relay and channel selection method is proposed to achieve relay‐aided anti‐jamming communication. Specifically, the SN tries to make the optimal selection of relay which is under the least jamming threat, while the RN will access the jamming‐free channels. Various simulation results show that the algorithm can quickly learn the unknown changing pattern of the jamming environment, and make the effective joint relay and channel decisions to obtain high communication throughput which is close to the optimal.

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“…Network attack and defense game models include static games, dynamic games, and evolutionary games. Based on static game models, Feng et al [15] and Jia et al [16] studied the game problem against anti-jamming attacks and eavesdropping attacks and proposed a deception defense strategy based on the Stackelberg game. Yuan et al [17] and Li et al [18] studied interactive decision-making between defenders and attackers and analyzed the optimal solution for the decision under different types of budget constraints.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-Player Evolutionary Game of Network Attack and Defense Based on System Dynamics

2021

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We formalize the adversarial process between defender and attackers as a game and study the non-cooperative evolutionary game mechanism under bounded rationality. We analyze the long-term dynamic process between the attacking and defending parties using the evolutionary stable strategies derived from the evolutionary game model. First, we construct a multi-player evolutionary game model consisting of a defender and multiple attackers, formally describe the strategies, and construct a three-player game payoff matrix. Then, we propose two punishment schemes, i.e., static and dynamic ones. Moreover, through the combination of mathematical derivation with simulation, we obtain the evolutionary stable strategies of each player. Different from previous work, in this paper, we consider the influence of strategies among different attackers. The simulation shows that (1) in the static punishment scheme, increasing the penalty can quickly control the occurrence of network attacks in the short term; (2) in the dynamic punishment scheme, the game can be stabilized effectively, and the stable state and equilibrium values are not affected by the change of the initial values.

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

Cited by 9 publications

References 46 publications

A Model-Free Cognitive Anti-Jamming Strategy Using Adversarial Learning Algorithm

A Model-Free Cognitive Anti-Jamming Strategy Using Adversarial Learning Algorithm

Joint relay and channel selection in relay‐aided anti‐jamming system: A reinforcement learning approach

Multi-Player Evolutionary Game of Network Attack and Defense Based on System Dynamics

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