Intelligent Decision-making for Selection of Communication Jamming Channel and Power

ZHOU Cheng; LIN Qian; MA Congshan; YING Tao; MAN Xin

doi:10.11999/JEIT240100

Volume 46 Issue 10

Oct. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(10): 3957-3965

ZHOU Cheng, LIN Qian, MA Congshan, YING Tao, MAN Xin. Intelligent Decision-making for Selection of Communication Jamming Channel and Power[J]. Journal of Electronics & Information Technology, 2024, 46(10): 3957-3965. doi: 10.11999/JEIT240100

Citation:

ZHOU Cheng, LIN Qian, MA Congshan, YING Tao, MAN Xin. Intelligent Decision-making for Selection of Communication Jamming Channel and Power[J]. Journal of Electronics & Information Technology, 2024, 46(10): 3957-3965. doi: 10.11999/JEIT240100

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Intelligent Decision-making for Selection of Communication Jamming Channel and Power

doi: 10.11999/JEIT240100

College of Electronic Engineering, Naval University of Engineering, Wuhan 430033, China

Funds: The National Natural Science Foundation of China (61501484)

Received Date: 2024-02-26
Rev Recd Date: 2024-10-01

Available Online: 2024-10-09

Publish Date: 2024-10-30

Abstract

Abstract

Intelligent jamming is a technique that utilizes environmental feedback information and autonomous learning of jamming strategies to effectively disrupt the communication links of the enemy. However, most existing research on intelligent jamming assumes that jammers can directly access the feedback of communication quality indicators, such as bit error rate or packet loss rate. This assumption is difficult to achieve in practical adversarial environments, thus limiting the applicability of intelligent jamming. To address this issue, the communication jamming problem is modeled as a Markov Decision Process (MDP), and by considering both the fundamental principles of jamming and the dynamic behavior of communication objectives, an Improved Policy Hill-Climbing (IPHC) algorithm is proposed. This algorithm follows an OODA loop of “Observe-Orient-Decide-Act”, continuously observes the changes of communication objectives in real time, flexibly adjusts jamming strategies, and applies a mixed strategy decision-making to execute communication jamming. Simulation results demonstrate that when the communication objectives adopt deterministic evasion strategies, the proposed algorithm can quickly converge to the optimal jamming strategy, and the convergence time is at least two-thirds shorter than that of the Q-learning algorithm. When the communication objectives switch evasion strategies, the algorithm can adaptively learn and readjust to the optimal jamming strategy. In the case of communication objectives using mixed evasion strategies, the proposed algorithm also achieves fast convergence and obtains superior jamming effects.
- Intelligent Jamming,
- Jamming Effectiveness Measure,
- Mixed Strategy,
- Improved Policy Hill-Climbing Algorithm

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References(21)

References

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