Reinforcement Learning Based UAV Trajectory and Power Control Against Jamming

  • Zihan Lin
  • Xiaozhen Lu
  • Canhuang Dai
  • Geyi Sheng
  • Liang XiaoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11806)


Unmanned aerial vehicles (UAVs) are vulnerable to jamming attacks that aim to interrupt the communications between the UAVs and ground nodes and to prevent the UAVs from completing their sensing duties. In this paper, we design a reinforcement learning based UAV trajectory and power control scheme against jamming attacks without knowing the ground node and jammer locations, the UAV channel model and jamming model. By evaluating the UAV transmission quality obtained from the feedback channel and the UAV channel condition, this scheme uses reinforcement learning to choose the UAV trajectory and transmit power based on the UAV location, signal-to-interference-and-noise ratio of the previous sensing data signal received by the ground node, and the radio channel state. Simulation results show that this scheme improves the quality of service of the UAV sensing duty given the required UAV waypoints and saves the UAV energy consumption.


Unmanned aerial vehicle Jamming Trajectory control Power control Reinforcement learning 



This paper was in part supported by the National Natural Science Foundation of China (Grants No. 61671396 and No. 61971366), the Natural Science Foundation of Fujian Province, China (Grant No. 2019J01843), the open research fund of National Mobile Communications Research Laboratory, Southeast University (No. 2018D08) and the Fundamental Research Funds for the Central Universities of China (No. 20720190034).


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Zihan Lin
    • 1
  • Xiaozhen Lu
    • 1
  • Canhuang Dai
    • 1
  • Geyi Sheng
    • 1
  • Liang Xiao
    • 1
    • 2
    Email author
  1. 1.Department of Communication EngineeringXiamen UniversityXiamenChina
  2. 2.National Mobile Communications Research LaboratorySoutheast UniversityNanjingChina

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