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Reinforcement Learning Based UAV Trajectory and Power Control Against Jamming

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

Abstract

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.

Keywords

Unmanned aerial vehicle Jamming Trajectory control Power control Reinforcement learning 

Notes

Acknowledgements

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).

References

  1. 1.
    Bhattacharya, S., Başar, T.: Game-theoretic analysis of an aerial jamming attack on a UAV communication network. In: Proceedings of the American Control Conference, Baltimore, MD, pp. 818–823, June/July 2010Google Scholar
  2. 2.
    Bhunia, S., Sengupta, S.: Distributed adaptive beam nulling to mitigate jamming in 3D UAV mesh networks. In: Proceedings of the IEEE International Conference on Computing Networking Communication (ICNC), Santa Clara, CA, pp. 120–125, January 2017Google Scholar
  3. 3.
    Gwon, Y., Dastangoo, S., Fossa, C., Kung, H.: Competing mobile network game: Embracing antijamming and jamming strategies with reinforcement learning. In: Proceedings of the IEEE Conference on Communication Network Security (CNS), National Harbor, MD, pp. 28–36, October 2013Google Scholar
  4. 4.
    Han, G., Xiao, L., Poor, H.V.: Two-dimensional anti-jamming communication based on deep reinforcement learning. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), New Orleans, LA, March 2017Google Scholar
  5. 5.
    Kingston, D., Rasmussen, S., Humphrey, L.: Automated UAV tasks for search and surveillance. In: IEEE Conference on Control Application (CCA), Buenos Aires, Argentina, pp. 1–8, September 2016Google Scholar
  6. 6.
    Lv, S., Xiao, L., Hu, Q., Wang, X., Hu, C., Sun, L.: Anti-jamming power control game in unmanned aerial vehicle networks. In: Proceedings of the IEEE Global Communication Conference (GLOBECOM), Singapore, pp. 1–6, December 2017Google Scholar
  7. 7.
    Mnih, V., et al.: Human-level control through deep reinforcement learning. Nature 518(7540), 529–533 (2015)CrossRefGoogle Scholar
  8. 8.
    Roldán, J.J., del Cerro, J., Barrientos, A.: A proposal of methodology for multi-UAV mission modeling. In: Proceedings of the IEEE Mediterranean Conference on Control Automation (MED), Torremolinos, Spain, pp. 1–7, June 2015Google Scholar
  9. 9.
    Shin, H., Choi, K., Park, Y., Choi, J., Kim, Y.: Security analysis of FHSS-type drone controller. In: Kim, H., Choi, D. (eds.) WISA 2015. LNCS, vol. 9503, pp. 240–253. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-31875-2_20CrossRefGoogle Scholar
  10. 10.
    Sun, R., Matolak, D.W.: Air–ground channel characterization for unmanned aircraft systems part II: Hilly and mountainous settings. IEEE Trans. Veh. Technol. 66(3), 1913–1925 (2017)CrossRefGoogle Scholar
  11. 11.
    Xiao, L., Li, Y., Dai, C., Dai, H., Poor, H.V.: Reinforcement learning-based NOMA power allocation in the presence of smart jamming. IEEE Trans. Veh. Technol. 67(4), 3377–3389 (2018)CrossRefGoogle Scholar
  12. 12.
    Xiao, L., Xie, C., Min, M., Zhuang, W.: User-centric view of unmanned aerial vehicle transmission against smart attacks. IEEE Trans. Veh. Technol. 67(4), 3420–3430 (2018)CrossRefGoogle Scholar
  13. 13.
    Xu, Y., et al.: A one-leader multi-follower Bayesian-Stackelberg game for anti-jamming transmission in UAV communication networks. IEEE Access 6, 21697–21709 (2018)CrossRefGoogle Scholar
  14. 14.
    Zhang, G., Wu, Q., Cui, M., Zhang, R.: Securing UAV communications via joint trajectory and power control. IEEE Trans. Wirel. Commun. 18(2), 1376–1389 (2019)CrossRefGoogle Scholar

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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