한국정보통신학회논문지 (Journal of the Korea Institute of Information and Communication Engineering)

  • 제25권9호
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  • Pages.1267-1270
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  • 2021
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  • 2234-4772(pISSN)
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  • 2288-4165(eISSN)
한국정보통신학회 (The Korea Institute of Information and Commucation Engineering)
권호 표지
DOI QR코드

DOI QR Code

MANET에서 종단간 통신지연 최소화를 위한 심층 강화학습 기반 분산 라우팅 알고리즘

Deep Reinforcement Learning-based Distributed Routing Algorithm for Minimizing End-to-end Delay in MANET

  • Choi, Yeong-Jun (Department of Information and Communication Engineering, Pukyong National University) ;
  • Seo, Ju-Sung (Department of Information and Communication Engineering, Pukyong National University) ;
  • Hong, Jun-Pyo (Department of Information and Communication Engineering, Pukyong National University)
  • 투고 : 2021.07.13
  • 심사 : 2021.08.12
  • 발행 : 2021.09.30
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초록

In this paper, we propose a distributed routing algorithm for mobile ad hoc networks (MANET) where mobile devices can be utilized as relays for communication between remote source-destination nodes. The objective of the proposed algorithm is to minimize the end-to-end communication delay caused by transmission failure with deep channel fading. In each hop, the node needs to select the next relaying node by considering a tradeoff relationship between the link stability and forward link distance. Based on such feature, we formulate the problem with partially observable Markov decision process (MDP) and apply deep reinforcement learning to derive effective routing strategy for the formulated MDP. Simulation results show that the proposed algorithm outperforms other baseline schemes in terms of the average end-to-end delay.

키워드

과제정보

This paper was supported by a Research Grant of Pukyong National University(2019)

참고문헌

  1. M. Conti and S. Giordano, "Mobile ad hoc networking: Milestones, challenges, and new research directions," IEEE Commun. Mag., vol. 52, no. 1, pp. 85-96, Jan. 2014. https://doi.org/10.1109/MCOM.2014.6710069
  2. N. Singh and L. Shrivastava, "Impact of Antenna model with the variation of speed for Reactive and Hybrid routing protocols in Mobile Ad-Hoc Networks," Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, vol. 5, no. 5, pp. 17-27, Oct. 2015. https://doi.org/10.14257/AJMAHS.2015.10.30
  3. R. Li, F. Li, X. Li, and Y. Wang, "Qgrid : Q-learning based routing protocol for vehicular ad hoc networks," in Proc. IEEE IPCCC, pp. 1-8, Dec. 2014.
  4. A. Ghaffari, "Real-time routing algorithm for mobile ad hoc networks using reinforcement learning and heuristic algorithms," in Wireless Networks, pp. 703-714, vol. 23, no. 3, 2017. https://doi.org/10.1007/s11276-015-1180-0
  5. W. S. Jung, J. Yim, and Y. B. Ko, "QGeo: Q-learning based geograpthic ad hoc routing protocol for unmanned robotic networks," in IEEE Commun. Lett. vol. 21, no. 10, pp. 2258-2261, Oct. 2017. https://doi.org/10.1109/LCOMM.2017.2656879
  6. J. Liu, Q. Wang, C. He, K. Jaffres-Runser, Y. Xu, Z. Li, and Y. Xu, "QMR: Q-learning based multi-objective optimization routing protocol for flying ad hoc networks," Comput. Commun., vol. 150, pp. 304-316, Jan. 2020. https://doi.org/10.1016/j.comcom.2019.11.011
  7. K. Lee and J. P. Hong, "Device-to-device communication power control technique for ensuring communication security of cellular system," J. Korea Inst. Inf. Commun. Eng., vol. 21, no. 6, pp. 1100-1105, Jun. 2017. https://doi.org/10.6109/jkiice.2017.21.6.1100
  8. R. S. Sutton and A. G. Barto, Reinforcement learning: An introduction. Cambridge, MA: MIT Press, 2018.
  9. V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller, "Playing Atari with deep reinforcement learning," in arXiv preprint arxiv:1312.5602, 2013.