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동적 저궤도 위성 네트워크를 위한 Dueling DQN 기반 라우팅 기법

Dueling DQN-based Routing for Dynamic LEO Satellite Networks

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초록

This paper deals with a routing algorithm which can find the best communication route to a desired point considering disconnected links in the LEO (low earth orbit) satellite networks. If the LEO satellite networks are dynamic, the number and distribution of the disconnected links are varying, which makes the routing problem challenging. To solve the problem, in this paper, we propose a routing method based on Dueling DQN which is one of the reinforcement learning algorithms. The proposed method was successfully conducted and verified by showing improved performance by reducing convergence times and converging more stably compared to other existing reinforcement learning-based routing algorithms.

Abstract

This paper deals with a routing algorithm which can find the best communication route to a desired point considering disconnected links in the LEO (low earth orbit) satellite networks. If the LEO satellite networks are dynamic, the number and distribution of the disconnected links are varying, which makes the routing problem challenging. To solve the problem, in this paper, we propose a routing method based on Dueling DQN which is one of the reinforcement learning algorithms. The proposed method was successfully conducted and verified by showing improved performance by reducing convergence times and converging more stably compared to other existing reinforcement learning-based routing algorithms.

참고문헌 (17)

  1. B. S. Roh, "A Study on the Reinforcement Learning Routing for LEO Satellite Network", Proceedings of the Korean Institute of Communication Sciences Conference, pp.537~538 (2022)
  2. J. H. Lee, "Study on the Low-earth Orbit Satellite Based Non-terrestrial Network Systems Via Deep-reinforcement Learning", Proceedings of the Korean Institute of Communication Sciences Conference, pp.1306~1307 (2021)
  3. X. Wang, "LEO Satellite Network Routing Algorithm Based on Reinforcement Learning", 2021 IEEE 4th International Conference on Electronics Technology (ICET), pp.1105~1109 (2021), IEEE
  4. P. Zuo, "An Intelligent Routing Algorithm for Leo Satellites Based on Deep Reinforcement Learning", 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall), pp.1~5 (2021), IEEE
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  11. Z. Wang, "Dueling Network Architectures for Deep Reinforcement Learning", International Conference on Machine Learning, pp.1995~2003 (2016), PMLR
  12. V. Mnih, "Human-level Control Through Deep Reinforcement Learning", Nature, 518(7540), pp.529~533 (2015)
  13. J. H. Baek, "A Study about Application of Indoor Autonomous Driving for Obstacle Avoidance Using Atari Deep Q Network Model", Proceedings of the Korea Information Processing Society Conference, pp.715~718 (2018), Korea Information Processing Society
  14. T. P. Lillicrap, "Continuous Control with Deep Reinforcement Learning", ~ (2015)
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  17. J. Schulman, "Proximal Policy Optimization Algorithms", ~ (2017)

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