With the widespread use of satellite communications in low earth orbit (LEO), satellite routing has become an important component in ensuring that information can be transmitted efficiently and reliably. However, the time-varying topology and unstable nature of inter-satellite links (ISLs) make it challenging to design LEO satellite routing that guarantees low end-to-end latency. In this paper, we formulate this problem as an optimization problem that aims to minimize the end-to-end latency. We propose a Two-Hop Sate-Aware and Dijkstra-Aided Deep Reinforcement Learning Satellite Routing method (SDDRL-SR) which utilizes Dijkstra's algorithm for supervised training to accelerate the convergence of the algorithm. In the online execution phase, we design a Two-Hop Convergence method that updates the routing strategy by predicting the future two-hop state of the data. Experimental results show that the proposed SDDRL-SR outperforms the Deep Q-Network (DQN), Dijkstra and random routing in the performance of average delay and packet arriving ratio.


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    Title :

    SDDRL-SR: A High-Reliability Satellite Routing Algorithm Based on Deep Reinforcement Learning


    Contributors:
    Ding, Zhao-Long (author) / Liu, Hui-Jie (author) / Tian, Feng (author) / Yang, Zi-Jian (author)


    Publication date :

    2024-06-24


    Size :

    654947 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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