The Low Earth Orbit (LEO) satellite constellation has been recognized as an important component of the future 6G network. However, due to the high-speed movement of LEO satellites and the potential for link failures, achieving optimal satellite communication performance with a static inter-satellite links (ISLs) scheme is challenging. To solve this problem, this paper proposes an ISL re-planning algorithm with considering link failures based on multi-agent deep reinforcement learning (named ReISL). In ReISL, a multi-objective optimization problem is formulated to maximize the system capacity while minimizing the link switching costs. Then, multi-agent deep reinforcement learning is employed to derive the optimal ISL re-planning schemes, where each satellite utilizes Double Deep Q-Network (DDQN). Finally, extensive experiments are carried out and the results demonstrate that our proposed algorithm ReISL can outperform the baseline algorithms.


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    Titel :

    Inter-Satellite Link Re-Planning Algorithm under Link Failures of LEO Satellite Constellations


    Beteiligte:
    Chen, Kaixin (Autor:in) / Ran, Yongyi (Autor:in) / Xia, Shaohua (Autor:in) / Luo, Jiangtao (Autor:in) / Chen, Shuangwu (Autor:in)


    Erscheinungsdatum :

    07.10.2024


    Format / Umfang :

    1136094 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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