Low Earth Orbit (LEO) satellite networks are favored for their global coverage, low latency, and geographical flexibility, but they also face challenges of high dynamism in network topology and congestion control. To address these issues, this study introduces an improved BBR congestion control algorithm (Deep-BBR) based on traffic prediction. The algorithm integrates Long Short-Term Memory networks, Graph Convolutional Networks, and attention mechanisms to accurately predict bandwidth utilization and dynamically adjust pacing gains. Experimental results show that the Deep-BBR algorithm significantly outperforms the traditional BBR algorithm in effective throughput, latency, and in-flight data management, effectively enhancing the communication performance and stability of LEO satellite networks. This improvement offers a more efficient and intelligent solution for congestion control in LEO satellite networks.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    An Improved BBR Algorithm with Adaptive Congestion Control for LEO Satellite Networks


    Contributors:
    Wang, Zhiyi (author) / Zhao, Ya (author) / Han, Wei (author) / Jin, Fan (author) / Lan, Lixin (author) / Lu, Haitao (author) / Chen, Chen (author)


    Publication date :

    2024-05-31


    Size :

    1259597 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Improved explicit congestion notification for satellite networks

    Durresi, Arjan / Sridharan, Mukundan / Liu, Chunlei et al. | SPIE | 2001





    Algorithm improved congestion propagation analysis method

    GAO YUCHAO / ZHAO ZEYUAN / ZHANG XING et al. | European Patent Office | 2022

    Free access