The data transmission resource scheduling problem (DTRSP) is pivotal for effectively utilizing remotely sensed Big Data. However, existing literature tends to address this challenge by separately considering ground-based or space-based antenna resources. This article introduces a paradigm, known as the space-ground integrated DTRSP (SGI-DTRSP), wherein both ground-based and space-based antennas are collaboratively scheduled for efficient data transmission back to the ground. For the first time, a mathematical integer programming model for addressing SGI-DTRSP is proposed. To tackle this problem efficiently, we present a distributed discrete Jaya (DD-Jaya) algorithm. In DD-Jaya, the entire population is divided into three subpopulations, each evolving independently through different global search strategies'—best-Jaya or random-Jaya. Additionally, an adaptive local search strategy is incorporated into DD-Jaya to enhance exploitation capacity. The quality of individuals is further improved by sharing superior evolutionary information among subpopulations. Finally, extensive simulation experiments demonstrate that DD-Jaya outperforms the existing algorithms, especially when dealing with large-scale SGI-DTRSP.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Distributed Discrete Jaya Algorithm for Space-Ground Integrated Data Transmission Resource Scheduling


    Contributors:
    Ren, Sida (author) / Feng, Yanxiang (author) / Li, Xiaoling (author) / Xing, Keyi (author) / Yang, Yikang (author)


    Publication date :

    2025-04-01


    Size :

    4124411 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English







    Cognitive Scheduling and Resource Allocation for Space to Ground Communication

    Koets, Michael A. / Blount, Justin L. / Blount, Jarred L. | IEEE | 2019