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For the large-scale disaster scenario, this paper investigates a hybrid satellite-aerial-terrestrial network (HSATN) to provide computing services for ground user equipment (GUE). The optimization problem is modeled to maximize the quality of experience (QoE) for all GUEs. To solve this problem, a multi-user game computation offloading (MUGCO) strategy is proposed. Firstly, the Initialized offloading decision is given according to the priority of the tasks. Then, the cooperative game algorithm and the task priority-based resource competition (TPRC) algorithm are used to allocate subcarrier resources and computing resources for the offloaded tasks, respectively. Finally, the offloading decision problem is proved as a potential game process. According to the properties of the potential game, the offloading decision under the Nash equilibrium is found. The simulation results show that the proposed strategy can effectively improve the total QoE value of the system compared to the comparison strategies.

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A Computation Offloading Strategy for the Large-Scale Disaster Scenario

  • 会议论文

  • 发表日期：

    2025年

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  • Bo Wang,
  • Xu Wang,
  • Dongyan Huang

摘要

For the large-scale disaster scenario, this paper investigates a hybrid satellite-aerial-terrestrial network (HSATN) to provide computing services for ground user equipment (GUE). The optimization problem is modeled to maximize the quality of experience (QoE) for all GUEs. To solve this problem, a multi-user game computation offloading (MUGCO) strategy is proposed. Firstly, the Initialized offloading decision is given according to the priority of the tasks. Then, the cooperative game algorithm and the task priority-based resource competition (TPRC) algorithm are used to allocate subcarrier resources and computing resources for the offloaded tasks, respectively. Finally, the offloading decision problem is proved as a potential game process. According to the properties of the potential game, the offloading decision under the Nash equilibrium is found. The simulation results show that the proposed strategy can effectively improve the total QoE value of the system compared to the comparison strategies.