任务卸载和资源分配策略在非地面网络中,用于连续分布式任务场景.
Yueming Qi1, Yu Du2, Yijun Guo1
1Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Sensors (Basel, Switzerland)
|October 16, 2025
概括
这项研究引入了一个新的云端架构用于非地面网络,优化任务卸载6G和物联网. 一个新的深度强化学习算法通过高效地管理资源,大大降低了系统成本.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 电信 电信服务 电信服务 电信服务
背景情况:
- 非地面网络对于6G,物联网和数字化至关重要.
- 现有的任务分类方法在不断分布的任务属性上扎.
- 在边缘计算中处理定量连续任务要求方面存在差距.
研究的目的:
- 为边缘计算建模一个具有连续分布属性的多任务场景.
- 提出一个三层云端协作卸载架构.
- 通过整合无人机负载平衡和卫星能效来最大限度地降低系统成本.
主要方法:
- 开发了一个三层架构:无人机边缘节点,LEO卫星和地面云数据中心.
- 制定了一个系统成本最小化问题.
- 提出了一个双层多类型代理深度强化学习 (TMDRL) 算法,集成DQN和DPG.
主要成果:
- TMDRL算法有效地优化了任务卸载和资源分配.
- 拟议的架构和算法解决了量化连续任务要求.
- 与基线方法相比,模拟结果显示系统成本降低了7.82%.
结论:
- 开发的TMDRL算法为非地球边缘计算中的任务卸载提供了有效的解决方案.
- 三层架构提高了系统性能和成本效益.
- 这项工作有助于推进6G和无处不在的数字服务.
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