基于决策变压器的高效数据卸载在LEO-IoT中
Pengcheng Xia1, Mengfei Zang2, Jie Zhao3
1School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
Entropy (Basel, Switzerland)
|October 25, 2024
概括
本研究介绍了使用低地球轨道 (LEO) 卫星和移动边缘计算 (MEC) 的物联网 (IoT) 有效的数据卸载机制. 与传统方法相比,决策变压器 (DT) 显著提高了卸载速度和性能.
科学领域:
- 计算机科学 计算机科学
- 航空航天工程 航空航天工程
- 电信 电信服务 电信服务 电信服务
背景情况:
- 物联网 (IoT) 应用正在扩展,但受到地面计算资源稀缺的限制.
- 低地球轨道 (LEO) 卫星为物联网任务卸载到移动边缘计算 (MEC) 服务器提供更广泛的覆盖范围和更低的延迟.
- 在地面物联网设备和LEO卫星之间有效地共享带宽和电源是一个重大挑战.
研究的目的:
- 为LEO卫星物联网 (LEO-IoT) 系统开发一个高效的数据卸载机制.
- 通过优化LEO卫星选择和通信资源分配来最大限度地降低数据卸载延迟和能源消耗.
- 利用先进的AI技术在LEO-IoT环境中解决复杂的优化问题.
主要方法:
- 在LEO-IoT架构中探索一个高效的数据卸载机制,LEO卫星将数据转发到MEC服务器.
- 最佳选择转发LEO卫星,并为每个物联网任务分配通信资源.
- 应用决策转换器 (DT) 模型,涉及特定任务的预培训和微调,以解决优化问题.
主要成果:
- 决策转换器 (DT) 模型显示的趋同速度是近接政策优化 (PPO) 的三倍.
- 与传统的强化学习方法相比,DT可以实现高达30%的绩效改善.
- 提出的基于DT的方法有效地解决了资源共享的挑战,并优化了LEO-IoT中的数据卸载.
结论:
- 决策转换器 (DT) 提供了一个高效和高性能解决方案,用于优化LEO卫星物联网网络中的数据卸载.
- DT模型的快速融合和卓越的性能比传统的强化学习方法有了显著的进步.
- 这项研究通过克服资源限制,为LEO-IoT的增强能力和更广泛应用铺平了道路.
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