English(EN) ML-Based Hierarchical Prediction for Practical Energy Scheduling in Dynamic NTN-WPT Systems

机器学习方法优化卫星到设备能量调度的方案

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-11 04:00

研究人员开发了一种新颖的基于机器学习的能量调度方法，用于动态非地面网络-无线能量传输（NTN-WPT）系统。该方法旨在优化低地球轨道卫星向用户设备传输能量的能效、任务完成率和任务等待时间。该系统采用三层预测框架，利用图神经网络进行能量传输效率建模，并采用多目标强化学习来平衡相互竞争的目标。 AI

影响这项研究可以提高移动设备的无线能量传输系统的效率和可靠性，尤其是在陆地基础设施有限的地区。

排序理由学术论文，详细介绍了用于NTN-WPT系统的能量调度的创新机器学习方法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Zhanyu Ju, Wenchi Cheng · 2026-08-11 04:00

面向动态NTN-WPT系统中实际能量调度的基于机器学习的分层预测

arXiv:2608.08804v1 Announce Type: cross Abstract: With advancements in long-distance wireless power transfer (WPT) and space-based energy technologies, integrating WPT into non-terrestrial networks (NTNs), referred to as NTN-WPT, is emerging as a promising approach for next-gener…