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ML approach optimizes energy scheduling for satellite-to-device power transfer

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel machine learning-based approach for energy scheduling in dynamic Non-Terrestrial Network-Wireless Power Transfer (NTN-WPT) systems. This method aims to optimize energy efficiency, task completion rates, and task waiting times for power transfer from low Earth orbit satellites to user devices. The system employs a three-layer predictive framework, utilizing a graph neural network for energy transfer efficiency modeling and multi-objective reinforcement learning to balance competing objectives. AI

IMPACT This research could improve the efficiency and reliability of wireless power transfer systems for mobile devices, particularly in areas with limited terrestrial infrastructure.

RANK_REASON Academic paper detailing a novel ML-based approach for energy scheduling in NTN-WPT systems. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

ML approach optimizes energy scheduling for satellite-to-device power transfer

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Academic paper detailing a novel ML-based approach for energy scheduling in NTN-WPT systems. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Zhanyu Ju, Wenchi Cheng ·

    ML-Based Hierarchical Prediction for Practical Energy Scheduling in Dynamic NTN-WPT Systems

    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…

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