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

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 →

• arXiv
• graph neural network
• low Earth orbit
• Multi-objective reinforcement learning
• NTN-WPT

• paper
• infra

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