Toward Proactive RF Charging Scheduling: Generative AI for Decision Support

2026-06-09

AI Summary

This work addresses the challenges of efficient energy allocation in large-scale radio-frequency wireless power transfer (RF-WPT) systems, where schedulers operate under limited resources, incomplete receiver information, and uncertain future charging conditions. To tackle these issues, the study introduces generative artificial intelligence (GenAI) into RF-WPT scheduling for the first time, establishing an uncertainty-aware decision-support layer that generates coarse-grained contextual scenarios to produce diverse predictions. These multi-scenario forecasts provide robust inputs for downstream scheduling algorithms. By leveraging the sampling capabilities of generative models, the approach enables risk-sensitive optimization rather than relying on single-point deterministic predictions. Experimental results in a warehouse setting demonstrate that the proposed method significantly enhances scheduling robustness compared to both deterministic and non-learning baselines, with particularly notable improvements under risk-sensitive objectives.

Abstract

Radio frequency wireless power transfer (RF-WPT) is an enabling technology for supporting uninterrupted communications in future Internet of Things systems by reducing the need for battery replacement and mitigating battery-waste-related issues. For large-scale RF-WPT deployment, one of the main challenges is the scheduler-level resource allocation. Specifically, the transmitter must decide how much energy to deliver, when, and to whom, under limited charging resources, incomplete receiver-side information, and uncertain near-future charging conditions. This article positions generative artificial intelligence (GenAI) as a promising tool for this setting because it can foresee multiple plausible charging scenarios conditioned on coarse operational context and receiver-side information. We propose GenAI to act as an uncertainty-aware support layer for the RF-WPT scheduler rather than as a standalone forecasting or decision-making tool. To this end, we first revisit the main challenges of RF-WPT scheduling, and discuss how major GenAI families can support uncertainty-aware charging decisions by generating scenario-based inputs for downstream tasks. We then present a warehouse-style case study showing that preserving uncertainty through the sampling capability of generative models can improve robust charging decisions compared with deterministic prediction and simple non-learning baselines, especially under risk-sensitive objectives. Finally, we identify key open challenges and present some directions for future research.

Problem

• RF-WPT
• resource allocation
• uncertainty
• charging scheduling
• decision support

Innovation

• Generative AI
• RF wireless power transfer
• uncertainty-aware scheduling
• scenario generation
• decision support