UAV-RIS Enhanced Wireless Power Transfer for Urban IoT Networks

• Mohammed Aboud Kadhim,
• Ahmed A. Thabit,

Vol. 19, No. 10, October 31, 2025
10.3837/tiis.2025.10.011, Download Paper (Free):

• UAV
• RIS
• wireless power transfer
• Internet of Things
• Urban Networks
• Energy Harvesting
• Deep Reinforcement Learning

Abstract

In this paper, we introduce a new framework for urban Wireless Powered Communication Networks (WPCN) empowered by Unmanned Aerial Vehicle (UAV)-assisted Reconfigurable Intelligent Surfaces (RIS). The utilization of UAV can be employed to greatly improve the deployment and operation of WPCN in dense urban spaces especially the guarantees of good communication and energy transference for the Internet of Things IoT devices. However, low-rise buildings and variable node density in dense urban areas frequently make wireless energy transfer inefficient. To deal with these issues, we design a joint Unmanned Aerial Vehicle-Reconfigurable Intelligent UAV-RIS system by combining the UAV on-demand deployment and RIS signal modulation to enhance Wireless Power Transfer WPT performance. Our full stack simulator features cognitive spectrum sensing, urban mobility and hierarchical multi-UAV coordination. Adopting a harvest-then-transmit scheme, IoT devices exploit the harvested energy to transmit data under Time Division Multiple Access TDMA scheduling. Deep Reinforcement Learning DR-RLIs achieve joint optimization of UAV trajectory and RIS phase shifts. Simulation results show that the proposed UAV-RIS-assisted scheme can outperform conventional schemes by attaining 40% performance improvement in energy harvesting and 30% increase in throughput as systems with no aid of RIS. In the complex urban environment, the presented work proposes an energy-efficient IoT networking solution with novel WPT technologies when traditional WPT methods are inapplicable.

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