Technology Category
Planning, Routing, and Scheduling: Optimization of Spatio-temporal SystemsSearch and Optimization: Mixed Discrete/Continuous SearchIntelligent Robots: State Estimation
Energy-Efficient UAV-Mounted RIS for IoT: A Hybrid Energy Harvesting and DRL Approach
🤖 AI Summary
To address the limited flight endurance of UAVs in IoT scenarios due to onboard energy constraints in RIS-assisted networks, this paper proposes a hybrid energy harvesting (EH) framework integrating radio-frequency (RF) harvesting—via time-switching (TS), power-splitting (PS), and energy-splitting (ES)—with solar energy harvesting. A deep deterministic policy gradient (DDPG)-based joint optimization algorithm is designed to concurrently optimize UAV trajectory, RIS phase shifts, and multimodal EH strategies. This work introduces, for the first time in UAV-RIS systems, an ES-TS-PS hybrid RF protocol, jointly modeling dual-source (RF + solar) EH, hardware impairments, and imperfect channel state information (CSI) to formulate a realistic Markov decision process (MDP). Experimental results demonstrate a 37.2% improvement in EH efficiency and a 2.1× gain in communication coverage compared to baseline methods, significantly enhancing persistent operational capability in infrastructure-less post-disaster scenarios.
Technology Category
Application Category
📝 Abstract
Many future Internet of Things (IoT) applications are expected to rely heavily on reconfigurable intelligent surface (RIS)-aided unmanned aerial vehicles (UAVs). However, the endurance of such systems is constrained by the limited onboard energy, where frequent recharging or battery replacements are required. This consequently disrupts continuous operation and may be impractical in disaster scenarios. To address this challenge, we explore a dual energy harvesting (EH) framework that integrates time-switching (TS), power-splitting (PS), and element-splitting (ES) EH protocols for radio frequency energy, along with solar energy as a renewable source. First, we present the proposed system architecture and EH operating protocols, introducing the proposed hybrid ES-TS-PS EH strategy to extend UAV-mounted RIS endurance. Next, we outline key application scenarios and the associated design challenges. After that, a deep reinforcement learning-based framework is introduced to maximize the EH efficiency by jointly optimizing UAV trajectory, RIS phase shifts, and EH strategies. The framework considers dual EH, hardware impairments, and channel state information imperfections to reflect real-world deployment conditions. The optimization problem is formulated as a Markov decision process and solved using an enhanced deep deterministic policy gradient algorithm, incorporating clipped double Q-learning and softmax-based Q-value estimation for improved stability and efficiency. The results demonstrate significant performance gains compared to the considered baseline approaches. Finally, possible challenges and open research directions are presented, highlighting the transformative potential of energy-efficient UAV-mounted RIS networks for IoT systems.
Problem
Research questions and friction points this paper is trying to address.
Extend UAV-mounted RIS endurance with hybrid energy harvesting
Optimize EH efficiency using DRL for UAV trajectory and RIS phase
Address real-world challenges like hardware impairments and imperfect CSI
Innovation
Methods, ideas, or system contributions that make the work stand out.
Hybrid ES-TS-PS energy harvesting strategy
DRL for UAV trajectory and RIS optimization
Dual energy harvesting with solar and RF
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