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Energy-Efficient UAV-Mounted RIS for IoT: A Hybrid Energy Harvesting and DRL Approach

Mahmoud M. Salim, Khaled M. Rabie, Ali H. Muqaibel

TL;DR

The paper tackles the critical issue of limited on-board energy in UAV-mounted RIS for IoT by proposing a dual energy harvesting framework that combines RF EH protocols (ES, TS, PS) with solar energy. It introduces a hybrid ES-TS-PS scheme and a DRL-based optimization (DDPG-EH) to jointly optimize UAV trajectory, RIS phase shifts, and EH scheduling under CSI imperfections and hardware impairments. The approach is formulated as a Markov decision process and employs enhanced TD3/DDPG techniques with clipping and softmax-based Q-value estimation to maximize EH efficiency while meeting QoS constraints. Results show the hybrid EH strategy with DRL yields superior EH efficiency and robust performance against baselines, underscoring the potential of energy-aware UAV-RIS networks for resilient 6G IoT deployments, including disaster response and remote connectivity.

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.

Energy-Efficient UAV-Mounted RIS for IoT: A Hybrid Energy Harvesting and DRL Approach

TL;DR

The paper tackles the critical issue of limited on-board energy in UAV-mounted RIS for IoT by proposing a dual energy harvesting framework that combines RF EH protocols (ES, TS, PS) with solar energy. It introduces a hybrid ES-TS-PS scheme and a DRL-based optimization (DDPG-EH) to jointly optimize UAV trajectory, RIS phase shifts, and EH scheduling under CSI imperfections and hardware impairments. The approach is formulated as a Markov decision process and employs enhanced TD3/DDPG techniques with clipping and softmax-based Q-value estimation to maximize EH efficiency while meeting QoS constraints. Results show the hybrid EH strategy with DRL yields superior EH efficiency and robust performance against baselines, underscoring the potential of energy-aware UAV-RIS networks for resilient 6G IoT deployments, including disaster response and remote connectivity.

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.

Paper Structure

This paper contains 27 sections, 6 figures.

Table of Contents

  1. Introduction
  2. System Architecture and Operating Protocols
  3. System Architecture
  4. Operating Protocols
  5. Application Scenarios for UAV-Mounted RIS
  6. Physical Layer Security (PLS)
  7. Public Safety and Disaster Response
  8. Vehicle-to-Everything (V2X) Communication
  9. UAV Swarm Coordination
  10. Remote and Maritime Connectivity
  11. Design Challenges of UAV-Mounted RIS Networks
  12. Energy and Resource Management
  13. Trajectory Optimization
  14. Practical Aspects and System Impairments
  15. Computational Complexity and Optimization Challenges
  16. ...and 12 more sections

Figures (6)

  • Figure 1: Envisioned future 6G-enabled IoT networks with UAV-mounted RIS.
  • Figure 2: Proposed UAV-mounted RIS framework (a) system architecture (b) RF EH operating protocols.
  • Figure 3: Proposed DDPG-EH framework for solving UAV-mounted RIS optimization problem.
  • Figure 4: The reward as EH efficiency vs the number of training episodes.
  • Figure 5: EH efficiency per testing step.
  • ...and 1 more figures