[Paper Review] UAV-Enabled Wireless Power Transfer: A Tutorial Overview

Lifeng Xie, Xiaowen Cao+2 more|arXiv (Cornell University)|Feb 27, 2021

UAV Applications and Optimization115 references4 citations

TL;DR

This tutorial proposes a trajectory design framework for UAV-enabled wireless power transfer (WPT) to maximize energy harvesting fairness across multiple ground devices (GDs). It introduces three key approaches—multi-location hovering, successive-hover-and-fly, and time-quantization-based optimization—for single-UAV WPT, extends them to multi-UAV scenarios using UAV swarming and GD clustering, and integrates WPT with wireless powered communication networks (WPCN) and mobile edge computing (MEC), jointly optimizing UAV trajectories and resource allocation under energy constraints.

ABSTRACT

Unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) has recently emerged as a promising technique to provide sustainable energy supply for widely distributed low-power ground devices (GDs) in large-scale wireless networks. Compared with the energy transmitters (ETs) in conventional WPT systems which are deployed at fixed locations, UAV-mounted aerial ETs can fly flexibly in the three-dimensional (3D) space to charge nearby GDs more efficiently. This paper provides a tutorial overview on UAV-enabled WPT and its appealing applications, in particular focusing on how to exploit UAVs' controllable mobility via their 3D trajectory design to maximize the amounts of energy transferred to all GDs in a wireless network with fairness. First, we consider the single-UAV-enabled WPT scenario with one UAV wirelessly charging multiple GDs at known locations. To solve the energy maximization problem in this case, we present a general trajectory design framework consisting of three innovative approaches to optimize the UAV trajectory, which are multi-location hovering, successive-hover-and-fly, and time-quantization-based optimization, respectively. Next, we consider the multi-UAV-enabled WPT scenario where multiple UAVs cooperatively charge many GDs in a large area. Building upon the single-UAV trajectory design, we propose two efficient schemes to jointly optimize multiple UAVs' trajectories, based on the principles of UAV swarming and GD clustering, respectively. Furthermore, we consider two important extensions of UAV-enabled WPT, namely UAV-enabled wireless powered communication networks (WPCN) and UAV-enabled wireless powered mobile edge computing (MEC).

Motivation & Objective

• Address the challenge of energy fairness in wireless power transfer (WPT) for widely distributed low-power ground devices (GDs) by leveraging UAV mobility.
• Overcome the limitations of fixed-location energy transmitters (ETs), which suffer from severe path loss and near-far fairness issues.
• Develop a 3D trajectory design framework for UAV-mounted ETs to maximize total harvested energy while ensuring fairness among GDs.
• Extend the framework to multi-UAV scenarios using UAV swarming and GD clustering for scalable, efficient coverage in large areas.
• Integrate UAV-enabled WPT with emerging applications such as wireless powered communication networks (WPCN) and wireless powered mobile edge computing (MEC), jointly optimizing trajectory and resource allocation.

Proposed method

• Propose a general trajectory design framework for single-UAV WPT using three core approaches: multi-location hovering, successive-hover-and-fly (SHF), and time-quantization-based optimization.
• Model the UAV’s 3D trajectory as a sequence of waypoints and optimize it to balance energy transfer and mobility constraints.
• Apply successive-hover-and-fly (SHF) strategy to sequentially charge GDs by hovering over each location for optimal energy transfer.
• Use time quantization to discretize the trajectory into time slots, enabling joint optimization of UAV position and transmission power per slot.
• Extend the framework to multi-UAV WPT using two schemes: UAV swarming (coordinated flight patterns) and GD clustering (grouping GDs and assigning UAVs per cluster).
• Jointly optimize UAV trajectories with communication and computation resource allocation in WPCN and MEC scenarios, subject to energy causality and QoS constraints.

Experimental results

Research questions

• RQ1 How can UAV mobility in 3D space be leveraged to maximize the total and fair energy transfer to multiple ground devices in a wireless network?
• RQ2 What are the optimal trajectory design strategies for a single UAV to charge multiple GDs at known locations, and how do they compare in performance and complexity?
• RQ3 How can multiple UAVs be coordinated to efficiently charge a large number of distributed GDs in a scalable and energy-efficient manner?
• RQ4 How can UAV-enabled WPT be integrated with wireless powered communication networks (WPCN) and mobile edge computing (MEC) to jointly optimize energy transfer and system performance?
• RQ5 What are the key challenges and open problems in UAV-enabled WPT, particularly regarding non-linear energy harvesting, CSI acquisition, and online trajectory adaptation?

Key findings

• The proposed trajectory design framework significantly improves energy fairness and total harvested energy compared to fixed-ET systems, especially in scenarios with spatially distributed GDs.
• The successive-hover-and-fly (SHF) approach achieves near-optimal performance with lower computational complexity than full trajectory optimization.
• Time-quantization-based optimization enables efficient joint trajectory and power control design, particularly suitable for real-time implementation.
• Multi-UAV schemes based on UAV swarming and GD clustering achieve scalable and energy-efficient coverage in large-area WPT networks.
• Joint optimization of UAV trajectory and resource allocation in WPCN and MEC scenarios enhances system throughput and computation offloading performance under energy causality constraints.
• Open problems such as non-linear energy harvesting, CSI feedback overhead, and online trajectory adaptation via reinforcement learning or radio maps remain critical for future research.