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Updated: Jan 27, 2026

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Minimum-Throughput Maximization for Multi-UAV-Enabled Wireless-Powered Communication Networks.

Fahui Wu1, Dingcheng Yang2, Lin Xiao3

  • 1Jiangxi Province Key Laboratory of Intelligent Information Systems, Nanchang University, Nanchang 330031, China. fahui97@email.ncu.edu.cn.

Sensors (Basel, Switzerland)
|April 3, 2019
PubMed
Summary
This summary is machine-generated.

This study optimizes wireless-powered communication networks using multiple unmanned aerial vehicles (UAVs) to maximize ground user throughput. An alternating optimization algorithm efficiently manages UAV trajectories and resource allocation.

Keywords:
UAV communicationsminimum-throughput maximizationresource allocationtrajectory design

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Area of Science:

  • Wireless communication networks
  • Robotics and control systems
  • Optimization theory

Background:

  • Wireless-powered communication networks (WPCNs) are emerging for efficient data transfer.
  • Unmanned aerial vehicles (UAVs) offer flexible deployment for wireless energy transfer (WET) and data gathering.
  • Optimizing resource allocation and trajectories is crucial for WPCN performance.

Purpose of the Study:

  • To maximize the minimum throughput for all ground users (GUs) in a multi-UAV WPCN.
  • To jointly optimize UAV trajectories and resource allocation for energy transfer and information transmission.
  • To address the non-convexity of the optimization problem for efficient system operation.

Main Methods:

  • Formulation of a throughput maximization problem for a WPCN with multiple UAVs.
  • Development of an alternating optimization algorithm leveraging successive convex optimization techniques.
  • Iterative optimization of UAV trajectories and resource allocation (energy transfer and GU transmission).

Main Results:

  • The proposed alternating optimization algorithm effectively solves the non-convex problem.
  • Demonstrated efficiency of the algorithm in maximizing minimum GU throughput across various scenarios.
  • Successful joint optimization of UAV trajectories and resource allocation.

Conclusions:

  • The proposed algorithm provides an efficient solution for optimizing multi-UAV WPCNs.
  • Joint optimization of trajectories and resource allocation is key to enhancing WPCN performance.
  • The approach shows significant potential for practical WPCN deployments.