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Related Experiment Video

Updated: Feb 11, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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DQN-empowered energy optimization for wireless powered communication networks.

Huajun Chen1, Xiaoye Wang2, Lina Yuan3

  • 1School of Data Science, Tongren University, Tongren, 554300, Guizhou, China.

Scientific Reports
|February 9, 2026
PubMed
Summary

This study introduces a Deep Q-Network (DQN) scheme for Wireless Powered Communication Networks (WPCNs), improving energy harvesting and resource management for sustainable IoT devices.

Keywords:
Deep Q-networkDynamic energy allocationMarkov decision processNonlinear energy modelQ-learning algorithmWireless powered communication networks

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Area of Science:

  • Wireless communication networks
  • Internet of Things (IoT)
  • Sustainable energy systems

Background:

  • Wireless Powered Communication Networks (WPCNs) are crucial for powering Internet of Things (IoT) devices.
  • Existing energy harvesting models in WPCNs often use linear approximations, leading to inaccuracies due to nonlinear Radio Frequency to Direct Current (RF-DC) conversion saturation.
  • Dynamic resource management is essential for optimizing performance and longevity in WPCNs.

Purpose of the Study:

  • To propose a Deep Q-Network (DQN)-empowered dynamic resource collaborative management scheme for WPCNs.
  • To address the limitations of traditional linear energy harvesting models by incorporating a piecewise nonlinear harvesting model.
  • To maximize network utility while balancing energy efficiency and fairness using a Markov Decision Process (MDP) framework.

Main Methods:

  • Formulated a multi-objective allocation problem within a Markov Decision Process (MDP) framework.
  • Employed a piecewise nonlinear energy harvesting model to accurately capture Radio Frequency to Direct Current (RF-DC) conversion effects.
  • Integrated Gaussian Process Regression (GPR) for energy harvest prediction within a closed-loop optimization system.
  • Provided theoretical convergence proofs for Q-learning and Lyapunov stability analysis for energy queue errors.

Main Results:

  • Extended network lifetime by 56.4% (117 to 183 rounds).
  • Reduced energy allocation standard deviation by 56.8% (23.7 mJ to 12.3 mJ).
  • Improved convergence speed by 53.1% (150 vs. 320 episodes) and dynamic adaptability by 66.7% (5 vs. 15 rounds).
  • Increased network throughput by 33.33% (80 vs. 60 Mbps).

Conclusions:

  • The proposed DQN-empowered scheme significantly enhances WPCN performance and sustainability.
  • The nonlinear harvesting model and MDP framework provide a more accurate and efficient approach to resource management.
  • The findings support the large-scale deployment of WPCNs for future IoT applications.