DQN-empowered energy optimization for wireless powered communication networks

With the rapid development of Internet of Things (IoT) technology, Wireless Powered Communication Networks (WPCNs) have emerged as a sustainable solution for powering IoT devices. This paper proposes a Deep Q-Network (DQN)-empowered dynamic resource collaborative management scheme addressing limitations in prior WPCN research. Traditional linear energy harvesting models introduce significant errors when Radio Frequency to Direct Current (RF–DC) conversion exhibits nonlinear saturation effects. We adopt a piecewise nonlinear harvesting model and formulate a multi-objective allocation problem using a Markov Decision Process (MDP) framework. Our objective function maximizes network utility while balancing energy efficiency and Jain’s fairness index. A closed-loop optimization framework integrates Gaussian Process Regression (GPR) for harvest prediction. Theoretical contributions include: (1) convergence proofs for Q-learning under Robbins-Monro conditions; (2) Lyapunov stability analysis ensuring bounded energy queue errors; and (3) O(N) computational complexity scalability. Simulation results for a 30-node network demonstrate that our scheme extends network lifetime by 56.4% (117 to 183 rounds), reduces energy allocation standard deviation by 56.8% (23.7 mJ to 12.3 mJ), improves convergence speed by 53.1% (150 vs. 320 episodes), enhances dynamic adaptability by 66.7% (5 vs. 15 rounds), and increases throughput by 33.33% (80 vs. 60 Mbps). These results provide strong support for large-scale WPCN deployment.