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Multi-UAV Collaborative Energy Charging for Battery-Free SWIPT-Enabled Sensor Networks Based on MADDPG 认领 引用
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作者 Xiangyi Le Deyu Lin +2 位作者 Yufei Zhao Wang Miao Yong Liang Guan 《Computers, Materials & Continua》 SCIE EI 2026年第9期2376-2395,共20页
The emergence of Unmanned Aerial Vehicle(UAV)-enabled Wireless Energy Transfer(WET)and Simultaneous Wireless Information and Power Transfer(SWIPT)technology provide a promising solution to overcome the energy sustaina... The emergence of Unmanned Aerial Vehicle(UAV)-enabled Wireless Energy Transfer(WET)and Simultaneous Wireless Information and Power Transfer(SWIPT)technology provide a promising solution to overcome the energy sustainability limitations of traditional harvesting-reliant sensor networks.However,in large-scale Battery-free SWIPT-enabled Sensor Networks(BSSN)characterized by sparse node distribution and heterogeneous energy consumption and harvesting rates,employing a single UAV for energy replenishment often suffers from insufficient operation continuity and low charging efficiency.To overcome these challenges,a Multi-UAV Collaborative Energy Charging for BSSN Based on Multi-Agent Deep Deterministic Policy Gradient(MCEC-MADDPG)is proposed in this paper.Specifically,we construct a collaborative one-to-one precision energy supply model where UAVs hover directly above specific nodes to achieve power transmission without complex beamforming requirements.To achieve collaborative scheduling among multiple UAVs in wide-area dynamic environments,the energy replenishment problem is first formulated as a Partially Observable Markov Decision Process(POMDP).Subsequently,the Centralized Training with Decentralized Execution(CTDE)architecture of the MADDPG algorithm is leveraged to solve this POMDP,which effectively tackles the non-stationarity challenge inherent in multi-agent environments.Simulation results demonstrate that MCEC-MADDPG exhibits superior performance in terms of convergence speed and stability.It enables the adaptive emergence of spatial-division collaborative strategies,significantly enhances the average residual energy of the network,and elevates the node survival rate to nearly 90%.Compared with Deep Deterministic Policy Gradient(DDPG),the traditional static Partition-Greedy method,the heuristic K-Means algorithm and the dynamic Two-Layer task allocation strategy,the proposed approach demonstrates substantial advantages. 展开更多
关键词 Battery-free SWIPT-enabled sensor networks multi-agent deep deterministic policy gradient multi-unmanned aerial vehicle collaborative energy charging partially observable Markov decision process centralized training with decentralized execution
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