Energy Efficiency Optimization for UAV-RIS-Assisted Wireless Powered Communication Networks
In urban environments, unmanned aerial vehicles (UAVs) can significantly enhance the performance of wireless powered communication networks (WPCNs), enabling reliable communication and efficient energy transfer for urban Internet of Things (IoTs) nodes. However, the complex urban landscape character...
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| Format: | Article |
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MDPI AG
2025-05-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/9/5/344 |
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| author | Xianhao Shen Ling Gu Jiazhi Yang Shuangqin Shen |
| author_facet | Xianhao Shen Ling Gu Jiazhi Yang Shuangqin Shen |
| author_sort | Xianhao Shen |
| collection | DOAJ |
| description | In urban environments, unmanned aerial vehicles (UAVs) can significantly enhance the performance of wireless powered communication networks (WPCNs), enabling reliable communication and efficient energy transfer for urban Internet of Things (IoTs) nodes. However, the complex urban landscape characterized by dense building structures and node distributions severely hampers the efficiency of wireless power transmission. To address this challenge, this paper presents a novel framework for urban WPCN systems assisted by UAVs equipped with reconfigurable intelligent surfaces (UAV-RISs). The framework adopts time division multiple access (TDMA) technology to coordinate the transmission process of information and energy. Considering two TDMA methods, the paper jointly optimizes the flight trajectory of the UAV, the energy harvesting scheduling of ground nodes, and the phase shift matrix of the RIS with the goal of improving the energy efficiency of the system. Furthermore, deep reinforcement learning (DRL) is introduced to effectively solve the formulated optimization problem. Simulation results demonstrate that the proposed optimized scheme outperforms benchmark schemes in terms of average throughput and energy efficiency. Experimental data also reveal the applicability of different TDMA strategies: dynamic TDMA exhibits superior performance in achieving higher average throughput at ground nodes in urban scenarios, while traditional TDMA is more advantageous for total energy harvesting efficiency. These findings provide critical theoretical insights and practical guidelines for UAV trajectory design and communication network optimization in urban environments. |
| format | Article |
| id | doaj-art-33a63538477446e98b9bfdb5233c1a7e |
| institution | DOAJ |
| issn | 2504-446X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-33a63538477446e98b9bfdb5233c1a7e2025-08-20T03:14:33ZengMDPI AGDrones2504-446X2025-05-019534410.3390/drones9050344Energy Efficiency Optimization for UAV-RIS-Assisted Wireless Powered Communication NetworksXianhao Shen0Ling Gu1Jiazhi Yang2Shuangqin Shen3Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin University of Technology, Guilin 541006, ChinaGuangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin University of Technology, Guilin 541006, ChinaSchool of Computer Science and Engineering, Guilin University of Aerospace Technology, Guilin 541004, ChinaSchool of Computer Science and Engineering, Guilin University of Technology, Guilin 541006, ChinaIn urban environments, unmanned aerial vehicles (UAVs) can significantly enhance the performance of wireless powered communication networks (WPCNs), enabling reliable communication and efficient energy transfer for urban Internet of Things (IoTs) nodes. However, the complex urban landscape characterized by dense building structures and node distributions severely hampers the efficiency of wireless power transmission. To address this challenge, this paper presents a novel framework for urban WPCN systems assisted by UAVs equipped with reconfigurable intelligent surfaces (UAV-RISs). The framework adopts time division multiple access (TDMA) technology to coordinate the transmission process of information and energy. Considering two TDMA methods, the paper jointly optimizes the flight trajectory of the UAV, the energy harvesting scheduling of ground nodes, and the phase shift matrix of the RIS with the goal of improving the energy efficiency of the system. Furthermore, deep reinforcement learning (DRL) is introduced to effectively solve the formulated optimization problem. Simulation results demonstrate that the proposed optimized scheme outperforms benchmark schemes in terms of average throughput and energy efficiency. Experimental data also reveal the applicability of different TDMA strategies: dynamic TDMA exhibits superior performance in achieving higher average throughput at ground nodes in urban scenarios, while traditional TDMA is more advantageous for total energy harvesting efficiency. These findings provide critical theoretical insights and practical guidelines for UAV trajectory design and communication network optimization in urban environments.https://www.mdpi.com/2504-446X/9/5/344wireless powered communication networkUAVreconfigurable intelligent surfacetime division multiple accessdeep reinforcement learning |
| spellingShingle | Xianhao Shen Ling Gu Jiazhi Yang Shuangqin Shen Energy Efficiency Optimization for UAV-RIS-Assisted Wireless Powered Communication Networks Drones wireless powered communication network UAV reconfigurable intelligent surface time division multiple access deep reinforcement learning |
| title | Energy Efficiency Optimization for UAV-RIS-Assisted Wireless Powered Communication Networks |
| title_full | Energy Efficiency Optimization for UAV-RIS-Assisted Wireless Powered Communication Networks |
| title_fullStr | Energy Efficiency Optimization for UAV-RIS-Assisted Wireless Powered Communication Networks |
| title_full_unstemmed | Energy Efficiency Optimization for UAV-RIS-Assisted Wireless Powered Communication Networks |
| title_short | Energy Efficiency Optimization for UAV-RIS-Assisted Wireless Powered Communication Networks |
| title_sort | energy efficiency optimization for uav ris assisted wireless powered communication networks |
| topic | wireless powered communication network UAV reconfigurable intelligent surface time division multiple access deep reinforcement learning |
| url | https://www.mdpi.com/2504-446X/9/5/344 |
| work_keys_str_mv | AT xianhaoshen energyefficiencyoptimizationforuavrisassistedwirelesspoweredcommunicationnetworks AT linggu energyefficiencyoptimizationforuavrisassistedwirelesspoweredcommunicationnetworks AT jiazhiyang energyefficiencyoptimizationforuavrisassistedwirelesspoweredcommunicationnetworks AT shuangqinshen energyefficiencyoptimizationforuavrisassistedwirelesspoweredcommunicationnetworks |