Deep Reinforcement Learning-Based Deployment Method for Emergency Communication Network
Emergency communication networks play a crucial role in disaster relief operations. Current automated deployment strategies based on rule-driven or heuristic algorithms struggle to adapt to the dynamic and heterogeneous network environments in disaster scenarios, while manual command deployment is c...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/14/7961 |
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| author | Bo Huang Yiwei Lu Hao Ma Changsheng Yin Ruopeng Yang Yongqi Shi Yu Tao Yongqi Wen Yihao Zhong |
| author_facet | Bo Huang Yiwei Lu Hao Ma Changsheng Yin Ruopeng Yang Yongqi Shi Yu Tao Yongqi Wen Yihao Zhong |
| author_sort | Bo Huang |
| collection | DOAJ |
| description | Emergency communication networks play a crucial role in disaster relief operations. Current automated deployment strategies based on rule-driven or heuristic algorithms struggle to adapt to the dynamic and heterogeneous network environments in disaster scenarios, while manual command deployment is constrained by personnel expertise and response time requirements, leading to suboptimal trade-offs between deployment efficiency and reliability. To address these challenges, this study proposes a novel deep reinforcement learning framework with a fully convolutional value network architecture, which achieves breakthroughs in multi-dimensional spatial decision-making through end-to-end feature extraction. This design effectively mitigates the “curse of dimensionality” inherent in traditional reinforcement learning methods for topology planning. Experimental results demonstrate that the proposed method effectively accomplishes the planning tasks of emergency communication hub elements, significantly improving deployment efficiency while maintaining robustness in complex environments. |
| format | Article |
| id | doaj-art-4cebae32a04b47859d290cf0796a4125 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-4cebae32a04b47859d290cf0796a41252025-08-20T03:36:13ZengMDPI AGApplied Sciences2076-34172025-07-011514796110.3390/app15147961Deep Reinforcement Learning-Based Deployment Method for Emergency Communication NetworkBo Huang0Yiwei Lu1Hao Ma2Changsheng Yin3Ruopeng Yang4Yongqi Shi5Yu Tao6Yongqi Wen7Yihao Zhong8National University of Defense Technology, Wuhan 430030, ChinaInformation Support Force Engineering University, Wuhan 430030, China32125 Unit, Jinan 250000, ChinaInformation Support Force Engineering University, Wuhan 430030, ChinaInformation Support Force Engineering University, Wuhan 430030, ChinaNational University of Defense Technology, Wuhan 430030, ChinaNational University of Defense Technology, Wuhan 430030, ChinaNational University of Defense Technology, Wuhan 430030, ChinaNational University of Defense Technology, Wuhan 430030, ChinaEmergency communication networks play a crucial role in disaster relief operations. Current automated deployment strategies based on rule-driven or heuristic algorithms struggle to adapt to the dynamic and heterogeneous network environments in disaster scenarios, while manual command deployment is constrained by personnel expertise and response time requirements, leading to suboptimal trade-offs between deployment efficiency and reliability. To address these challenges, this study proposes a novel deep reinforcement learning framework with a fully convolutional value network architecture, which achieves breakthroughs in multi-dimensional spatial decision-making through end-to-end feature extraction. This design effectively mitigates the “curse of dimensionality” inherent in traditional reinforcement learning methods for topology planning. Experimental results demonstrate that the proposed method effectively accomplishes the planning tasks of emergency communication hub elements, significantly improving deployment efficiency while maintaining robustness in complex environments.https://www.mdpi.com/2076-3417/15/14/7961emergency communicationreinforcement learningneural networkdeployment problem |
| spellingShingle | Bo Huang Yiwei Lu Hao Ma Changsheng Yin Ruopeng Yang Yongqi Shi Yu Tao Yongqi Wen Yihao Zhong Deep Reinforcement Learning-Based Deployment Method for Emergency Communication Network Applied Sciences emergency communication reinforcement learning neural network deployment problem |
| title | Deep Reinforcement Learning-Based Deployment Method for Emergency Communication Network |
| title_full | Deep Reinforcement Learning-Based Deployment Method for Emergency Communication Network |
| title_fullStr | Deep Reinforcement Learning-Based Deployment Method for Emergency Communication Network |
| title_full_unstemmed | Deep Reinforcement Learning-Based Deployment Method for Emergency Communication Network |
| title_short | Deep Reinforcement Learning-Based Deployment Method for Emergency Communication Network |
| title_sort | deep reinforcement learning based deployment method for emergency communication network |
| topic | emergency communication reinforcement learning neural network deployment problem |
| url | https://www.mdpi.com/2076-3417/15/14/7961 |
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