Dynamic Mask Enhanced Intelligent Multi-UAV Deployment for Urban Vehicular Networks
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866914441643163648 |
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| author | Cao, Gaoxiang Yuan, Wenke Hou, Yunpeng He, Huasen Zheng, Quan Yang, Jian |
| author_facet | Cao, Gaoxiang Yuan, Wenke Hou, Yunpeng He, Huasen Zheng, Quan Yang, Jian |
| contents | Vehicular Ad Hoc Networks (VANETs) play a crucial role in realizing vehicle-road collaboration and intelligent transportation. However, urban VANETs often face challenges such as frequent link disconnections and subnet fragmentation, which hinder reliable connectivity. To address these issues, we dynamically deploy multiple Unmanned Aerial Vehicles (UAVs) as communication relays to enhance VANET. A novel Score based Dynamic Action Mask enhanced QMIX algorithm (Q-SDAM) is proposed for multi-UAV deployment, which maximizes vehicle connectivity while minimizing multi-UAV energy consumption. Specifically, we design a score-based dynamic action mask mechanism to guide UAV agents in exploring large action spaces, accelerate the learning process and enhance optimization performance. The practicality of Q-SDAM is validated using real-world datasets. We show that Q-SDAM improves connectivity by 18.2% while reducing energy consumption by 66.6% compared with existing algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_02358 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Dynamic Mask Enhanced Intelligent Multi-UAV Deployment for Urban Vehicular Networks Cao, Gaoxiang Yuan, Wenke Hou, Yunpeng He, Huasen Zheng, Quan Yang, Jian Networking and Internet Architecture Artificial Intelligence Vehicular Ad Hoc Networks (VANETs) play a crucial role in realizing vehicle-road collaboration and intelligent transportation. However, urban VANETs often face challenges such as frequent link disconnections and subnet fragmentation, which hinder reliable connectivity. To address these issues, we dynamically deploy multiple Unmanned Aerial Vehicles (UAVs) as communication relays to enhance VANET. A novel Score based Dynamic Action Mask enhanced QMIX algorithm (Q-SDAM) is proposed for multi-UAV deployment, which maximizes vehicle connectivity while minimizing multi-UAV energy consumption. Specifically, we design a score-based dynamic action mask mechanism to guide UAV agents in exploring large action spaces, accelerate the learning process and enhance optimization performance. The practicality of Q-SDAM is validated using real-world datasets. We show that Q-SDAM improves connectivity by 18.2% while reducing energy consumption by 66.6% compared with existing algorithms. |
| title | Dynamic Mask Enhanced Intelligent Multi-UAV Deployment for Urban Vehicular Networks |
| topic | Networking and Internet Architecture Artificial Intelligence |
| url | https://arxiv.org/abs/2604.02358 |