用智能无人机动态组网，提升城市车联网连接率并省电
Dynamic Mask Enhanced Intelligent Multi-UAV Deployment for Urban Vehicular Networks
- 设计动态动作掩码机制，让多架无人机高效探索复杂部署空间
- 实测连接率提升18.2%，能耗降低66.6%，优于现有方法
- 适合交通智能、无人机协同、车联网优化方向的研究者
车载自组织网络（VANETs）在实现车路协同与智能交通中起关键作用。然而，城市 VANET 常面临链路频繁中断和子网分裂等问题，影响可靠通信。为此，本文提出将多架无人机作为通信中继，动态部署以增强网络。设计了一种基于评分的动态动作掩码增强型 QMIX 算法（Q-SDAM），在最大化车辆连通性的同时最小化多无人机能耗。通过评分机制引导无人机代理在大规模动作空间中高效探索，加速学习并提升优化性能。利用真实世界数据集验证了 Q-SDAM 的实用性。结果表明，相比现有算法，该方法使连通性提升 18.2%，能耗降低 66.6%。
原文摘要 · Abstract (English)
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.
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