面向节能无人机集群的通信与控制一体化：一种多智能体强化学习方法
Integrated Communication and Control for Energy-Efficient UAV Swarms: A Multi-Agent Reinforcement Learning Approach
AI总结：
针对复杂地理环境下无人机集群通信的链路中断与能耗约束，提出一体化通信控制机制和MAHPPO-AM算法，实现公平性0.99并最高节能25%。
摘要：
部署无人机（UAV）集群辅助通信网络已成为弥补基础设施匮乏环境中覆盖限制的一种日益重要的手段，在应急救援、军事与安全行动以及偏远地区覆盖等临时场景中具有尤为迫切的应用。然而，复杂地理环境会导致不可预测且高度动态的无线信道状况，造成空对地（A2G）链路频繁中断，从而严重制约无人机集群辅助移动通信的可靠性和服务质量。为提升复杂地理环境中无人机集群辅助通信的质量，我们提出一种通信与控制一体化协同设计机制。鉴于无人机集群固有的严格能量约束，所提机制旨在优化能量效率，同时在移动地面用户（GU）的公平通信速率与无人机能量消耗之间维持平衡。我们将联合资源分配与三维轨迹控制问题建模为马尔可夫决策过程（MDP），并开发了一个多智能体强化学习（MARL）框架，以实现无人机集群间的实时协同动作。为优化无人机集群的动作策略，我们提出一种新颖的带动作掩码的多智能体混合近端策略优化（MAHPPO-AM）算法，专门用于处理复杂的混合动作空间。该算法引入动作掩码，以在高维动作空间中强制执行硬约束。实验结果表明，与基线方法相比，我们的方法实现了0.99的公平性指数，同时将能耗降低至多25%。
英文摘要：
The deployment of unmanned aerial vehicle (UAV) swarm-assisted communication networks has become an increasingly vital approach for remediating coverage limitations in infrastructure-deficient environments, with especially pressing applications in temporary scenarios, such as emergency rescue, military and security operations, and remote area coverage. However, complex geographic environments lead to unpredictable and highly dynamic wireless channel conditions, resulting in frequent interruptions of air-to-ground (A2G) links that severely constrain the reliability and quality of service in UAV swarm-assisted mobile communications. To improve the quality of UAV swarm-assisted communications in complex geographic environments, we propose an integrated communication and control co-design mechanism. Given the stringent energy constraints inherent in UAV swarms, our proposed mechanism is designed to optimize energy efficiency while maintaining an equilibrium between equitable communication rates for mobile ground users (GUs) and UAV energy expenditure. We formulate the joint resource allocation and 3D trajectory control problem as a Markov decision process (MDP), and develop a multi-agent reinforcement learning (MARL) framework to enable real-time coordinated actions across the UAV swarm. To optimize the action policy of UAV swarms, we propose a novel multi-agent hybrid proximal policy optimization with action masking (MAHPPO-AM) algorithm, specifically designed to handle complex hybrid action spaces. The algorithm incorporates action masking to enforce hard constraints in high-dimensional action spaces. Experimental results demonstrate that our approach achieves a fairness index of 0.99 while reducing energy consumption by up to 25% compared to baseline methods.
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