AI 中文总结
该研究针对空地ISAC的两大挑战，设计跨区域协作框架，采用服务驱动划分、自适应握手及MAPPO算法，仿真显示其通信QoS达90%、CRB降45%，性能优于传统方法。
AI 中文摘要
为服务立体空地空间，急需无人机（UAV）支持。但用其实现集成感知与通信（ISAC）存在两大关键挑战：1）地面通信需求动态且不均衡；2）感知的观测多样性有限。针对这些问题，设计了跨区域协作框架来协调无人机集群。具体而言，提出一种服务驱动的区域划分方案，以支持感知流量感知的无人机通信；还引入自适应握手机制，通过以可控同步开销缓解残留的区域间相位误差，提升协作感知精度。基于这些设计，开发了区域级多智能体近端策略优化（MAPPO）框架，采用集中式训练与分布式执行（CTDE）模式，用于跨区域协作决策。仿真结果表明，与传统基线方法相比，所提方法实现了约90%的通信服务质量（QoS），并将克拉美罗下界（CRB）降低了约45%。
英文摘要
To serve the volumetric air-ground space, uncrewed aerial vehicles (UAVs) are urgently needed. Yet, relying on them for integrated sensing and communication (ISAC) introduces two key challenges: 1) dynamic and imbalanced ground communication demand, and 2) limited observation diversity for sensing. To address these issues, a cross-region cooperative framework is designed to coordinate UAV swarms. Specifically, a service-driven regional partitioning scheme is proposed to support traffic-aware UAV communication, and an adaptive handshaking mechanism is introduced to improve cooperative sensing accuracy by mitigating residual inter-region phase errors with controlled synchronization overhead. Based on these designs, a region-level multi-agent proximal policy optimization (MAPPO) framework with centralized training and decentralized execution (CTDE) is developed for cross-region cooperative decision-making. Simulation results demonstrate that the proposed method achieves a communication quality-of-service (QoS) of approximately 90% and reduces the Cramér-Rao bound (CRB) by about 45% compared to conventional baselines.
