先进空中交通（AAM）已成为下一代交通系统的关键支柱，涵盖广泛的应用场景，包括各类无人飞行器（UAV）。为实现AAM，保障UAV与控制中心之间可靠、高效的通信链路至关重要。与此同时，无线网络的高度动态性以及UAV有限的机载能源，使得高效的轨迹规划与网络关联尤为关键。现有地面网络常因频繁切换与覆盖空洞而难以提供无处不在的覆盖。为应对这些挑战，地球静止轨道（GEO）卫星可作为有前景的补充方案，将UAV连通性扩展至地面网络边界之外。本文提出一种集成GEO卫星与地面网络的架构，以确保UAV的无缝连通性。借助人工智能（AI），设计了一种基于深度Q网络（DQN）的算法——无人机轨迹与关联联合规划（JUTAP），旨在最小化能耗、切换频率及断连概率。仿真结果验证了该算法在集成GEO-地面网络框架下的有效性。

Advanced Air Mobility (AAM) has emerged as a key pillar of next-generation transportation systems, encompassing a wide range of uncrewed aerial vehicle (UAV) applications. To enable AAM, maintaining reliable and efficient communication links between UAVs and control centers is essential. At the same time, the highly dynamic nature of wireless networks, combined with the limited onboard energy of UAVs, makes efficient trajectory planning and network association crucial. Existing terrestrial networks often fail to provide ubiquitous coverage due to frequent handovers and coverage gaps. To address these challenges, geostationary Earth orbit (GEO) satellites offer a promising complementary solution for extending UAV connectivity beyond terrestrial boundaries. This work proposes an integrated GEO terrestrial network architecture to ensure seamless UAV connectivity. Leveraging artificial intelligence (AI), a deep Q network (DQN) based algorithm is developed for joint UAV trajectory and association planning (JUTAP), aiming to minimize energy consumption, handover frequency, and disconnectivity. Simulation results validate the effectiveness of the proposed algorithm within the integrated GEO terrestrial framework.