The proliferation of users, devices, and novel vehicular applications - propelled by advancements in autonomous systems and connected technologies - is precipitating an unprecedented surge in novel services. These emerging services require substantial bandwidth allocation, adherence to stringent Quality of Service (QoS) parameters, and energy-efficient implementations, particularly within highly dynamic vehicular environments. The complexity of these requirements necessitates a fundamental paradigm shift in service orchestration methodologies to facilitate seamless and robust service delivery. This paper addresses this challenge by presenting a novel framework for service orchestration in Unmanned Aerial Vehicles (UAV)-assisted 6G aerial-terrestrial networks. The proposed framework synergistically integrates UAV trajectory planning, Multiple-Access Control (MAC), and service placement to facilitate energy-efficient service coverage while maintaining ultra-low latency communication for vehicular user service requests. We first present a non-linear programming model that formulates the optimization problem. Next, to address the problem, we employ a Hierarchical Deep Reinforcement Learning (HDRL) algorithm that dynamically predicts service requests, user mobility, and channel conditions, addressing the challenges of interference, resource scarcity, and mobility in heterogeneous networks. Simulation results demonstrate that the proposed framework outperforms state-of-the-art solutions in request acceptance, energy efficiency, and latency minimization, showcasing its potential to support the high demands of next-generation vehicular networks.

翻译：用户、设备及新型车载应用的激增——受自主系统与互联技术进步的推动——正引发前所未有的新服务浪潮。这些新兴服务需要充足的带宽分配、严格遵守服务质量参数以及节能实现，尤其是在高度动态的车载环境中。这些要求的复杂性需要服务编排方法的根本范式转变，以实现无缝且稳健的服务交付。本文通过提出一个新颖的框架来解决这一挑战，该框架用于无人机辅助的6G空地网络中的服务编排。所提框架协同集成了无人机轨迹规划、多址接入控制和服务放置，以在保持车载用户服务请求的超低延迟通信的同时，实现节能的服务覆盖。我们首先提出一个非线性规划模型来形式化优化问题。接着，为应对该问题，我们采用了一种层次化深度强化学习算法，该算法动态预测服务请求、用户移动性和信道条件，解决了异构网络中的干扰、资源稀缺和移动性挑战。仿真结果表明，所提框架在请求接受率、能效和延迟最小化方面优于现有先进解决方案，展示了其支持下一代车载网络高需求的潜力。