Fast and reliable wireless communication has become a critical demand in human life. In the case of mission-critical (MC) scenarios, for instance, when natural disasters strike, providing ubiquitous connectivity becomes challenging by using traditional wireless networks. In this context, unmanned aerial vehicle (UAV) based aerial networks offer a promising alternative for fast, flexible, and reliable wireless communications. Due to unique characteristics such as mobility, flexible deployment, and rapid reconfiguration, drones can readily change location dynamically to provide on-demand communications to users on the ground in emergency scenarios. As a result, the usage of UAV base stations (UAV-BSs) has been considered an appropriate approach for providing rapid connection in MC scenarios. In this paper, we study how to control multiple UAV-BSs in both static and dynamic environments. We use a system-level simulator to model an MC scenario in which a macro BS of a cellular network is out of service and multiple UAV-BSs are deployed using integrated access and backhaul (IAB) technology to provide coverage for users in the disaster area. With the data collected from the system-level simulation, a deep reinforcement learning algorithm is developed to jointly optimize the three-dimensional placement of these multiple UAV-BSs, which adapt their 3-D locations to the on-ground user movement. The evaluation results show that the proposed algorithm can support the autonomous navigation of the UAV-BSs to meet the MC service requirements in terms of user throughput and drop rate.

翻译：快速可靠的无线通信已成为人类生活中的关键需求。在关键任务场景中，例如自然灾害发生时，传统无线网络难以提供普适连接。在此背景下，基于无人机的空中网络为快速、灵活且可靠的无线通信提供了有前景的替代方案。由于具备机动性、灵活部署和快速重构等独特特性，无人机可在应急场景中动态改变位置，为地面用户提供按需通信。因此，无人机基站被视为在关键任务场景中提供快速连接的适当方法。本文研究如何在静态和动态环境中控制多个无人机基站。我们使用系统级模拟器建模一个关键任务场景：蜂窝网络的宏基站失效，多个无人机基站采用集成接入与回程技术部署，为灾区用户提供覆盖。利用系统级仿真收集的数据，开发了一种深度强化学习算法，用于联合优化这些无人机基站的三维部署位置，使其能够根据地面上用户的移动自适应调整三维坐标。评估结果表明，所提出算法能够支持无人机基站的自主导航，从而满足关键任务服务在用户吞吐量和掉线率方面的要求。