  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.
