  UAV-assisted integrated sensing and communication (ISAC) network is crucial
for post-disaster emergency rescue. The speed of UAV deployment will directly
impact rescue results. However, the ISAC UAV deployment in emergency scenarios
is difficult to solve, which contradicts the rapid deployment. In this paper,
we propose a two-stage deployment framework to achieve rapid ISAC UAV
deployment in emergency scenarios, which consists of an offline stage and an
online stage. Specifically, in the offline stage, we first formulate the ISAC
UAV deployment problem and define the ISAC utility as the objective function,
which integrates communication rate and localization accuracy. Secondly, we
develop a dynamic particle swarm optimization (DPSO) algorithm to construct an
optimized UAV deployment dataset. Finally, we train a convolutional neural
network (CNN) model with this dataset, which replaces the time-consuming DPSO
algorithm. In the online stage, the trained CNN model can be used to make quick
decisions for the ISAC UAV deployment. The simulation results indicate that the
trained CNN model achieves superior ISAC performance compared to the classic
particle swarm optimization algorithm. Additionally, it significantly reduces
the deployment time by more than 96%.
