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

UAV-assisted Emergency Integrated Sensing and Communication Networks: A CNN-based Rapid Deployment Approach