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%.

翻译：无人机辅助的集成感知与通信（ISAC）网络对灾后紧急救援至关重要。无人机部署速度将直接影响救援效果。然而，应急场景下的ISAC无人机部署问题难以求解，与快速部署需求相矛盾。本文提出一种两阶段部署框架，包括离线阶段和在线阶段，以实现应急场景下ISAC无人机的快速部署。具体而言，在离线阶段，首先构建ISAC无人机部署问题，并定义以通信速率与定位精度为综合指标的ISAC效用函数作为目标函数；其次，开发动态粒子群优化（DPSO）算法构建最优无人机部署数据集；最后，利用该数据集训练卷积神经网络（CNN）模型，以替代耗时的DPSO算法。在线阶段中，训练完成的CNN模型可用于快速决策ISAC无人机部署方案。仿真结果表明，相较于经典粒子群优化算法，训练后的CNN模型在实现更优ISAC性能的同时，将部署时间显著降低96%以上。