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

TL;DR

This paper presents a CNN-based rapid deployment framework for UAV-assisted ISAC networks in emergencies, significantly reducing deployment time while maintaining high communication and localization performance.

Contribution

It introduces a two-stage deployment approach combining offline optimization and CNN training for fast online UAV deployment in emergency scenarios.

Findings

CNN model reduces deployment time by over 96%.

The CNN achieves better ISAC performance than traditional algorithms.

The framework enables rapid and effective UAV deployment in disaster rescue.

Abstract

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%.