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

TL;DR

The paper addresses rapid deployment of UAV-assisted ISAC networks in post-disaster emergencies by introducing a two-stage CNN-based deployment agent (CNNDA). It combines an offline DPSO-driven data generation phase with an online CNN inference phase to map sparse user distributions to near-optimal UAV placements, leveraging BGAS for associations and a PDOP-based localization metric. The key contributions are the DPSO-enabled dataset construction, the CNN-based rapid deployment framework with Gaussian-enhanced inputs, and demonstrated deployment-time reductions (>96%) while maintaining high ISAC performance. This approach enables real-time, scalable emergency response by reducing computation while preserving sensing and communication quality.

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%.

Figures (5)

• Figure 1: An overview of the UAV-assisted ISAC network
• Figure 2: Process flow of the CNNDA framework
• Figure 3: The convergence of the proposed algorithm and its benchmarks
• Figure 4: The ISAC performance versus the number of users.
• Figure 5: Algorithm execution time versus the number of users.