Collaborative multi-UAV sensing in integrated sensing and communication networks

Xianzhe Xu, Rentuo Tao, Shikang Li, Yawei Chen, Linghao Xia, Yuhao Yang
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Abstract

This paper studies the collaborative unmanned aerial vehicle (UAV) sensing in integrated sensing and communication (ISAC) networks. By equipping sensing and communication units on UAVs, they can execute sensing tasks and transmit the sensing information to the base station (BS) for environment sensing. Due to the mobility and dense deployment of UAVs, they can sense the environment with much lower cost compared to the BS sensing. We aim to minimize the network sensing cost by optimizing the UAV deployment and task assignment collaboratively. For this joint optimization problem, we propose an iterative mechanism to optimize the UAV deployment and task assignment iteratively. UAV deployment problem is modeled as a cluster problem and we utilize a K-means cluster algorithm to solve it efficiently. For task assignment problem, we propose a greedy algorithm to solve it with low complexity. Simulation results validate the effectiveness of our proposed method in different scenarios.
集成传感与通信网络中的协同多无人机传感
研究了集成传感与通信(ISAC)网络中的协同无人机(UAV)传感问题。通过在无人机上装备传感和通信单元,无人机可以执行传感任务,并将传感信息传输到基站(BS)进行环境传感。由于无人机的机动性和密集部署,它们可以以比BS传感低得多的成本感知环境。我们的目标是通过协同优化无人机部署和任务分配,使网络感知成本最小化。针对这一联合优化问题,提出了一种迭代机制来迭代优化无人机的部署和任务分配。将无人机部署问题建模为一个聚类问题,利用k -均值聚类算法对其进行有效求解。对于任务分配问题,我们提出了一种贪心算法,以较低的复杂度来求解。仿真结果验证了该方法在不同场景下的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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