标题： 通过去中心化共识ADMM的无人机蜂群集成感知与通信 显示英文标题

标题： Integrated Sensing and Communication with UAV Swarms via Decentralized Consensus ADMM

摘要： 无人机群可以形成虚拟天线阵列，以利用额外的空间自由度并增强集成感知与通信（ISAC）。 由于群集的分布式特性以及单个无人机缺乏全局视角，无人机位置的优化具有挑战性。 本文提出了一种新的分布式优化框架，使无人机能够并行决定其位置，并在ISAC的全局最优群集几何上达成共识。 具体而言，我们推导了可实现的上行链路速率和Cramér-Rao界（CRB）作为通信和感知的可处理指标。 无人机位置被优化以平衡最大化通信速率和最小化CRB。 为了解决具有耦合变量的非凸问题，我们开发了一种分布式共识交替方向乘子法（ADMM）算法，该算法使无人机能够迭代对齐其局部更新并达成共识。 该算法将全局目标分解为局部投影更新、代理辅助共识协调和轻量级对偶更新，确保在整个群集中保持可扩展性和一致性。 仿真结果表明，所提出的共识ADMM算法收敛迅速且具有强大的可扩展性，并且无人机群在通信和感知性能方面显著优于固定阵列基线。 显示英文摘要

摘要： UAV swarms can form virtual antenna arrays to exploit additional spatial degrees of freedom and enhance integrated sensing and communication (ISAC). The optimization of UAV positions is challenging due to the distributed nature of swarms and the lack of a global view at individual UAVs. This paper presents a new decentralized optimization framework that allows UAVs to decide their locations in parallel and reach consensus on a globally optimal swarm geometry for ISAC. Specifically, we derive the achievable uplink rate and Cram\'er-Rao Bound (CRB) as tractable metrics for communication and sensing, respectively. The UAV positions are optimized to balance maximizing the communication rate and minimizing the CRB. To solve this non-convex problem with coupled variables, we develop a decentralized consensus alternating direction method of multipliers (ADMM) algorithm, which enables the UAVs to iteratively align their local updates and reach consensus. The algorithm decomposes the global objective into local projection updates, proxy-assisted consensus coordination, and lightweight dual updates, ensuring scalability and consistency throughout the swarm. Simulations demonstrate that the proposed consensus ADMM algorithm converges rapidly with strong scalability, and that the UAV swarm significantly outperforms fixed-array baselines in both communication and sensing performance.