发表机构
National Mobile Communications Research Laboratory, Southeast University; Purple Mountain Laboratories（东南大学移动通信国家重点实验室; 紫金山实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对无人机辅助三维ISAC系统，通过优化无人机锚点布置，刻画并实现了通信速率与感知CRLB之间的Pareto最优权衡，提出了高效算法并验证了性能优势。
AI 中文摘要
本文研究了一种无人机辅助的三维集成感知与通信（ISAC）系统，其中部署一架无人机与基站（BS）通信，并同时监测一个感兴趣体积（VoI）。通过利用无人机锚节点的灵活布置，我们旨在刻画通信速率与感知CRLB之间的Pareto最优权衡。对于单个VoI的特殊情况，精确的Pareto最优无人机锚点布置集合被证明是连接基站与感知目标的线段。对于径向截断的球形扇区VoI，我们推导了几何条件，使得所有Pareto最优无人机位置都限制在内径向边界内的轴向段上。对于一般VoI，我们证明了内区域中区域最坏情况CRLB的凸性，推导了Pareto最优半径上界，并开发了一种高效算法来寻找Pareto最优的无人机锚节点布置。数值结果验证了分析布置结构，并表明我们提出的设计相比基准方案实现了显著扩大的速率-CRLB区域。
英文摘要
This paper considers an unmanned aerial vehicle (UAV)-assisted 3D integrated sensing and communication (ISAC) system, where a UAV is deployed to communicate with a base station (BS) and simultaneously monitor a volume of interest (VoI). By exploiting the flexible placement of the UAV anchor node, we aim to characterize the Pareto optimal tradeoff between the communication rate and sensing CRLB. For the special case of a singleton VoI, the exact Pareto-optimal UAV anchor placement set is shown to be the line segment connecting the BS and the sensing target. For a radially truncated spherical sector VoI, we derive geometric conditions under which all Pareto optimal UAV locations are confined to an axial segment inside the inner radial boundary. For general VoIs, we prove the convexity of the regional worst-case CRLB in the inner region, derive a Pareto-optimal radius upper bound, and develop an efficient algorithm to find the Pareto-optimal UAV anchor node placement. Numerical results validate the analytical placement structures and demonstrate that our proposed design achieves significantly enlarged rate-CRLB region over benchmark schemes.
