分布式ISAC系统中的资源分配：一种位置敏感的模式选择与任务分配框架

中文摘要 AI 辅助

本文针对分布式集成感知与通信（ISAC）系统，提出了一种区域感兴趣（ROI）感知的模式选择与任务分配（RAMSTA）框架。在实际ISAC用例中，感知性能要求可能取决于位置。例如，交通路口的车辆目标、接近受限空域的无人机或人口稀疏区域，可能需要不同的检测或定位精度。这要求设计位置敏感的资源分配以适应变化的感知性能需求。为此，我们设计了一个基于sigmoid的ROI邻近参数，用于在加权和速率与位置后验Cramér-Rao下界最小化问题中调节通信-感知权衡。所得到的混合整数非线性规划通过应用惩罚凸凹过程求解。我们表明，ROI灵敏度阈值允许根据目标的预测位置相对于预定义ROI自适应地变化通信-感知权衡。

英文摘要

In this work we propose a region of interest (ROI)-aware mode selection and task assignment (RAMSTA) framework for distributed integrated sensing and communication (ISAC) systems. In realistic ISAC use-cases, the sensing performance requirement can be location dependent. Vehicular targets at a traffic intersection, unmanned aerial vehicle approaching a restricted airspace or a sparsely populated areas may require different levels of detection or localization accuracy. This warrants the design of location sensitive resource allocation to account for the varying sensing performance requirements. Thus, we design a sigmoid-based ROI proximity parameter to tune the communication-sensing trade-off in a weighted sum rate and position posterior Cramér-Rao lower bound minimization problem. The resulting mixed-integer non-linear program is solved by applying a penalized convex-concave procedure. We show that the ROI sensitivity thresholds allows the adaptive variation of the communication-sensing trade-off with respect to the predicted location of the target and relative to a predefined ROI.

发表机构

• Faculty of EEMCS, University of Twente（特温特大学EEMCS学院）

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