具有不确定位置感知误差的无人机辅助ISAC系统的鲁棒功率分配
Robust Power Allocation for UAV-aided ISAC Systems with Uncertain Location Sensing Errors
AI总结：
本文提出一种无人机辅助ISAC框架，针对位置感知误差分别建模为椭球、高斯和任意分布，设计了三种鲁棒功率分配方案以最小化感知CRB，并验证了其鲁棒性与通感权衡。
摘要：
无人机（UAV）在物联网（IoT）的通感一体化（ISAC）系统中具有巨大潜力。本文提出了一种UAV辅助的ISAC框架，并研究了三种鲁棒功率分配方案。首先，基于到达时间（ToA）估计，推导了显式的Cramér-Rao下界（CRB）表达式，作为位置感知的性能指标。然后，分析了位置感知误差（LSE）对通信的影响，揭示了通信与感知之间固有的耦合关系。此外，通过分别将LSE建模为椭球分布模型、高斯分布模型和任意分布模型，构建了三个鲁棒的通信与感知功率分配问题。值得注意的是，这些优化问题旨在最小化CRB，并受限于数据速率和总功率约束。然而，这些问题是非凸且难以处理的。为了应对上述三种LSE模型相关的挑战，分别提出使用S-Procedure与交替优化（S-AO）方法、Bernstein型不等式与逐次凸逼近（BI-SCA）方法，以及条件风险价值（CVaR）与AO（CVaR-AO）方法来求解这些问题。最后，仿真结果通过与非鲁棒设计进行比较，证明了所提出的UAV辅助ISAC系统对LSE的鲁棒性，并评估了ISAC系统中通信与感知之间的权衡。
英文摘要：
Unmanned aerial vehicle (UAV) holds immense potential in integrated sensing and communication (ISAC) systems for the Internet of Things (IoT). In this paper, we propose a UAV-aided ISAC framework and investigate three robust power allocation schemes. First, we derive an explicit expression of the Cramér-Rao bound (CRB) based on time-of-arrival (ToA) estimation, which serves as the performance metric for location sensing. Then, we analyze the impact of the location sensing error (LSE) on communications, revealing the inherent coupling relationship between communication and sensing. Moreover, we formulate three robust communication and sensing power allocation problems by respectively characterizing the LSE as an ellipsoidal distributed model, a Gaussian distributed model, and an arbitrary distributed model. Notably, the optimization problems seek to minimize the CRB, subject to data rate and total power constraints. However, these problems are non-convex and intractable. To address the challenges related to the three aforementioned LSE models, we respectively propose to use the ${\cal{S}}$-Procedure and alternating optimization (${\cal{S}}$-AO) method, Bernstein-type inequality and successive convex approximation (BI-SCA) method, and conditional value-at-risk (CVaR) and AO (CVaR-AO) method to solve these problems. Finally, simulation results demonstrate the robustness of our proposed UAV-aided ISAC system against the LSE by comparing with the non-robust design, and evaluate the trade-off between communication and sensing in the ISAC system.
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