AI 中文总结
本文针对视距路径遮挡场景下的目标定位问题，设计多跳RIS辅助的ISAC系统，通过CPD分解提取目标位置参数，联合优化波束成形与RIS相移配置，提升定位精度，性能优于基线方案。
AI 中文摘要
可重构智能表面（RIS）已展现出提升集成感知与通信（ISAC）性能的巨大潜力，尤其在视距（LoS）路径被遮挡的场景中。通过控制表面上的可重构元件，RIS可建立虚拟视距路径并提供可观的被动波束成形增益，从而显著改善接收信号质量。本文设计了一种用于目标定位的新型多跳RIS ISAC系统，该系统部署多个RIS，以辅助发射机与关联用户的通信，同时提升目标定位中的接收机感知性能。具体而言，我们构建优化问题以最小化目标检测的均方根误差（RMSE），同时保证用户的通信需求。为求解该问题，我们首先通过平行因子分解展开级联感知信道，并开发一种基于低秩CANDECOMP/PARAFAC分解（CPD）的方案，以提取感知目标的位置参数（即到达角、离开角和时延）。随后，我们开发一种联合选择发射波束成形和RIS相移配置的方案，以最大化接收机处的感知能量，进而提升目标定位精度。我们还提供了所提方法估计参数的唯一性分析、复杂度分析以及克拉美罗下界（CRLB）。仿真结果验证了，与基线方案相比，本文设计的方案在目标定位性能上的提升。
英文摘要
Reconfigurable intelligent surface (RIS) has demon- strated remarkable potential to enhance the performance of integrated sensing and communication (ISAC), particularly when the line-of-sight (LoS) paths are obstructed. By controlling the reconfigurable elements on the surface, RIS can establish virtual LoS paths and provide considerable passive beamforming gains, thereby significantly improving the received signal quality. In this paper, we design a novel multi-hop RIS ISAC system for target positioning, where multiple RISs are deployed to assist the communication from a transmitter to associated users while simultaneously enhancing receiver sensing performance in target positioning. Specifically, we formulate an optimization problem to minimize the root mean square error (RMSE) of the target detection while guaranteeing the communication requirements of the users. To solve this problem, we first unfold the cascaded sens- ing channel through parallel factor decomposition, and develop a low-rank CANDECOMP/PARAFAC decomposition (CPD)-based scheme to extract the location parameters (i.e., angle of arrival, angle of departure and delay) of the sensing targets. Then, we develop a scheme for jointly selecting the transmit beamforming and RIS phase shift configurations to maximize the sensing energy at the receiver, which in turn leads to improved accuracy in target positioning. We also provide a uniqueness analysis, complexity analysis, and Cramér-Rao lower bound (CRLB) of the parameters estimated by our methodology. Simulation results validate the improvement in target positioning obtained by our design relative to baselines.
CommentsAccepted by IEEE TWC
