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arXiv 2402.03042eess.SP
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半无源智能反射面赋能的感知系统

Semi-Passive Intelligent Reflecting Surface Enabled Sensing Systems

Qiaoyan Peng, Qingqing Wu, Wen Chen, Shaodan Ma, Ming-Min Zhao, Octavia A. Dobre

AI总结:

本文研究半无源智能反射面感知系统,通过推导克拉美-罗界并联合优化IRS相移与基站波束成形,最小化点目标和扩展目标下的参数估计误差,仿真验证了所提设计的有效性。

摘要:

智能反射面(IRS)因其在促进和支持无线通信与感知方面的潜力而受到越来越多的关注和重视。本文研究了一种半无源IRS赋能的感知系统,其中IRS由无源反射元件和有源传感器组成。我们的目标是在点目标和扩展目标两种情况下最小化参数估计的克拉美-罗界(CRB)。为实现这一目标,我们首先推导了到达方向(DoA)估计的CRB闭式表达式,然后基于CRB理论分析了在单天线基站(BS)的点目标情况下IRS反射元件和传感器的分配设计。为了高效求解多天线基站情况下的相应优化问题,我们提出了一种联合优化IRS相移和BS波束成形器的高效算法。在扩展目标情况下,通过优化BS发射波束成形器来最小化目标响应矩阵(TRM)估计的CRB。此外,我们探讨了各种系统参数对CRB的影响,并将这些影响与点目标情况下观察到的效果进行了比较。仿真结果表明,半无源IRS和我们提出的波束成形设计在提高感知系统性能方面的有效性。

英文摘要:

Intelligent reflecting surface (IRS) has garnered growing interest and attention due to its potential for facilitating and supporting wireless communications and sensing. This paper studies a semi-passive IRS-enabled sensing system, where an IRS consists of both passive reflecting elements and active sensors. Our goal is to minimize the Cramér-Rao bound (CRB) for parameter estimation under both point and extended target cases. Towards this goal, we begin by deriving the CRB for the direction-of-arrival (DoA) estimation in closed-form and then theoretically analyze the IRS reflecting elements and sensors allocation design based on the CRB under the point target case with a single-antenna base station (BS). To efficiently solve the corresponding optimization problem for the case with a multi-antenna BS, we propose an efficient algorithm by jointly optimizing the IRS phase shifts and the BS beamformers. Under the extended target case, the CRB for the target response matrix (TRM) estimation is minimized via the optimization of the BS transmit beamformers. Moreover, we explore the influence of various system parameters on the CRB and compare these effects to those observed under the point target case. Simulation results show the effectiveness of the semi-passive IRS and our proposed beamforming design for improving the performance of the sensing system.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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