Multiple Intelligent Reflecting Surfaces Collaborative Wireless Localization System
<|reference_start|>Multiple Intelligent Reflecting Surfaces Collaborative Wireless Localization System: This paper studies a multiple intelligent reflecting surfaces (IRSs) collaborative localization system where multiple semi-passive IRSs are deployed in the network to locate one or more targets based on time-of-arrival. It is assumed that each semi-passive IRS is equipped with reflective elements and sensors, which are used to establish the line-of-sight links from the base station (BS) to multiple targets and process echo signals, respectively. Based on the above model, we derive the Fisher information matrix of the echo signal with respect to the time delay. By employing the chain rule and exploiting the geometric relationship between time delay and position, the Cramer-Rao bound (CRB) for estimating the target's Cartesian coordinate position is derived. Then, we propose a two-stage algorithmic framework to minimize CRB in single- and multi-target localization systems by joint optimizing active beamforming at BS, passive beamforming at multiple IRSs and IRS selection. For the single-target case, we derive the optimal closed-form solution for multiple IRSs coefficients design and propose a lowcomplexity algorithm based on alternating direction method of multipliers to obtain the optimal solution for active beaming design. For the multi-target case, alternating optimization is used to transform the original problem into two subproblems where semi-definite relaxation and successive convex approximation are applied to tackle the quadraticity and indefiniteness in the CRB expression, respectively. Finally, numerical simulation results validate the effectiveness of the proposed algorithm for multiple IRSs collaborative localization system compared to other benchmark schemes as well as the significant performance gains.<|reference_end|>
@article{zhang2024multiple,
    title={Multiple Intelligent Reflecting Surfaces Collaborative Wireless
  Localization System},
    author={Ziheng Zhang, Wen Chen, Qingqing Wu, Zhendong Li, Xusheng Zhu,
  Jingfeng Chen, and Nan Cheng},
    journal={arXiv preprint arXiv:2406.09846},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.09846},
    primaryClass={cs.IT eess.SP math.IT}
}