论文标题
关于硬件损伤对RIS AID本地化的影响
On the Impact of Hardware Impairments on RIS-aided Localization
论文作者
论文摘要
我们通过考虑使用实用的相关相关的RIS振幅变化模型，研究了具有单安顿纳用户设备（UE）（UE）和基本站（BS）的可重构智能表面（RIS）近场定位系统。为了分析实际模型与单位振幅元素的理想模型之间的不匹配下的本地化性能，我们采用了误指定的Cramér-Rao Bound（MCRB）。基于MCRB推导，评估了UE位置估计的均方误差上的下限（LB），并证明以低信噪比（SNRS）收敛到MCRB。模拟结果表明，由于模型错误指定而增加了SNR，因此性能降解更加严重。此外，在高SNR制度的LB中得出了不匹配的最大似然（MML）估计量。最后，我们观察到模型不匹配可以导致高SNRS处的魔力定位性能损失。
We investigate a reconfigurable intelligent surface (RIS)-aided near-field localization system with single-antenna user equipment (UE) and base station (BS) under hardware impairments by considering a practical phase-dependent RIS amplitude variations model. To analyze the localization performance under the mismatch between the practical model and the ideal model with unit-amplitude RIS elements, we employ the misspecified Cramér-Rao bound (MCRB). Based on the MCRB derivation, the lower bound (LB) on the mean-squared error for estimation of UE position is evaluated and shown to converge to the MCRB at low signal-to-noise ratios (SNRs). Simulation results indicate more severe performance degradation due to the model misspecification with increasing SNR. In addition, the mismatched maximum likelihood (MML) estimator is derived and found to be tight to the LB in the high SNR regime. Finally, we observe that the model mismatch can lead to an order-of-magnitude localization performance loss at high SNRs.
