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arXiv 2302.04436eess.SP
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像素故障下的RIS辅助定位

RIS-aided Localization under Pixel Failures

Cuneyd Ozturk, Musa Furkan Keskin, Vincenzo Sciancalepore, Henk Wymeersch, Sinan Gezici

AI总结:

本文针对RIS像素故障下的LoS阻塞定位问题,提出两种联合定位与故障诊断算法,利用稀疏正则化或逐次假设检验检测故障并高精度定位UE,仿真验证其显著优于忽略故障的基准方法。

摘要:

可重构智能表面(RIS)作为超越5G无线网络的关键技术推动者之一,具有巨大潜力,可在视距(LoS)阻塞条件下提升定位与通信性能。然而,硬件缺陷可能导致RIS单元发生故障,这一问题被称为像素故障,尤其可能成为定位的主要障碍。本文研究了在LoS阻塞且存在RIS像素故障的情况下,RIS辅助定位用户设备(UE)的问题,并考虑了具有挑战性的单输入单输出(SISO)场景。我们首先通过错误指定的克拉美-罗下界(MCRB)分析探讨此类故障对精度的影响,该分析揭示了即使少量像素故障也会导致严重的性能损失。为解决此问题,我们开发了两种联合定位与故障诊断(JLFD)策略,以在同时高精度定位UE的同时检测故障像素。第一种策略利用故障稀疏性,基于l_1正则化。第二种策略通过每次迭代解决多重假设检验问题,逐一检测故障,逐步提升定位与诊断精度。仿真结果表明,所提出的JLFD算法相比传统的忽略故障的基准方法具有显著的性能提升,能够成功恢复由故障引起的性能退化。

英文摘要:

Reconfigurable intelligent surfaces (RISs) hold great potential as one of the key technological enablers for beyond-5G wireless networks, improving localization and communication performance under line-of-sight (LoS) blockage conditions. However, hardware imperfections might cause RIS elements to become faulty, a problem referred to as pixel failures, which can constitute a major showstopper especially for localization. In this paper, we investigate the problem of RIS-aided localization of a user equipment (UE) under LoS blockage in the presence of RIS pixel failures, considering the challenging single-input single-output (SISO) scenario. We first explore the impact of such failures on accuracy through misspecified Cramer-Rao bound (MCRB) analysis, which reveals severe performance loss with even a small percentage of pixel failures. To remedy this issue, we develop two strategies for joint localization and failure diagnosis (JLFD) to detect failing pixels while simultaneously locating the UE with high accuracy. The first strategy relies on l_1-regularization through exploitation of failure sparsity. The second strategy detects the failures one-by-one by solving a multiple hypothesis testing problem at each iteration, successively enhancing localization and diagnosis accuracy. Simulation results show significant performance improvements of the proposed JLFD algorithms over the conventional failure-agnostic benchmark, enabling successful recovery of failure-induced performance degradations.

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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