RIS-aided Localization under Pixel Failures

TL;DR

This paper addresses the challenge of RIS pixel failures affecting localization accuracy by proposing joint localization and failure diagnosis algorithms that significantly improve performance in faulty RIS scenarios.

Contribution

The paper introduces two novel joint localization and failure diagnosis strategies for RIS-aided localization under pixel failures, enhancing robustness and accuracy.

Findings

Severe performance loss due to pixel failures shown by MCRB analysis.

Proposed JLFD algorithms outperform failure-agnostic benchmarks.

Successful recovery of localization accuracy despite pixel failures.

Abstract

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.