  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.
