Visible Light Positioning with Intelligent Reflecting Surfaces under Mismatched Orientations

TL;DR

This paper investigates the impact of orientation mismatches of intelligent reflecting surfaces on visible light positioning accuracy, deriving bounds and estimators to quantify performance degradation in non-line-of-sight scenarios.

Contribution

It introduces the MCRB and MML estimators for IRS orientation mismatches and compares their performance with conventional methods, highlighting the effects on localization accuracy.

Findings

Orientation mismatches significantly degrade localization accuracy at high SNR.

Derived the MCRB and MML estimators for mismatched IRS orientations.

Compared performance with conventional estimators to quantify mismatch effects.

Abstract

Accurate localization can be performed in visible light systems in non-line-of-sight (NLOS) scenarios by utilizing intelligent reflecting surfaces (IRSs), which are commonly in the form of mirror arrays with adjustable orientations. When signals transmitted from light emitting diodes (LEDs) are reflected from IRSs and collected by a receiver, the position of the receiver can be estimated based on power measurements by utilizing the known parameters of the LEDs and IRSs. Since the orientation vectors of IRS elements (mirrors) cannot be adjusted perfectly in practice, it is important to evaluate the effects of mismatches between desired and true orientations of IRS elements. In this study, we derive the misspecified Cramer-Rao lower bound (MCRB) and the mismatched maximum likelihood (MML) estimator for specifying the estimation performance and the lower bound in the presence of mismatches in IRS orientations. We also provide comparisons with the conventional maximum likelihood (ML) estimator and the CRB in absence of orientation mismatches for quantifying the effects of mismatches. It is shown that orientation mismatches can result in significant degradation in localization accuracy at high signal-to-noise ratios.