The integration of Optical Intelligent Reflective Surfaces (OIRSs) into Visible Light Communication (VLC) systems is gaining momentum as a valid alternative to RF technologies, harnessing the existing lighting infrastructures and the vast unlicensed optical spectrum to enable higher spectral efficiency, improved resilience to Line-of-Sight (LoS) blockages, and enhanced positioning capabilities. This paper investigates the problem of localizing a low-cost Photo Detector (PD) in a VLC-based indoor environment consisting of only a single Light Emitting Diode (LED) as an active anchor, and multiple spatially distributed single-element OIRSs. We formulate the problem within an indirect, computationally efficient localization framework: first, the optimal Maximum Likelihood (ML) estimators of the LoS and Non-Line-of-Sight (NLoS) distances are derived, using a suitable OIRS activation strategy to prevent interferences. To overcome the grid-based optimization required by the ML NLoS estimator, we devise a novel algorithm based on an unstructured noise variance transformation, which admits a closed-form solution. The set of estimated LoS/NLoS distances are then used within a low-complexity localization algorithm combining an Iterative Weighted Least Squares (IWLS) procedure, whose weights are set according to the inverse of the Cramér-Rao Lower Bound (CRLB), with an adaptive beam steering strategy that allows the OIRSs network to dynamically align with the PD, without any prior knowledge of its position. Accordingly, we derive the CRLB for both LoS/NLoS distance estimation and PD position estimation. Simulation results demonstrate the effectiveness of our approach in terms of localization accuracy, robustness against OIRSs misalignment conditions, and low number of iterations required to attain the theoretical bounds.

翻译：光学智能反射面（OIRS）与可见光通信（VLC）系统的融合正日益成为射频技术的有效替代方案，其利用现有照明基础设施和广阔的免许可光学频谱，可实现更高的频谱效率、更强的视距（LoS）阻塞鲁棒性以及更优的定位能力。本文研究了在仅使用单个发光二极管（LED）作为主动锚点及多个空间分布的单元素OIRS构成的VLC室内环境中，对低成本光电探测器（PD）的定位问题。我们将该问题置于一种间接且计算高效的定位框架中：首先，通过采用适当的OIRS激活策略以避免干扰，推导出视距与非视距（NLoS）距离的最优最大似然（ML）估计器。为克服ML NLoS估计器所需的网格优化，我们设计了一种基于非结构化噪声方差变换的新算法，该算法具有闭式解。随后，将估计得到的视距/NLoS距离集合用于一种低复杂度定位算法，该算法结合了迭代加权最小二乘（IWLS）过程（其权重根据克拉美-罗下界（CRLB）的倒数设置）与自适应波束赋形策略，使得OIRS网络能够在无需先验位置知识的情况下动态对准PD。据此，我们推导了视距/NLoS距离估计及PD位置估计的CRLB。仿真结果表明，所提方法在定位精度、对OIRS失准条件的鲁棒性以及达到理论边界所需的低迭代次数方面均表现出显著有效性。