A Data-Model Jointly Driven Framework for Visible Light Positioning Using Harmonic-Enhanced Graph Neural Networks

Abstract

Visible light positioning (VLP), due to its widespread infrastructure deployment and high accuracy, has emerged as a highly promising key technology for Internet of Things (IoT). Current research mainly relies on either data-driven methods based on fingerprint features or model-driven methods based on geometric localization to estimate position. Although data-driven approaches can effectively cope with complex environmental disturbances, their performance is limited by insufficient exploitation of latent signal features on the one hand and strong dependence on training data on the other, resulting in limited generalization capability. In contrast, model-driven methods can adapt to different scenarios by leveraging physical models and geometric constraints, but they struggle to characterize complex interference patterns in dynamic environments. To address these issues, this article proposes a data-model jointly driven VLP framework that integrates the complementary strengths of both paradigms. First, at the framework level, a tightly coupled joint optimization scheme is constructed to integrate data-driven ranging with model-driven localization, preserving physical interpretability while leveraging the representation capability of deep learning. In contrast to conventional methods that discard harmonics as detrimental components, this article introduces a harmonic-enhanced ranging module that uses selected harmonic components as auxiliary structured spectral cues to improve the robustness of VLP ranging. The fundamental received signal strength (RSS) and selected harmonic RSS values of the modulation signal are incorporated into the ranging process, and a graph neural network (GNN)-based data-driven ranging model is developed to capture the structured relationships between the fundamental component and its harmonics, thereby improving ranging robustness in complex environments. Finally, to enable end-to-end optimization of the joint framework, a geometry-aware loss function is designed, allowing the model to jointly consider data fitting and physical geometric constraints during training, thereby coupling learned signal representations with model-driven geometric constraints.