VLP-KAN: Low-Complexity and Interpretable RSS-Based VLP Using Kolmogorov-Arnold Networks

Received signal strength (RSS)-based Visible Light Positioning (VLP) has gained attention due to its relatively easy infrastructure deployment and high localization accuracy. Different low-complexity geometrical and machine learning (ML)-based models have been proposed for localization because of their robustness against the uncertainty produced by tilted devices, non-Lambertian sources, and the low dimensionality of RSS-vector. Inspired by the Kolmogorov-Arnold representation theorem, Kolmogorov-Arnold Networks (KANs) have been proposed as a promising alternative to Multi-Layer Perceptrons (MLPs). This paper evaluates the use and performance of KANs in VLP systems for the first time. Results show that in the proposed scenario, KANs achieve below cm-level accuracy. Moreover, symbolic regression (SR) can be implemented straightforwardly to find a function that relates RSS with distance. It is shown in this paper that both KANs and SR-KAN models outperform MLP and Weighted K-nearest neighbors based approaches.