A Hybrid VLC/RF indoor localization with supervised learning

Abstract

Indoor localization has become a key technology for a wide range of applications, from smart buildings to asset tracking, but conventional RF-based systems are limited by multipath fading and interference between signals. In this paper, a hybrid Visible Light Communication (VLC) and Radio Frequency (RF) localization system is proposed, using supervised learning to improve its performance against these limitations. By integrating the high precision of VLC and the extensive coverage of RF, our system achieves stable and accurate indoor positioning. Machine learning methods—Support Vector Regression (SVR), Random Forest Regression (RFR), and Gaussian Process Regression (GPR)on hybrid VLC/RF signal features for inferring device locations. Experimental results demonstrate that the hybrid system reduces root mean square error (RMSE) by 27% compared to RF-only and 4% compared to VLC-only approaches, with Random Forest Regression (RFR) emerging as the top-performing model. The system achieves sub-0.5m accuracy for 90% of test points, validated in a controlled 5m×5m×3m indoor environment. This work demonstrates the potential of hybrid VLC/RF systems, augmented by supervised learning, to revolutionize indoor localization for IoT and smart infrastructure use cases.