Utilizing Lighting Design Software for Simulation and Planning of Machine Learning Based Angle-of-Arrival (AOA) Visible Light Positioning (VLP) Systems

Abstract

We propose to utilize a commercially available DIALux lighting design software for simulation and planning of machine learning (ML) based angle-of-arrival (AOA) visible light positioning (VLP) systems. Here, different ML models, for example, second order linear regression (LR), artificial neural-network (ANN), and convolutional neural-network (CNN) are employed. The proposed VLP simulator works well with different ML algorithms. The results show that the proposed scheme can acts as an effective indoor VLP planning and design tool. Besides, it may also alleviate the training data collection in ML based VLP systems.