Utilizing Lighting Design Software for Simulation and Planning of Machine Learning Based Angle-of-Arrival (AOA) Visible Light Positioning (VLP) Systems
Hei Man Chan, Chi‐Wai Chow, Li-Sheng Hsu, Yang Liu, Ching-Wei Peng, Yin-He Jian, Chien-Hung Yeh
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.
Topics & Concepts
Computer scienceConvolutional neural networkSoftwareVisible light communicationArtificial neural networkAngle of arrivalScheme (mathematics)Artificial intelligenceReal-time computingSimulationEngineeringLight-emitting diodeTelecommunicationsAntenna (radio)Mathematical analysisElectrical engineeringMathematicsProgramming languageOptical Wireless Communication TechnologiesImpact of Light on Environment and HealthRemote Sensing and LiDAR Applications