AI-Based Positioning with Input Parameter Optimization in Indoor VLC Environments

Abstract

Indoorlocation-based service (LBS) technology has been emerged as a major research topic in recent years. Positioning technology is essential for providing LBSs. The existing indoor positioning solutions generally use radio-frequency (RF)-based communication technologies such as Wi-Fi. However, RF-based communication technologies do not provide precise positioning owing to rapid changes in the received signal strength due to walls, obstacles, and people movement in indoor environments. Hence, this study adopts visible-light communication (VLC) for user positioning in an indoor environment. VLC is based on light-emitting diodes (LEDs) and its advantage includes high efficiency and long lifespan. In addition, this study uses a deep neural network (DNN) to improve the positioning accuracy and reduce the positioning processing time. The hyperparameters of the DNN model are optimized to improve the positioning performance. The trained DNN model is designed to yield the actual three-dimensional position of a user. The simulation results show that our optimized DNN model achieves a positioning error of 0.0898 m with a processing time of 0.5 ms, which means that the proposed method yields more precise positioning than the other methods.