Design and Implementation of Optical Wireless Localization System Towards Dataset Generation

This paper introduces a novel approach for the development of system design and architecture for the data generation methods tailored for indoor localization within Optical Wireless Communication (OWC) systems. We explore key aspects of the Optical Wireless Localization (OWL) dataset generation method, namely hardware design and simulation-based high-precision localization. While physical architecture is well-established, dataset generation and hardware design methods require innovation. In this study, we propose an enhanced hardware design technology that combines collected indoor localization dataset generation with Deep neural network testing. This approach allows not only for the dataset generation of optical wireless localization in location-based OWC systems but also the high precision of positioning accuracy. The paper details the process, benefits, and outcomes of this innovative methodology.