A Novel Frame Identification and Synchronization Technique for Smartphone Visible Light Communication Systems Based on Convolutional Neural Networks
Journal:
arXiv
Published Date:
Jun 28, 2025
Abstract
This paper proposes a novel, robust, and lightweight supervised Convolutional
Neural Network (CNN)-based technique for frame identification and
synchronization, designed to enhance short-link communication performance in a
screen-to-camera (S2C) based visible light communication (VLC) system.
Developed using Python and the TensorFlow Keras framework, the proposed CNN
model was trained through three real-time experimental investigations conducted
in Jupyter Notebook. These experiments incorporated a dataset created from
scratch to address various real-time challenges in S2C communication, including
blurring, cropping, and rotated images in mobility scenarios. Overhead frames
were introduced for synchronization, which leads to enhanced system
performance. The experimental results demonstrate that the proposed model
achieves an overall accuracy of approximately 98.74%, highlighting its
effectiveness in identifying and synchronizing frames in S2C VLC systems.
