A Novel Frame Identification and Synchronization Technique for Smartphone Visible Light Communication Systems Based on Convolutional Neural Networks

TL;DR

This paper introduces a CNN-based method for frame identification and synchronization in smartphone VLC systems, achieving high accuracy and robustness in real-time scenarios with various image distortions.

Contribution

The paper presents a lightweight CNN model trained on a custom dataset to improve frame synchronization in S2C VLC systems under real-world conditions.

Findings

Achieves approximately 98.74% accuracy in frame identification and synchronization.

Effectively handles blurring, cropping, and rotation in real-time mobility scenarios.

Demonstrates robustness and efficiency of the CNN approach for VLC system synchronization.

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.