用CNN提升手机可见光通信的帧识别与同步精度
A Novel Frame Identification and Synchronization Technique for Smartphone Visible Light Communication Systems Based on Convolutional Neural Networks
- 基于CNN设计轻量级模型，自动识别与同步帧
- 实测准确率达98.74%，抗模糊、旋转等干扰
- 适合移动场景下的短距可见光通信系统
本文提出一种新型、鲁棒且轻量的监督式卷积神经网络（CNN）方法，用于屏幕到相机（S2C）可见光通信（VLC）系统中的帧识别与同步，以提升短距离通信性能。该模型基于Python和TensorFlow Keras框架开发，在Jupyter Notebook中通过三次实时实验训练完成。实验数据集由研究者从零构建，涵盖移动场景中常见的图像模糊、裁剪与旋转等问题。引入开销帧实现同步，显著提升系统表现。实验结果表明，该模型整体准确率约为98.74%，在复杂环境下仍能有效识别并同步帧，验证了其在S2C VLC系统中的有效性。
原文摘要 · Abstract (English)
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.
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