Yu Ke, He Jing, Huang Zheng
Appl Opt. 2020 Aug 10;59(23):7109-7113. doi: 10.1364/AO.395717.
A decoding scheme based on a convolution neural network (CNN) is proposed and experimentally demonstrated in mobile optical camera communication (OCC). The CNN can be used to extract features between bright and dark stripes in images effectively. Thus, it can alleviate the stripe distortion in the mobile environment and reduce bit error rates (BERs) by using the proposed decoding scheme based on CNN. A controllable lateral and vertical mobile platform is built to simulate the mobile scenarios with different moving speeds (40-80 cm/s). The experimental results show that, at the moving speed of 80 cm/s, the proposed scheme based on CNN can achieve the BERs of 3.8×10 at the lateral case and 1×10 at the vertical case in a mobile OCC system.
提出了一种基于卷积神经网络（CNN）的解码方案，并在移动光相机通信（OCC）中进行了实验验证。CNN可有效用于提取图像中明暗条纹之间的特征。因此，通过使用所提出的基于CNN的解码方案，它可以减轻移动环境中的条纹失真并降低误码率（BER）。构建了一个可控的横向和纵向移动平台，以模拟不同移动速度（40 - 80厘米/秒）的移动场景。实验结果表明，在80厘米/秒的移动速度下，所提出的基于CNN的方案在移动OCC系统的横向情况下可实现3.8×10的误码率，在纵向情况下可实现1×10的误码率。
