Deep Learning for 2D-MIMO scheme based on Optical Camera Communication

Huy Nguyen, Van Linh Nguyen, Duc Hoang Tran, Y. Jang
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引用次数: 0

Abstract

Radio frequency (RF) waveforms are widely used in wireless technologies, such as mobile system, smart factory, and smart home systems. Due to its simplicity of installation, wireless communication is better than wire communication. However, the use of high frequencies for data transmission can be harmful to human health. To replace RF waveform for communication system, Optical Wireless Communication (OWC), which deploys visible light waveform for communication, was researched widely by researchers around the world. Visible Light Communication (VLC), Light Fidelity (LiFi), and Optical Camera Communication (OCC) are the three candidates for replacing RF system. In this research, we present an multiple-input multiple-output (MIMO) modulation system based on on-off keying (OOK) modulation in the time domain and a light-emitting diode (LED) array. By adjusting the exposure time, focal length, forward error correction, and Deep learning for object detection, it is still possible to implement this technique with 10 links in various locations at a distance of 20 m with a velocity of 2 m/s.
基于光学相机通信的2D-MIMO方案的深度学习
射频(RF)波形广泛应用于移动系统、智能工厂和智能家居系统等无线技术中。由于安装简单,无线通信比有线通信好。然而,使用高频率进行数据传输可能对人体健康有害。为了取代射频波形用于通信系统,利用可见光波形进行通信的光无线通信(OWC)得到了国内外研究者的广泛研究。可见光通信(VLC)、光保真度(LiFi)和光学相机通信(OCC)是替代射频系统的三个候选方案。在这项研究中,我们提出了一种基于时域开关键控(OOK)调制和发光二极管(LED)阵列的多输入多输出(MIMO)调制系统。通过调整曝光时间、焦距、前向纠错和物体检测的深度学习,仍然可以在距离20米的不同位置以2米/秒的速度实现10个链接。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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