基于MIMO方案的光学摄像机通信系统的深度学习开发

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

摘要

物联网(IoT)、卫星通信和其他利用射频波形的通信系统经常使用现代无线通信技术。由于安装简单,无线通信比传统通信更有优势。其中最著名的是光学摄像机通信(OCC)技术,它具有几个优点,包括对人体健康没有负面影响,安全性好,运营费用低廉。在本文中,我们提供了一种多输入多输出调制方法,该方法使用深度学习(DL)来识别发光二极管并预测阈值,同时考虑了移动辅助和远程通信。我们提出的策略采用DL算法,通过降低误码率和通信距离来提高传统摄像机开关键控(C-OOK)方案的性能。,以及数据速率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development of MIMO Scheme-based Optical Camera Communication System using Deep Learning method
The Internet of Things (IoT), satellite communication, and other communication systems that utilize radio frequency waveforms frequently use modern wireless communication technologies. Wireless communication provides benefits over conventional communication due to its simple installation. One of the most well-known of them is Optical Camera Communication (OCC) technology, which has several advantages, including no negative effects on human health, good security, and inexpensive operating expenses. In this paper, we offer a multiple-input multiple-output modulation method that uses deep learning (DL) to recognize light-emitting diodes and predict thresholds while considering mobility assistance and long-distance communication into considerations. Our suggested strategy employs DL algorithms to enhance the performance of the traditional camera on-off keying (C-OOK) scheme by reducing bit error rate, communication distance., and data rate.
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