Applications of Machine Learning in Visible Light Communication

Zengyi Xu, Tianqu Chen, Guojin Qin, N. Chi
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引用次数: 1

Abstract

Visible light communication (VLC) is predicted to become an indispensable part of 6G network as it has a rich spectrum resource and other desired characteristics in communication such as high security and low electromagnetic interference. However, the direct modulation/demodulation in VLC introduces the non-linearity effect that limits the performance in communication. To tackle this problem, recent researches apply the emerging machine learning technique (ML) and neural network (NN) to enhance the system performance under high non-linearity. This article introduces four applications of machine learning in VLC system, and discusses their effectiveness.
机器学习在可见光通信中的应用
可见光通信(VLC)由于具有丰富的频谱资源和高安全性、低电磁干扰等通信所需的其他特性,预计将成为6G网络不可或缺的一部分。然而,VLC中的直接调制/解调引入了非线性效应,限制了通信性能。为了解决这一问题,最近的研究应用了新兴的机器学习技术(ML)和神经网络(NN)来提高系统在高非线性下的性能。本文介绍了机器学习在VLC系统中的四种应用,并讨论了它们的有效性。
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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