Challenges and Prospects of Machine Learning in Visible Light Communication

被引：0

作者：

Chi N. ^{[1
]}

Jia J. ^{[1
]}

Hu F. ^{[1
]}

Zhao Y. ^{[1
]}

Zou P. ^{[1
]}

机构：

[1] Key Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shang-hai

来源：

| 1600年 / Posts and Telecom Press Co Ltd卷 / 05期

基金：

中国国家自然科学基金;

关键词：

artificial intelligence; deep learning; machine learn-ing; neural network; visual light communication;

D O I：

10.23919/JCIN.2020.9200893

中图分类号：

学科分类号：

摘要：

Visible light communication (VLC) is a promising research field in modern wireless communica-tion. VLC has its irreplaceable strength including rich spectrum resources, no electromagnetic disturbance, and high-security guarantee. However, VLC systems suffer from the non-linear effects that exist in almost every part of the system. As a part of artificial intelligence, machine learning (ML) is showing its potential in non-linear mitigating for its natural ability to fit all kinds of transfer functions, which may dramatically push the research in VLC. This paper introduces the application of ML in VLC, describes five recent research of deep learning applications in VLC, and analyses the performance. © 2020, Posts and Telecom Press Co Ltd. All rights reserved.

引用

页码：302 / 309

页数：7

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