NDLI: AI-Enabled Intelligent Visible Light Communications: Challenges, Progress, and Future

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AI-Enabled Intelligent Visible Light Communications: Challenges, Progress, and Future

Content Provider	MDPI
Author	Shi, Jianyang Niu, Wenqing Ha, Yinaer Xu, Zengyi Li, Ziwei Yu, Shaohua Chi, Nan
Copyright Year	2022
Description	Visible light communication (VLC) is a highly promising complement to conventional wireless communication for local-area networking in future 6G. However, the extra electro-optical and photoelectric conversions in VLC systems usually introduce exceeding complexity to communication channels, in particular severe nonlinearities. Artificial intelligence (AI) techniques are investigated to overcome the unique challenges in VLC, whereas considerable obstacles are found in practical VLC systems applied with intelligent learning approaches. In this paper, we present a comprehensive study of the intelligent physical and network layer technologies for AI-empowered intelligent VLC (IVLC). We first depict a full model of the visible light channel and discuss its main challenges. The advantages and disadvantages of machine learning in VLC are discussed and analyzed by simulation. We then present a detailed overview of advances in intelligent physical layers, including optimal coding, channel emulator, MIMO, channel equalization, and optimal decision. Finally, we envision the prospects of IVLC in both the intelligent physical and network layers. This article lays out a roadmap for developing machine learning-based intelligent visible light communication in 6G.
Starting Page	529
e-ISSN	23046732
DOI	10.3390/photonics9080529
Journal	Photonics
Issue Number	8
Volume Number	9
Language	English
Publisher	MDPI
Publisher Date	2022-07-29
Access Restriction	Open
Subject Keyword	Photonics Information and Library Science Visible Light Communication Artificial Intelligence Machine Learning Physical Layer Network Layer
Content Type	Text
Resource Type	Article

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