基于深度学习的多波长可见光通信信道估计

2022 4th West Asian Symposium on Optical and Millimeter-wave Wireless Communications (WASOWC) Pub Date : 2022-05-12 DOI:10.1109/wasowc54657.2022.9798444

Zhao Ma, Peiyu Jia, Dahai Han, Min Zhang, Zabih Ghassemlooy, Liqiang Wang

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引用次数: 0

摘要

提出了一种多波长可见光通信(VLC)系统的信道建模方法，并比较了长短期记忆(LSTM)、门控循环单元(GRU)和稀疏自编码器(SAEs)算法在信道建模中的作用。结果表明，SAEs算法拟合最佳，均方误差仅为3 ×10−6。此外，我们构建了RGB三色LED光源的多波长通道，并对其进行了建模。最后，在分析了多种因素对信道的影响后，得出短波长的信号源建模效果最好。

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Deep-Learning-Based Channel Estimation for Multi-wavelength Visible Light Communication System

This paper presents a method for channel modeling of multi-wavelength Visible Light Communication (VLC) system, and compares the effects of long short-term memory (LSTM), gated recurrent unit (GRU), sparse autoencoder-s (SAEs) algorithms in channel modeling. The results show that the SAEs algorithm fits the best with a mean square error of only 3 ×10−6. Besides, we construct the multi-wavelength channel of the RGB tricolor LED light source and model it. Finally, after analyzing the influence of many factors on the channel, it is concluded that the modeling effect of the short-wavelength signal source is the best.

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2022 4th West Asian Symposium on Optical and Millimeter-wave Wireless Communications (WASOWC)

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