Gaussian kernel-aided deep neural network equalizer utilized in underwater PAM8 visible light communication system

被引：134

作者：

Chi, Nan ^{[1
]}

Zhao, Yiheng ^{[1
]}

Shi, Meng ^{[1
]}

Zou, Peng ^{[1
]}

Lu, Xingyu ^{[1
]}

机构：

[1] Fudan Univ, Key Lab Informat Sci Electromagnet Waves MoE, Shanghai Inst Adv Commun & Data Sci, Shanghai 200433, Peoples R China

来源：

OPTICS EXPRESS | 2018年 / 26卷 / 20期

基金：

中国国家自然科学基金;

关键词：

MODULATION;

D O I：

10.1364/OE.26.026700

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

In this paper, we demonstrate a novel Gaussian kernel-aided deep neural network (GK-DNN) equalizer that can effectively compensate for the high nonlinear distortion of underwater PAM8 visible light communication (VLC) channels. The application of a Gaussian kernel can reduce the necessary training iterations to 47.06%, enabling it to outperform the traditional DNN equalizer. At the same time, a novel design strategy with respect to the structure of the GK-DNN equalizer is proposed, which can effectively save computing resources and reduce the data volume of the necessary training data set. By using the GK-DNN equalizer. a 1.5 Gbps PAM8 VLC system over 1.2-m underwater transmission is successfully demonstrated. (C) 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

引用

页码：26700 / 26712

页数：13

共 21 条

[1]

[Anonymous], 2015, 2015 IEEECIC INT C C

[2] LIGHT-EMITTING DIODES [J].