基于SVM的广义空移键控可见光通信系统信号检测算法

商建东; 孙浩博; 王法松

doi:10.11999/JEIT200711

基于SVM的广义空移键控可见光通信系统信号检测算法

doi: 10.11999/JEIT200711

1.
郑州大学河南省超算中心郑州 450001
2.
郑州大学信息工程学院郑州 450001

基金项目: 国家自然科学基金(61401401)，河南省科技攻关项目(192102210088)

详细信息

作者简介:
商建东：男，1968年生，教授，博士生导师，研究方向为高性能计算、计算机网络与通信

孙浩博：男，1995年生，硕士生，研究方向为可见光通信

王法松：男，1979年生，教授，硕士生导师，研究方向为盲信号处理、可见光通信

通讯作者:
王法松　iefswang@zzu.edu.cn

中图分类号: TN929.12
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- 被引次数: 0
出版历程
- 收稿日期: 2020-08-11
- 修回日期: 2021-04-15
- 网络出版日期: 2021-07-14
- 刊出日期: 2021-10-18

SVM-aided Signal Detection in Generalized Space Shift Keying Visible Light Communication System

1.
Henan Supercomputing Center, Zhengzhou University, Zhengzhou 450001, China
2.
School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China

Funds: The National Natural Science Foundation of China (61401401), The Science and Technology Research Project of Henan Province (192102210088)

摘要

摘要: 针对室内广义空移键控(GSSK)调制的可见光通信(VLC)系统，该文提出一种基于支持向量机(SVM)的机器学习信号检测算法。在一般的VLC系统中，极大似然(ML)检测是最优检测算法，但是ML检测算法具有很高的计算复杂度。为了解决此问题，该文利用机器学习中的SVM分类思想实现对系统接收端的信号检测，以在保证信号检测正确率的情况下，降低计算复杂度，提高GSSK-VLC系统的信号检测效率。仿真结果表明，该文提出的针对室内GSSK-VLC系统的SVM信号检测算法与ML检测算法相比，在具有接近ML的误比特率(BER)性能的同时，计算复杂度明显降低，有效提升了系统的检测性能。
- 可见光通信 /
- 信号检测 /
- 支持向量机 /
- 空移键控 /
- 广义空移键控
Abstract: Novel signal detection technique is conceived for Generalized Space Shift Keying (GSSK) modulated indoor Visible Light Communication (VLC) system, which is aided by one of popular machine learning approach termed as Support Vector Machine (SVM). For general classic VLC system, as the optimal detection algorithm, Maximum Likelihood (ML) detection has a high computational complexity. In order to alleviate this problem, classification idea in SVM is utilized to realize signal detection at the user’s receiving end by a particular trained learning model. As a result, a signal detection algorithm for the considered GSSK-VLC system based on SVM is designed with lower computational complexity and nearly optimal detection accuracy. Simulation results demonstrate that the proposed SVM-aided signal detection technique can have near optima ML Bit Error Rate (BER) performance while the computational complexity is significantly reduced in the considered indoor GSSK-VLC system
- Visible Light Communication (VLC) /
- Signal detection /
- Support Vector Machine (SVM) /
- Space Shift Keying (SSK) /
- Generalized Space Shift Keying (GSSK)

HTML全文

图 1 室内VLC系统概念图

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图 2 GSSK-VLC系统框图

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图 3 LoS传输的几何模型示意图

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图 4 针对SSK-VLC系统不同算法的BER性能分析

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图 5 针对GSSK-VLC系统的不同算法的BER性能分析

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表 1 GSSK-VLC系统标签

$n$	${l_n}$	${i_n}$	LED index
1	1	000	LED 1 & 2
2	2	001	LED 1 & 3
3	3	010	LED 1 & 4
4	4	011	LED 1 & 5
5	5	100	LED 2 & 3
6	6	101	LED 2 & 4
7	7	110	LED 2 & 5
8	8	111	LED 3 & 4

下载: 导出CSV

表 2 LED位置的空间分布坐标

4 LEDs		8 LEDs
LED	$({O_X},{O_Y},{O_Z})\;{ \rm{m} }$	1	(1.25, 0.35, 3)
1	(1.25, 1.25, 3)	2	(3.85, 0.35, 3)
2	(3.25, 1.25, 3)	3	(1.25, 1.85, 3)
3	(1.35, 3.25, 3)	4	(3.85, 1.85, 3)
4	(3.25, 3.25, 3)	5	(1.25, 3.25, 3)
		6	(3.85, 3.25, 3)
		7	(1.25, 4.25, 3)
		8	(3.85, 4.25, 3)

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表 3 系统仿真参数设置

发射端 (LED)		接收端(PD)
LED数量$({N_{\rm{t} } })$	4, 8	PD数量$({N_r})$	4
距地面高度	3 m	距地面高度	0.85 m
半功率半角$({\phi _{1/2} })$	${60^ \circ }$	PD的物理面积$({A_{\rm{PD} }})$	$1 \;{{\rm{cm}} ^2}$
光学滤波器增益$({{T}_s}(\psi ))$	1.0	PD的FoV半角$({\varPsi _{\rm{FoV}}})$	${60^ \circ }$
光电转换效率$(\eta )$	813.6 μW/mA	折光率$(\beta )$	$1.5$
调制指数$(\alpha )$	$0.1$	PD响应度$(R)$	100 μA/mW

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表 4 针对GSSK-VLC系统基于不同检测算法所需时间对比

检测算法	计算时间(s)
KMC	175.8836
KMC (20)	3517.6607
KMC (50)	8794.1735
IKMC	879.418
SVM	98.6687
ML	10904.7794

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