Support vector machine based machine learning method for GS 8QAM constellation classification in seamless integrated fiber and visible light communication system

作     者：Wenqing NIU Yinaer HA Nan CHI Wenqing NIU;Yinaer HA;Nan CHI

作者机构：Key Laboratory for Information Science of Electromagnetic Waves (MoE)Fudan University 

出 版 物：《Science China(Information Sciences)》 (中国科学:信息科学(英文版))

年 卷 期：2020年第63卷第10期

页      面：182-193页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 081104[工学-模式识别与智能系统] 0803[工学-光学工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by National Key Research and Development Program of China (Grant No. 2017YFB0403603) National Natural Science Foundation of China (Grant No. 61571133) 

主　　题：visible light communication VLC support vector machine SVM geometrically shaping GS VLC network constellation classification 

摘      要：Visible light communication(VLC) network over optical fiber has become a potential candidate in ultra-high speed indoor wireless communication. To mitigate signal distortion accumulated in optical fiber and VLC channel, we present to utilize support vector machine(SVM) for constellation classification in two kinds of geometrically-shaped 8 QAM(quadrature amplitude modulation) seamless integrated fiber and VLC system. We introduce 4 sub-bands to simulate multi-user. Experimental results show that system performance can be significantly improved, and transmission at-2.5 dB m input optical power under 7%forward error correction(FEC) threshold can be realized employing Circular(7, 1) geometrically-shaped8 QAM and SVM. At overall capacity of 960 Mbps, Q-factor increases by up to 11.5 dB.