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Support vector machine based machine learning method for GS 8QAM constellation classification in seamless integrated fiber and visible light communication system
Science China Information Sciences ( IF 8.8 ) Pub Date : 2020-09-21 , DOI: 10.1007/s11432-019-2850-3
Wenqing Niu , Yinaer Ha , Nan Chi

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 8QAM (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 dBm input optical power under 7% forward error correction (FEC) threshold can be realized employing Circular (7, 1) geometrically-shaped 8QAM and SVM. At overall capacity of 960 Mbps, Q-factor increases by up to 11.5 dB.



中文翻译:

无缝集成光纤与可见光通信系统中基于支持向量机的GS 8QAM星座分类的机器学习方法

光纤上的可见光通信(VLC)网络已成为超高速室内无线通信的潜在候选者。为了减轻光纤和VLC信道中积累的信号失真,我们提出在两种几何形状的8QAM(正交幅度调制)无缝集成光纤和VLC系统中利用支持向量机(SVM)进行星座分类。我们介绍了4个子带来模拟多用户。实验结果表明,使用圆形(7,1)几何形状的8QAM和SVM,可以显着改善系统性能,并在7%前向纠错(FEC)阈值下在-2.5 dBm输入光功率下实现传输。在960 Mbps的总容量下,Q因子最多增加11.5 dB。

更新日期:2020-10-02
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