基于抗非线性SVM的几何整形可见光通信系统

Anti-nonlinear Support Vector Machine Based Geometrically Shaping Visible Light Communication System

非线性效应限制是高速可见光通信系统性能提升的一大瓶颈问题,为此提出将支持向量机(support vector machine, SVM)监督学习算法应用于几何整形可见光通信系统接收信号星座点的分类判决,将信号的同向分量和正交分量作为特征向量,建立最优分类界面,以降低非线性条件下星座点变形带来的符号误判,同时比较几种几何整形设计的性能.仿真分析和实验结果表明,SVM提升了非线性条件下系统的性能,在数据速率为1.2 Gbit/s的高速可见光通信传输中,圆-169几何整形16正交幅度调制(quadrature amplitude modulation,QAM)的符号误码性能最优.

Nonlinear effect has been becoming a major bottleneck in high speed visible light communication(VLC) system. In this paper, we propose a supervised learning algorithm, support vector machine(SVM) for improving constellation classification in geometrically shaping(GS) VLC system. By taking the in-phase and quadrature components of the signal as feature vectors, an optimal classification plane can be built, and the symbol error introduced by nonlinearity could be therefore reduced. The performances of several GS designs are conducted and compared. Simulation and experimental results show that SVM could significantly reduce the error rate, compared with conventional classification scheme based on Euclidean distance. Among all simulations with SVM, the system with the data rate of 1.2 Gbit/s, circle169 GS-16 QAM performs the lowest symbol error rate.