GSSK⁃VLC系统中基于SVM的LED选择算法

SVM⁃Based LED Selection Algorithm in GSSK⁃VLC System

为了有效降低室内基于广义空移键控(Generalized Space Shift Keying,GSSK)调制技术的可见光通信(Visible Light Communications,VLC)系统LED选择算法的复杂度,提高系统的LED选择速度,提出了一种基于支持向量机(Support Vector Machine,SVM)的机器学习低复杂度高效率LED选择算法.通过将室内GSSK⁃VLC系统的LED选择等价建模为多分类的机器学习问题,利用核SVM构建LED选择的最优化问题,通过对偶理论,获得原问题的二次凸规划对偶问题,从而高效的获取SVM的最优分类参数.最后,通过学习训练获得的最优分类参数实现对任意给定用户信道信息的在线天线选择.通过计算机仿真和复杂度分析,与传统的LED选择算法相比,本文提出的算法能够在实现在线LED选择的同时保持低误码率(Bit Error Ratio,BER)性能,表明了该算法的有效性.

In order to reduce the complexity of LED selection algorithm in generalized space shift keying(GSSK)aided indoor visible light communication(VLC)system,a support vector machine(SVM)assisted low complexity and high efficiency machine learning LED selection algorithm is proposed for the considered GSSK⁃VLC system.By modeling the LED selection in indoor GSSK⁃VLC system as a multi⁃classification problem,an optimization problem is constructed by utilizing kernel SVM.After the optimal parameters of the learning system are obtained,the LED selection procedure can be accomplished efficiently for any given user’s channel state information.Simulation results and complexity analysis show that,compared with traditional LED selection algorithms,the proposed SVM aided LED selection algorithm can achieve an ideal bit error ratio(BER)performance while having considerably lower complexity.