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

为了降低广义空间调制 (GSM) 辅助室内可见光通信 (VLC) 系统中发光二极管 (LED) 选择过程的复杂性，支持向量机 (SVM) 辅助低复杂度和高效的机器学习 LED为所考虑的 GSM-VLC 系统提出了选择算法。通过将室内 GSM-VLC 系统中的 LED 选择问题建模为多分类任务，利用核 SVM 构建优化问题。从训练阶段获得最佳参数后，可以通过 SVM 辅助学习系统针对任何给定用户的信道状态信息有效地完成 LED 选择过程。仿真结果和复杂度分析表明，与传统的 LED 选择算法相比，