Fig. 1. Block diagram of machine learning based VLC system

Fig. 2. Application of machine learning in visible light communication

Fig. 3. Diagram of post equalization of K-means algorithm, in which black points are receiving constellation points after post-equalization, and I and Q represent in-phase component and orthogonal component of receiving data, respectively. (a) Receiving constellation and normal decision board, in which points in black circle will be misjudged; (b) CAPD decision board

Fig. 4. Flowchart of K-means algorithm

Fig. 5. Principle diagram of K-means algorithm based pre-equalization

Fig. 6. Diagrams of PAM4 signal fluctuation and DBSCAN re-classification^[33]. (a) Fluctuation of PAM4 receiving signal; (b) diagram of DBSCAN reclassification

Fig. 7. Description of core points, accessory points, and noise points of DBSCAN

Fig. 8. Description of SVM classification

Fig. 9. Effects of SVM classification and phase correction. (a) Receiving constellation before phase correction; (b) effect of SVM classification, in which Four colors at the bottom represent the four categories of the four constellation points according to the QPSK data, and Red, green, blue colors represent the input training set; (c) effect after phase correction

Fig. 10. Neural network structure of GK-DNN

Fig. 11. Structure of ANN

Table 1. Summarization of machine learning algorithms