The modulation classification methods in PPM-VLC systems

Abstract

Intelligent methods have been applied to many fields for a long time. Recently, Visible Light Communication (VLC) systems widely include learning and classification models to improve their performances. The classification of L-Pulse Position Modulation (L-PPM) formats is crucial for VLC systems since the modulation order L is very effective for providing energy efficiency and increasing the transmission capacity. In this paper, therefore, it is reported for the first time, the classification of L-PPM schemes in VLC systems by using Decision Tree, K-nearest neighbor (KNN), Support Vector Machine, and a Direct Decision-based Linear Model technique. A novel feature extraction model is derived to be able to classify the type of L-PPM modulation schemes. A comparison has been given to observe the performance of classification schemes by taking into account the level of Signal to Noise Ratio and the transmission distance between receiver and transmitter. The 2-PPM scheme has the highest success rate against other schemes in terms of performance metrics at the overall distance. Additionally, the KNN method gives the best accuracy performance at the distance of 2.32 m and more, while it has been obtained the best accuracy of 97.85% by the Decision Tree Model at the distance of 2.20 m.