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 PPM schemes in VLC systems by using Decision Tree, Knearest neighbor (KNN), Support Vector Machine (SVM), and a Direct Decision-based Linear Model (LM) technique. A novel feature extraction model is derived to be able to classify the type of 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 KNN method gives the best accuracy performance against other schemes at the SNR of 25dB and the distance of 2.32m and more, while the Tree model is superior to KNN at the distances of 2.25m and 2.20m. Additionally, it has been obtained the best accuracy of 97.85% by the Decision Tree Model at the distance of 2.20m.

VLCMnet-Based Modulation Format Recognition for Indoor Visible Light Communication Systems

“…Initially, the modulation format recognition technique rapidly developed in the field of radio communications [5][6][7]. In recent years, numerous machine learning algorithms have also made strides in the field of MFR in optical communications [8][9][10][11][12][13]. While inputting time-series signals can capture the complete dynamic characteristics of signals over time, severe noise interference may cause modulation features to become blurred, thus increasing the difficulty of recognition.…”