VLC Indoor Positioning Using RFR and SVM Reduced Features Machine Learning Techniques

Abstract

Artificial intelligence algorithms require large datasets for better performance for all kinds of tasks such as classification and regression. In this paper, we explore the potential of the Random Forest Regression (RFR) algorithm and Support Vector Machine (SVM) algorithm with minimum features, such as signal power and its variants, for Visible Light Communication (VLC) based indoor positioning. We explore the performance of the RFR algorithm and SVM by using variations of the received signal power to increase the accuracy and reduce the computation complexity. The simulation results demonstrate that both techniques have estimated the location with high accuracy, however, RFR outperforms SVM in terms of mean error.