Vehicular Visible Light Positioning Using Receiver Diversity with Machine Learning

This paper proposes a 2-D vehicular visible light positioning (VLP) system using existing streetlights and diversity receivers. Due to the linear arrangement of streetlights, traditional positioning techniques based on triangulation or similar algorithms fail. Thus, in this work, we propose a spatial and angular diversity receiver with machine learning (ML) techniques for VLP. It is shown that a multi-layer neural network (NN) with the proposed receiver scheme outperforms other ML algorithms and can offer high accuracy with root mean square (RMS) error of 0.22 m and 0.14 m during the day and night time, respectively. Furthermore, the NN shows robustness in VLP across different weather conditions and road scenarios. The results show that only dense fog deteriorates the performance of the system due to reduced visibility across the road.

Moment-Generating Function Based Symbol Error Probability Comparison of M-ary Modulation Techniques in Fading Environment

This paper analyses and compare Symbol Error Probability (SEP) of M-ary modulation in different fading environment. Maximal ratio combining (MRC) technique with N branch receiver diversity is taken into consideration for the analysis. Expression for probability of symbol error for M-ary frequency shift keying/M-ary phase shift keying/M-ary differential phase shift key/and M-Level Quadrature Amplitude Modulation are obtained through moment-generating function (MGF) approach. Conventional integration process has probability of uncertainty and erroneousness because of composite mathematical calculations and infinite integration bounds. MGF method is used to avoid complex numerical computation for calculation of SEP. Comparison of SEP for diverse values of Rician factor “K”, Nakagami-M factor, modulation order “M” and diversity order “N” has been carried out.