Neural Network-Based Channel Estimation and Detection in Spatial Modulation VLC Systems

Abstract

We consider a spatial modulation aided indoor visible light communication system with user mobility and random receiver orientation. Two artificial neural networks (ANNs) are proposed which are able to predict the channel state information (CSI) with high accuracy and resolution. These architectures use estimated CSI at pilot instances obtained using least square or minimum mean square error estimation and predict CSI at intermediate locations. Moreover in ANN 2, predicted user position information is used to improve the performance. Numerical results show that the proposed ANNs deliver a better bit error rate compared to a benchmark spline interpolation-based method. Further, ANN 2 is shown to perform robustly in a high mobility scenario.