Neural Network Equalizer in Visible Light Communication: State of the Art and Future Trends

Abstract

As 6G research progresses, both visible light communication (VLC) and artificial intelligence (AI) become important components, which makes them appear to converge. Neural networks (NN) as equalizers are gradually occupying an increasingly important position in the research of the physical layer of VLC, especially in nonlinear compensation. In this paper, we will propose three categories of neural network equalizers, including input data reconfiguration NN, network reconfiguration NN and loss function reconfiguration NN. We give the definitions of these three neural networks and their applications in VLC systems. This work allows the reader to have a clearer understanding and future trends of neural networks in visible light communication, especially in terms of equalizers.