Deep-Learning-Based Channel Estimation for Multi-wavelength Visible Light Communication System

Abstract

This paper presents a method for channel modeling of multi-wavelength Visible Light Communication (VLC) system, and compares the effects of long short-term memory (LSTM), gated recurrent unit (GRU), sparse autoencoder-s (SAEs) algorithms in channel modeling. The results show that the SAEs algorithm fits the best with a mean square error of only 3 ×10−6. Besides, we construct the multi-wavelength channel of the RGB tricolor LED light source and model it. Finally, after analyzing the influence of many factors on the channel, it is concluded that the modeling effect of the short-wavelength signal source is the best.