Deep Hybrid Neural Network-Based Channel Equalization in Visible Light Communication

In this letter, the channel impairments compensation of visible light communication is formulated as a time sequence with memory prediction. Then we propose efficient nonlinear post equalization, using a combined long-short term memory (LSTM) and deep neural network (DNN), to learn the complicated channel characteristics and recover the original transmitted signal. We leverage the long-term memory parameters of LSTM to represent the sequence causality within the memory channel and refine the results by DNN to improve the reconstruction accuracy. Results demonstrate that the proposed scheme can robustly address the overall channel impairments and accurately recover the original transmitted signal with fairly fast convergence speed. Besides, it can achieve better balance between performance and complexity than that of the conventional competitive approaches, which demonstrates the potential and validity of the proposed methodology for channel equalization.

I. Introduction

Visible light communication (VLC) based on light-emitting diode (LED) has gained significant attention for indoor short-range wireless communication [1]. The spectral efficiency of VLC has been increased with the help of high-order modulation, however, it is distorted by the channel impairments, which are mainly derived from the inherent nonlinearity of the LED and the inevitable inter-symbol interference (ISI) from the multipath optical transmission [2].