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

Abstract

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.